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This Premium Career Track - Chief Technology Officer (CTO) program by Uplatz includes the following courses:

1) Cloud Computing Basics

2) Microsoft Azure Fundamentals

3) Business Intelligence and Data Analytics

4) Tableau (comprehensive)

5) Power BI

6) Python Programming (basic to advanced)

7) R Programming (basic to advanced)

8) Data Science with Python

9) Data Science with R

10) Machine Learning (basic to advanced)

11) Machine Learning with Python

12) Deep Learning Foundation

13) CISSP (Cybersecurity)

14) Introduction to DevOps

15) Angular 8

16) Node.js

17) ReactJS

18) SQL Programming with MySQL

19) Unix and Shell Scripting

20) Google Analytics

21) Microsoft Excel

22) Google Sheets

23) Project Management Fundamentals

24) Microsoft Project (basic to advanced)

25) Leadership and Management

A Chief Technology Officer (CTO) is a high-level executive in an organization responsible for overseeing the technology strategy, implementation, and management. The CTO plays a critical role in aligning technology initiatives with the overall business goals of the organization.

While the specific responsibilities of a CTO can vary depending on the size and nature of the company, their key responsibilities generally include: Technology Strategy, Innovation and Research, Technology Infrastructure, Cybersecurity and Data Privacy, Team Leadership and Development, Vendor Management, Budgeting and Resource Allocation, Technology Governance, Business Continuity and Disaster Recovery, Collaboration with Other Executives, Technology Partnerships and Alliances, Intellectual Property Management, Technology Roadmap, Data Analytics and Business Intelligence, IT Compliance and Risk Management, Technology Scalability, and Stakeholder Management.

The CTO is a strategic leader who plays a crucial role in driving technology initiatives that support the organization's growth, innovation, and success. They must combine technical expertise with business acumen and leadership skills to make informed decisions that align technology with the organization's overall vision and objectives.

Course/Topic 1 - Cloud Computing Basics - all lectures

In this lecture session we learn about cloud computing, which means storing and accessing data over the internet instead of a hard disk. It is defined as a service that provides users to work over the internet.
38:26
In this lecture session we learn about cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale.
30:33
In this lecture session we learn about Cloud computing is a general term for anything that involves delivering hosted services over the internet. These services are divided into three main categories or types of cloud computing.
33:14
In this tutorial we learn about the importance of cloud computing can be found in using services like Google Drive and Apple iCloud. The use of these services allows documents, contacts, pictures, and a whole lot more online.
32:19

Course/Topic 2 - Microsoft Azure Fundamentals - all lectures

In this lecture session we learn about microsoft azure course introduction and also talk about overview of microsoft azure.
21:37
In this lecture session we learn about azure basics functions and importance of microsoft azure.
20:36
In this lecture session we learn about microsoft azure features and also talk about functions of microsoft azure in brief.
29:49
In this lecture session we learn about microsoft azure glossary and also talk about features of azure glossary in microsoft azure fundamentals.
22:08
In this lecture session we learn about Microsoft azure certification path in microsoft azure fundamentals and also talk about features of certification path.
28:48
In this lecture session we learn about azure certification path and also talk about features of microsoft azure in brief.
33:38
In this lecture session we learn about the fundamentals of cloud computing and also talk about the features of cloud.
38:26
In this lecture session we learn about cloud computing functions and also talk about the importance of cloud computing in brief.
30:33
In this lecture session we learn about cloud computing terms and conditions and also talk about features of terms and computing of cloud computing.
33:14
In this lecture session we learn about amazon web service and azure and also talk about the basic difference between amazon web service and microsoft azure.
32:15
In this lecture session we learn about we learn about Getting started with microsoft azure and also talk about features and basic overview of Azure.
17:04
In this lecture session we learn about the advantages of Azure and also talk about the importance of Microsoft azure.
20:51
In this lecture session we learn about the basic advantage of Azure in microsoft fundamentals.
20:23
In this lecture session we learn about features of microsoft azure and also talk about functions of microsoft azure.
28:20
In this lecture session we learn about the advantages of microsoft azure and also talk about factors of microsoft azure.
28:00
In this lecture session we learn about career in microsoft azure and also career in microsoft azure in brief.
38:21
In this lecture session we learn about Microsoft Azure IOT and also talk about features of Azure IOT in brief.
25:04
In this lecture session we learn about fundamentals of azure components in microsoft components and also talk about features of azure components.
31:50
In this lecture session we learn about microsoft azure storage architecture and also talk about functions of azure storage architecture in microsoft azure.
1:03:57
In this lecture session we learn about microsoft azure table storage and also talk about features of azure table storage.
13:04
In this lecture session we learn about azure blob storage and also talk about the importance of microsoft azure blob storage.
14:15
In this lecture session we learn about the advantages of blob storage and also talk about all advantages of blob storage.
13:38
In this lecture session we learn about microsoft azure Queue storage in azure and also talk about features of azure Queue storage in microsoft azure.
17:05
In this lecture session we learn about azure virtual machines in microsoft azure and also talk about the importance of virtual machines.
16:25
In this lecture session we learn about azure mobile app service and also talk about how we develop mobile app services.
35:46
In this lecture session we learn about azure event grid and also talk about features of microsoft azure event grid.
27:10
In this lecture session we learn about microsoft azure backup service in azure cloud and also talk about the importance of microsoft azure backup services.
35:50
In this lecture session we learn about azure event hub and also talk about features of microsoft azure event hub.
31:09
In this lecture session we learn about azure quickstart templates in fundamentals of microsoft azure and also talk about the importance of azure quickstart template.
23:14
In this lecture session we learn about azure API app service in microsoft azure and also talk about functions of azure API app service.
35:33
In this lecture session we learn about azure cloud services and also talk about azure cloud services
39:30
In this lecture session we learn about azure SQL database configuration and also talk about SQL database features.
26:51
In this lecture session we learn about Microsoft Azure devOps and also talk about the importance of azure devOps.
27:29
In this lecture session we learn about azure express route and also talk about microsoft azure express route.
29:18
In this lecture session we learn about Microsoft Azure SQL database in microsoft azure and also talk about features of SQL.
19:59
In this lecture session we learn about microsoft azure key vault in microsoft azure and also talk about key vaults in azure.
25:55
In this lecture session we learn about microsoft azure load balancer in microsoft azure and also talk about azure load balancer.
27:41
In this lecture session we learn about azure CDN in microsoft azure fundamentals and also talk about azure CDN.
17:26
In this lecture session we learn about Microsoft Azure cosmos DB in cloud computing and also talk about features of cloud computing.
30:06
In this lecture session we learn about fundamentals of microsoft azure domain in azure and also talk about the importance of azure domains.
47:11
In this lecture session we learn about legacy application modernization and also talk about features of legacy application modernization.
18:04
In this lecture session we learn about microsoft azure portals and also talk about all portals of microsoft azure.
20:52
In this lecture session we learn about microsoft azure services portal and also talk about features of azure all services.
46:41
In this lecture session we learn about what is the best model in microsoft azure and also talk about best models for enterprise.
21:39
In this lecture session we learn about microsoft azure deep dive and also talk about features of microsoft azure deep dive.
1:02:04
In this lecture session we learn about azure management portal in microsoft azure management function and factors.
24:31
In this lecture session we learn about subscription and billing and also function importance subscription and billing.
44:32
In this lecture session we learn about azure storage services and also talk about functions of azure storage services.
30:38
In this lecture session we learn about creating and configuring azure storage accounts and also talk about functions of creating azure storage accounts.
48:18
In this lecture session we learn about microsoft azure storage account in fundamentals of azure portals.
31:02
In this lecture session we learn about Microsoft Azure Storage account and also talk about features of azure storage.
44:51
In this lecture session we learn about microsoft azure websites and services in azure and also talk about basics of websites and services.
54:27
In this lecture session we learn about factors of websites and services in microsoft azure.
37:40
In this lecture session we learn about validation warnings in microsoft azure.
12:52
In this lecture session we learn about how to develop azure websites and services and also talk about functions of websites and services.
40:01
In this lecture session we learn about what is services protections and DDos protections in microsoft azure.
30:31
In this lecture session we learn about network interface and also talk about functions of network interface.
34:12
In this lecture session we learn about microsoft azure Vnet connectivity and also talk about features of peeing and global peeing.
42:41
In this lecture session we learn about microsoft azure compute services and also talk about functions of features of compute services.
59:12
In this lecture session we learn about creating virtual machines and also talk about functions of creating virtual machines.
32:23
In this lecture session we learn about creating an availability set in Microsoft Azure and also talk about factors of creating an availability set.
19:46
In this lecture session we learn about scaling microsoft azure virtual machines and also talk about functions of scaling azure virtual machines.
26:48
In this lecture session we learn about azure app services and also talk about how we create microsoft azure app services.
59:34
In this lecture session we learn about creating a web application and deploying it and also talk about features of deploying.
31:13
In this lecture session we learn about features of creating a web application and deploying in microsoft azure.
32:36
In this lecture session we learn about microsoft azure mobile app services and also talk about features of mobile app services.
1:00:42
In this lecture session we learn about microsoft azure notification hub and mobile engagement.
27:11
In this lecture session we learn about microsoft azure functions apps and also talk about features of azure functions apps.
21:46
In this lecture session we learn about microsoft azure functions and also talk about functions of microsoft azure functions.
48:17
In this lecture session we learn about microsoft azure API app and API management and also talk about functions of API management in microsoft azure.
38:27
In this lecture session we learn about creating an API using azure portal and also talk about features creating an azure portal.
09:25
In this lecture session we learn about Azure API app services details and also talk about features of API app services.
40:47
In this lecture session we learn about microsoft azure database services and also talk about features of database services.
34:38
In this lecture session we learn about Microsoft Azure SQL database and also talk about features of SQL database.
14:48
In this lecture session we learn about Microsoft Azure SQL database configuration and also talk about features of SQL database.
29:53
In this lecture session we learn about global distribution and partitioning and also talk about features of global distributions.
23:31
In this lecture session we learn about Microsoft Azure SQL data warehouse and also talk about functions of azure SQL data warehouse.
39:16
In this lecture session we learn about cloud computing importance and also talk about functions of cloud computing importance.
32:19
In this lecture session we learn about microsoft azure certification path and also talk about features of azure certification path.
1:46:42

Course/Topic 3 - Business Intelligence and Data Analytics - all lectures

In this lecture session we discuss about Bi concepts, examples and application of business intelligence and data analytics and also cover other concepts of BI.
14:48
In this lecture session we learn about basic concepts of BI and also cover factors of business intelligence in brief.
19:22
In this lecture session we learn about data warehouse data access and data dashboarding and also cover presentation in BI.
24:10
In this lecture session we learn about product database, advertise database and customer demographic database and also cover data analyst concepts.
19:54
In this lecture session we learn about basic introduction of business intelligence and also cover factors of business intelligence in brief.
31:10
In this lecture session we learn about introduction of predictive modeling and also cover functions of predictive modeling in brief.
1:05:08
In this lecture session we learn about data related to customer services and also talk about customer relation databases in brief.
32:37
In this lecture session we learn about introduction of NoSQL and also cover basic functions of NoSQL in business intelligence.
33:46
In this lecture session we learn about graph stores and also talk about the advantages and disadvantages of graph stores in BI.
25:52
In this lecture session we learn about hierarchical clustering in business intelligence and also talk about clustering factors in BI.
29:58
In this lecture session we learn about introduction of salesforce in business intelligence and also talk about some basic uses of salesforce.
34:25
In this lecture session we learn about introduction to NLP and also cover what is natural language processing in artificial intelligence.
18:30
In this lecture session we learn about natural language processing speech to text conversion and also cover the importance of natural language processing.
25:22
In this lecture session we learn about introduction of apache server in business intelligence and also talk about basics of apache server.
44:24
In this lecture session we learn about deep drive into business intelligence and also talk about factors or deep drive in business intelligence.
30:54
In this lecture session we learn about data warehousing in business intelligence and data analytics and also talk about factors and features of data warehousing.
39:54
In this lecture session we learn about types of data in business intelligence and also talk about different types of data in BI.
25:07
In this lecture session we learn about mobile BI and also talk about open source BI software replacing vendor offering.
39:18
In this lecture session we learn about real time BI in business intelligence and also talk about factors of real time BI in brief.
1:35:17
In this lecture session we learn about data analytics comprehensively and also talk about functions of data analytics.
23:43
In this lecture session we talk about data analytics vs business analytics and also talk about the importance of data analytics.
41:04
In this lecture session we learn about Embedded analytics and also talk about functions of Embedded analytics in data analytics.
1:02:55
In this lecture session we learn about collection analytics and also cover the importance of collection analytics.
59:03
In this lecture session we learn about survival analytics and also cover duration analytics in brief.
29:35
In this lecture session we learn about machine learning techniques and also cover the importance and factors of machine learning techniques in business intelligence.
37:34
In this lecture session we learn about geospatial predictive analytics and also talk about functions of geospatial predictive analytics in business intelligence.
1:01:31
In this lecture session we learn about cohort analysis in data analyst and we also cover functions and importance of cohort analysis.
21:36
In this lecture session we learn about data mining in business intelligence and also talk about data mining functions and why we need data mining in business intelligence.
45:40
In these lecture sessions we learn about anomaly detection and also talk about functions of anomaly detection in brief.
1:00:36
In these lecture sessions we learn about statistically sound association and also talk about factors of statistically sound association in business intelligence.
31:29
In this lecture session we learn about cluster analysis. We’ll cover all types of cluster analysis in brief and also cover the importance of cluster analysis in business analysis.
36:21
In this lecture session we learn about DBSCAN in business intelligence and also talk about DBSCAN functions and importance.
59:58
In this lecture session we learn about regression models in business intelligence and also talk about the function of regression models.
31:57
In this lecture session we learn about extraction based summarization in business intelligence and also cover all types of summarization in data analyst.
10:57
In this lecture session we learn about machine learning in BI and also talk about factors and importance of machine learning in brief.
1:00:50
In this lecture session we learn about machine learning vs BI we also discuss the basic difference between machine learning and business intelligence.
1:15:37
In this lecture session we learn about how ml can make BI better and also talk about ml basic functions.
1:18:01
In this lecture session we learn about data warehousing and also talk about how we manage data warehousing in business intelligence.
18:28
In this lecture session we learn about data warehousing in business intelligence and data analytics and also talk about factors and features of data warehousing.
29:22
In this lecture session we learn about data mart in business intelligence and also talk about data mart function.
32:40
In this lecture session we learn about data dimensions in business intelligence and also cover all types of data dimension in BI.
30:31
In this lecture session we learn about data dimension in business intelligence and also cover functions and importance of data dimension.
24:02
In this lecture session we learn about data vault modeling in business intelligence and also cover different types of vault modeling in brief.
29:14
In this lecture session we learn about links and satellites and also cover the importance and factors of links and satellites in business intelligence.
28:59

Course/Topic 4 - Tableau (comprehensive) - all lectures

In this session you will learn about the Business intelligence (BI) which combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations to make more data-driven decisions
17:31
In this session we will introduce you about Tableau which is a widely used business intelligence (BI) and analytics software trusted by companies like Amazon, Experian, and Unilever to explore, visualize, and securely share data in the form of Workbooks and Dashboards. With its user-friendly drag-and-drop functionality it can be used by everyone to quickly clean, analyze, and visualize your team’s data.
33:45
This session is all about the history of Tableau which was founded by Pat Hanrahan, Christian Chabot, and Chris Stolte from Stanford University in 2003. The main idea behind its creation is to make the database industry interactive and comprehensive.
21:38
In this tutorial, we will discuss the Tableau interface and understand its functioning in detail. Followed by the general understanding of Tableau’s working. Along with this, we will learn the Components of Tableau Server.
34:10
In this session, you will get to know how to use Tableau Prep Builder to clean and prepare your data, start a new flow by connecting to your data, just like in Tableau Desktop. You can also open an existing flow and pick up where you left off.
43:54
In this video, once you have chosen the best Tableau product for you, it is time to start finding insights in your data! Much like Tableau’s suite of products, data connections come in many shapes and sizes. As of this writing, Tableau Desktop: Personal has four different types of data connections, and Tableau Desktop.
33:13
This session teaches you about the Data blending which is a method for combining data from multiple sources. Data blending brings in additional information from a secondary data source and displays it with data from the primary data source directly in the view.
30:50
If you are connected to a data source that has been modified, you can immediately update Tableau Desktop with the changes by selecting a data source on the Data menu and then selecting Refresh.
41:22
In this Tableau tutorial, we are going to study about what is sorting in Tableau. We will also discuss how to use Quick Sort in Tableau. At last, we will see why is my king broken and combined filed. Tableau sort is the process of arranging or ordering the data in Ascending Order or Descending Order.
19:38
In this video, we will show you How to perform sorting in Tableau reports with example. For this Tableau sort demo, we are going to use the report we created in our previous article.
13:43
In this tutorial, we will show you How to perform grouping in Tableau reports with example. For this Tableau Grouping demo, we are going to use the report we created in our previous article. Tableau Grouping is the process of merging or combining two or more values for further analysis.
12:18
In this video, we will show you How to perform grouping in Tableau reports with example? For this Tableau Grouping demo, we are going to use the report we created in our previous video.
24:55
In this video, we will show you how to create Tableau Set, Constant Sets, and Computed Sets. First, Drag and Drop the State Name from Dimension Region to Rows Shelf and Profit from Measures region to Columns Shelf.
26:50
In addition to a Set Action, you can also allow users to change the membership of a set by using a filter-like interface known as a Set Control, which makes it easy for you to designate inputs into calculations that drive interactive analysis. For details, see Show a set control in the video.
38:06
In this session you begin filtering data in Tableau, it's important to understand the order in which Tableau executes filters in your workbook. Filtering is an essential part of analyzing data. This article describes the many ways you can filter data from your view. It also describes how you can display interactive filters in the view, and format filters in the view.
26:07
In this video you will get to know about filtering which is an essential part of analyzing data. This article describes the many ways you can filter data from your view. It also describes how you can display interactive filters in the view, and format filters in the view.
20:49
In this tutorial, we will learn about another interesting and useful feature of Tableau that is Tableau parameters. Here, we will try and gain a good understanding of the parameters in Tableau and their use in Tableau. We will start by discussing the definition of parameters followed by learning how to create parameters and use them in Tableau.
31:36
In this session you will understand how to use parameter actions to let your audience change a parameter value through direct interaction with a viz, such as clicking or selecting a mark. You can use parameter actions with reference lines, calculations, filters, and SQL queries, and to customize how you display data in your visualizations.
24:43
In this Tableau tutorial, we will study What is Tableau Reference Lines, functions of Reference lines in Tableau and the steps involved in creating / Adding reference lines to the Tableau Bar Chart. At last, we will how to create reference lines in Tableau with example. So, let us start Tableau Reference Lines.
20:42
In the tutorial you will get to know how to show trend lines in a visualization to highlight trends in your data. You can publish a view that contains trend lines, and you add trend lines to a view as you edit it on the web. When you add trend lines to a view, you can specify how you want them to look and behave.
25:39
In this Tableau tutorial, you will learn about the story which is a sequence of visualizations that work together to convey information. You can create stories to tell a data narrative, provide context, demonstrate how decisions relate to outcomes, or to simply make a compelling case.
23:15
In this session you will understand how to Use stories to make your case more compelling by showing how facts are connected, and how decisions relate to outcomes. You can then publish your story to the web or present it to an audience. Each story point can be based on a different view or dashboard, or the entire story can be based on the same visualization seen at different stages, with different filters and annotations.
41:34
In this video, we will show you, How to Format Tableau Dashboard Layout with an example. For this, we are going to use the below-shown dashboard. Once you created your dashboard (added required Sheets), you can use the layout tab to format those Sheets or Items as per your requirements.
23:52
Tableau Layout Containers control the spacing between dashboard components. They allow you to format common elements and move multiple dashboard objects at the same time. 
36:38
In our last Tableau tutorial, we discuss How to Format Tableau Dashboard Layout. Here, in this tutorial, we are going to learn about How to Tableau Interactive Dashboard with Data Granularity, Interactivity, and Intuitiveness in Tableau. In other word or in general words we can call this playing with maps in a tableau. so, let us start with How to Create Tableau Interactive Dashboard.
29:40
This tutorial walks you through some of the most common tasks you might perform when creating maps in Tableau. You’ll learn how to connect to and join geographic data; format that data in Tableau; create location hierarchies; build and present a basic map view; and apply key mapping features along the way. If you're new to building maps in Tableau, this a great place to start.
07:55
This tutorial describes how to create and use calculated fields in Tableau using an example. You'll learn Tableau calculation concepts, as well as how to create and edit a calculated field. You will also learn how to work with the calculation editor, and use a calculated field in the view. If you're new to Tableau calculations or to creating calculated fields in Tableau, this is a good place to start.
21:08
You can build several different types of maps for your geographic analysis in Tableau. If you're new to maps, or simply want to take advantage of the built-in mapping capabilities that Tableau provides, you can create a simple point or filled (polygon) map.
13:07
You can always customize a table calculation by editing it in the Table Calculations dialog box, but there are other, more specialized ways to customize a table calculation.
14:15
This video introduces the basics of understanding calculations in Tableau. In this topic, you'll learn why and when to use calculations.
14:17
This session explains the types of LOD expressions you can use in Tableau, as well as when to use them, and how to format them. It also uses an example to demonstrate how to create a simple LOD expression. Level of Detail expressions (also known as LOD expressions) allow you to compute values at the data source level and the visualization level. However, LOD expressions give you even more control on the level of granularity you want to compute.
27:50
To edit a table calculation Right-click the measure in the view with the table calculation applied to it and select Edit Table Calculation. In the Table Calculation dialog box that appears, make your changes.
18:31
Tableau can create interactive visualizations customized for the target audience. In this tutorial, you will learn about the measures, chart types and its features.
15:18
When you save a level of detail expression, Tableau adds it to either the Dimensions or the Measures area in the Data pane. FIXED level of detail expressions can result in measures or dimensions, depending on the underlying field in the aggregate expression.
37:12
In this Tableau tutorial, we are going to learn about using a Histogram in Tableau. Here, we will find answers to questions like what is a histogram, and how do we create it in our Tableau software.
16:11
In this tutorial, 'Sample-Superstore.csv' is used for the demonstration. You can connect to the data source and follow the steps given in the tutorial. Tableau can create interactive visualizations customized for the target audience. In this tutorial, you will learn about the measures, chart types and its features.
25:01
In this Tableau Tutorial, we are going to learn about an interesting chart that is a bubble chart or packed bubble chart. Here, we will learn how to create a bubble chart in Tableau in a stepwise manner. You can create your first Tableau bubble chart with us on your own device. All you need, as of now is a sample data set and Tableau software in your device.
09:54
A histogram is a chart that displays the shape of a distribution. A histogram looks like a bar chart but groups values for a continuous measure into ranges, or bins.
11:51
Tableau Bubble Chart is used to display the data in circles. We can define each bubble using any of our Dimension members and size by Measure value.
09:54
In this tutorial we will learn about Tree maps which are the relatively simple data visualization that can provide insight in a visually attractive format. Use packed bubble charts to display data in a cluster of circles. Dimensions define the individual bubbles, and measures define the size and color of the individual circles.
10:42
In this Video we will get to know about the best practices which are key to developing informative visualizations that drive your audience to act. A dashboard is successful when people can easily use it to derive answers. Even a beautiful dashboard with an interesting data source could be rendered useless if your audience can’t use it to discover insights.
48:07

Course/Topic 5 - Power BI - all lectures

Learn how you can leverage Power BI to easily build reports and dashboards with interactive visualizations and see how other organizations have used this solution to drive business results with actionable insights.
20:51
In this session, with Power BI Desktop, you can build advanced queries, models, and reports that visualize data. You can also build data models, create reports, and share your work by publishing to the Power BI service.
01:57
This is the first part of Basic Dashboard in Power BI. In this video you will learn how to create a basic dashboard with simple data points.
23:35
In this Video, we will show you how can you install Power PI desktop in PC.
18:05
The third part in a series of Microsoft Power BI tutorials for beginners. This tutorial cover Filter’s pane and the Slicers.
30:14
In this Part 4, video shows the time slicer feature of Power BI Desktop. Also running some simple statistics using the matrix visualization.
30:09
In this Part 5 session you will learn about how to create a simple R script in Power BI desktop using the grid Extra package for displaying data and the dplyr package for data munging.
21:32
In this Microsoft Power BI video, you will learn how to represent the data in a Map using Power BI. For this purpose, a data that contains the columns such as a State, Province, Country, City, ZIP Code/Postal Code, etc. must be present in the database
33:52
In this video you can explore, what is Star Schema, why it is important in Power BI, Among the most basic design skills in designing a data warehouse solution is the star schema design.
09:10
In this Power BI Tutorial, you will look at how to use Power Query in Power BI Desktop to merge different queries and join kind. This Microsoft Power BI tutorial for beginners is aimed at new Power BI users.
07:49
In this video we will go through the basics of data modelling in Power BI, to get you started fast and easy.
01:52
In this video, learn how to use relationship’s view, what other views exist in Power BI Desktop and why it's important to use them.
10:39
This video explains the importance of cross filter direction in Microsoft Power BI. It discusses how the single or bi-directional filter affects the data in the report.
12:44
In this video you will see details about m language and dax language.
16:22
In this video you will learn how to create two interactive Power BI dashboards, plus a decomposition tree using the free Power BI tools.
26:35
In this video, we will show you how you can use a parameter, within a Power BI report, to dynamically change the data in a report.
15:46

Course/Topic 6 - Python Programming (basic to advanced) - all lectures

This video comprehends the terms Python which is to develop by Guido van Rossum. Guido van Rossum started implementing Python in 1989. Python is a very simple programming language so even if you are new to programming, you can learn python without facing any issues.
21:04
This topic will cover, Installing Python which is generally easy, and nowadays many Linux and UNIX distributions include a recent Python. Even some Windows computers now come with Python already installed.
34:34
In this Python tutorial, we will learn about Python variables and data types which is being used in Python. We will also learn about converting one data type to another in Python and local and global variables in Python. So, let’s begin with Python variables and data types Tutorial.
30:53
In this topic you will learn about the data type which is an important concept. Variables can store data of different types, and different types can do different things.
45:15
This session will teach you about the Python defines type conversion functions to directly convert one data type to another which is useful in day to day and competitive programming. This article is aimed at providing information about certain conversion functions.
31:11
In this tutorial, you will learn about the keywords which is the reserved words in Python and identifiers names given to variables, functions, etc. We cannot use a keyword as a variable name, function name or any other identifier. They are used to define the syntax and structure of the Python language.
40:45
In this tutorial, we are going to learn how to take multiple inputs from the user in Python. The data entered by the user will be in the string format. So, we can use the split method to divide the user entered data.
33:57
This tutorial focuses on two built in functions print and input to perform Input and Output task in Python. Also, you will learn to import modules and use them in your program. Some of the functions like input and print are widely used for standard input and output operations respectively. Let us see the output section first.
54:08
This tutorial covers the different types of operators in Python, operator overloading, precedence and associativity. Just like in mathematics, programming languages like Python have operators. You can think of them as extremely simple functions that lie at the basis of computer science.
35:15
In this tutorial, you'll learn everything about different types of operators in Python, their syntax and how to use them with examples. Operators are special symbols in Python that carry out arithmetic or the logical computation. The value that the operator operates on is called the operand.
31:05
Previously, in our tutorial on Python Operators., Today, in this Python Bitwise Operators Tutorial, we will discuss Python Bitwise AND, OR, XOR, Left-shift, Right-shift, and 1’s complement Bitwise Operators in Python Programming. Along with this, we will discuss syntax and example of Python Bitwise Operators.
44:28
Today, we talk about Python decision making constructs. This includes Python if statements, if else statements, elif statement, nested if conditions and single statement conditions. We will understand these with syntax and example to get a clear understanding. So, let’s start the Python Decision Making Tutorial.
37:10
In this session you will learn about the if elif else which are conditional statements that provide you with the decision making that is required when you want to execute code based on a particular condition. The if elif else statement used in Python helps automate that decision making process.
30:46
In this session, you'll learn the different variations of for loop, for loop is used for iterating over a sequence that is either a list, a tuple, a dictionary, a set, or a string. This is less like the for keyword in other programming languages, and works more like an iterator method as found in other object-orientated programming languages.
37:25
In this session, you will learn to create a while loop in Python. Loops are used in programming to repeat a specific block of code. In this article, you will learn to create a while loop in Python. Loops are used in programming to repeat a specific block of code.
38:54
In this session, we show how to create an infinite loop in Python. An infinite loop that never ends it never breaks out of the loop. So, whatever is in the loop gets executed forever, unless the program is terminated. For certain situations, an infinite loop may be necessary.
38:57
In this video, you will learn how to make the computer execute a group of statements over and over if certain criterion holds. The group of statements being executed repeatedly is called a loop.
29:04
In this session, you'll learn about the different numbers used in Python, how to convert from one data type to the other, and the mathematical operations supported in Python. Python supports integers, floats and complex numbers.
43:57
In the tutorial on strings in Python, you learned how to define strings objects that contain sequences of character data. Processing character data is integral to programming. It is a rare application that doesn’t need to manipulate strings at least to some extent.
23:30
As discussed in the above tutorial, strings in Python are immutable and thus updating or deleting an individual character in a string is not allowed, which means that changing a particular character in a string is not supported in Python. Although, the whole string can be updated and deleted.
55:48
In this session, we'll learn everything about Python lists, how they are created, slicing of a list, adding or removing elements from them and so on. The list is a most versatile datatype available in Python which can be written as a list of comma-separated values items between square brackets. Important thing about a list is that items in a list need not be of the same type.
44:43
In this tutorial, learn how to update list element using Python. Use the index position and assign the new element to change any element of List. You can change the element of the list or item of the list with the methods given here.
39:20
In that tutorial of Python Functions, we discussed user-defined functions in Python. But that isn’t all, a list of Python built-in functions that we can toy around with. In this tutorial on Built-in functions in Python, we will see each of those, we have 67 of those in Python 3.6 with their Python Syntax and examples.
24:00
In this tutorial, you'll learn everything about Python tuples. More specifically, what are tuples, how to create them, when to use them and various methods you should be familiar with. A tuple in Python is similar to a list. The difference between the two is that we cannot change the elements of a tuple once it is assigned whereas we can change the elements of a list.
34:37
This session teaches you the tuple in Python which are immutable sequences, you cannot update them. You cannot add, change, remove items (elements) in tuples.Tuple represent data that you don't need to update, so you should use list rather than tuple if you need to update it. However, if you really need to update tuple, you can convert it to list, update it, and then turn it back into tuple.
39:57
In this tutorial, you'll learn everything about Python dictionaries how they are created, accessing, adding, removing elements from them and various built in methods. Python dictionary is an unordered collection of items. Each item of a dictionary has a pair. Dictionaries are optimized to retrieve values when the key is known.
52:22
In this session we will teach you the dictionary which is a data type similar to arrays, but works with keys and values instead of indexes. Each value stored in a dictionary can be accessed using a key, which is any type of object a string, a number, a list, etc. instead of using its index to address it.
34:32
In this session we will teach you the Python for beginners training course which is a lead the students from the basics of writing and running Python scripts to more advanced features such as file operations, regular expressions, working with binary data, and using the extensive functionality of Python modules. Extra emphasis is placed on features unique to Python, such as tuples, array slices, and output formatting.
38:11
In this video, you will learn to manipulate date and time in Python with the help of examples. Python has a module named datetime to work with dates and times. Let's create a few simple programs related to date and time before we dig deeper.
26:48
In this session, you'll learn about functions, what a function is, the syntax, components, and types of functions. Also, you'll learn to create a function in Python. In Python, a function is a group of related statements that performs a specific task. Functions help break our program into smaller and modular chunks.
42:07
In this video we will learn, the function which use the same variable and object. Pass by Value. In pass by value the function is provided with a copy of the argument object passed to it by the caller. That means the original object stays intact and all changes made are to a copy of the same and stored at different memory locations.
28:54
In this tutorial, you'll learn about the anonymous function, also known as lambda functions. You'll learn what they are, their syntax and how to use them with examples.
31:23
In this tutorial we will teach you the module which is a piece of software that has a specific functionality. Like, when building a ping pong game, one module would be responsible for the game logic, and another module would be responsible for drawing the game on the screen. Each module is a different file, which can be edited separately.
31:39
This session teaches you the Python rename method which is used to rename a file or directory. This method is a part of the python module and comes extremely handy.
37:02
In this tutorial, you'll learn about Python file operations. More specifically, opening a file, reading from it, writing into it, closing it, and various file methods that you should be aware of.
34:35
In this tutorial we will learn about program for files in Python which provides us with an important feature for reading data from the file and writing data into a file. Mostly, in programming languages, all the values or data are stored in some variables which are volatile in nature.
36:20
In this session we will tell you the method that you the current position within the file; in other words, the next read or write will occur at that many bytes from the beginning of the file. The seek method changes the current file position.
38:05
In this tutorial, you'll learn how to handle exceptions in your Python program using try, except and finally statements with the help of examples. Python has many built-in exceptions that are raised when your program encounters an error (something in the program goes wrong).
38:08
In this tutorial, you will learn about different types of errors and exceptions that are built-in to Python. They are raised whenever the Python interpreter encounters errors.
42:46
In this video we will teach you about the Exception handling in Python which is very similar to Java. The code, which harbors the risk of an exception, is embedded in a try block.
31:27
In this tutorial, you will learn about the core functionality of Python objects and classes. You'll learn what a class is, how to create it and use it in your program.
38:34
In this session you will learn about the programming in Python (object-oriented programming) for some time, then you have definitely come across methods that have self as their first parameter. Let us first try to understand what this recurring self-parameter is.
42:03
This tutorial teaches you about the regular expression which is a special sequence of characters that helps you match or find other strings or sets of strings, using a specialized syntax held in a pattern. Regular expressions are widely used in UNIX world.
46:36
In this tutorial we will learn about the python search which is a method of the module That is Syntax of search () re. search (pattern, string). It is similar to re. match () but it doesn’t limit us to find matches at the beginning of the string only. Unlike in re. match () method, here searching for pattern ‘Tutorials’ in the string ‘TP Tutorials Point TP’ will return a match.
53:36
This workshop will introduce GUI programming in Python, it is a is a popular language for elementary programming but it not so easy to write programs with a graphical user interface (GUI).
28:07
In this tutorial, we will learn how to develop graphical user interfaces by writing some Python GUI examples using the Tkinter package. Tkinter package is shipped with Python as a standard package, so we don’t need to install anything to use it.
41:04
This session teaches you about the frame widgets which is a rectangular region on the screen. The frame widget is mainly used as a geometry master for other widgets, or to provide padding between other widgets.
46:10

Course/Topic 7 - R Programming (basic to advanced) - all lectures

In this lecture session we learn about basic introduction to R programming and also talk about some key features of R programming.
16:58
In this lecture session we learn about the setup of R language in your system and also talk about the importance of R programming.
24:13
In this lecture session we learn about variables and data types in R language and also talk about types of variables and data types in R programming.
31:36
In this lecture session we learn about uses of variable and data types in our programs and also talk about some key features of variables and data types.
29:16
In this lecture session we learn about input - output features and also talk about features of input - output features.
38:05
In this lecture session we learn about posted function () in input output features and also talk about features of posted functions().
25:43
In this lecture session we learn about operators in R and also talk about features of operators in R programming.
34:16
In this lecture session we learn about different types of operators in R language and also talk about features of all types of operators in R.
29:19
In this lecture session we learn about vectors in data structure in R programming and also talk about features of vectors in data structures in brief.
32:29
In this lecture session we learn about the importance of vectors in data structure and also talk about vectors in data structures in brief.
30:29
In this lecture session we learn about list data structure in R programming and also talk about features of list in data structure.
33:47
In this lecture session we learn about more operations on the list and also talk about features of List in data structures in brief.
29:10
In this lecture session we learn about matrix in R programming and also talk about features of matrix in data structure in R language.
43:57
In this lecture session we learn about matrix in R programming and also talk about features of matrix in data structure in R language.
25:15
In this lecture session we learn about matrix data structure in R programming and also talk about some key features of matrix and data structure.
09:36
In this lecture session we learn about arrays in R programming and also talk about features of arrays.
31:56
In this lecture session we learn about different types of arrays in data structure and also talk about features of Arrays in data structure in brief.
38:35
In this lecture session we learn about data frame in R programming and also talk about function of data frame.
43:06
In this lecture session we learn about data frame features in R programming and also talk about the importance of data structure.
33:59
In this lecture session we learn about the importance of Data frame in brief and also talk about function of data frame in R programming.
31:20
In this lecture session we learn about data frame key features of data frame in data structure in brief.
19:40
In this lecture session we learn about factors data structures in R programming and also talk about the importance of factors.
34:09
In this lecture session we learn about factors of data structure in R programming and also talk about different types of factors in R language.
17:14
In this lecture session we learn about decision making in R programming and also talk about features of decision making in R.
32:52
In this lecture session we learn about different types of decision making statements and also talk about features of all decision statements.
15:53
In this lecture session we learn about decision making using integers and also talk about functions of integers.
30:02
In this lecture session we learn about Loops in R programming and also talk about factors of Loops in R language.
28:28
In this lecture session we learn about functions of Loops and why we need Loop statement in R programming and also talk about key features of Loop statement.
32:03
In this lecture session we learn about different types of Loops in R programming and also talk about features of For loop, while loop and do while loop.
25:29
In this lecture session we learn about functions in R programming language and also talk about features of functions in R language.
37:42
In this lecture session we learn about different types of functions in R programming and also talk about the importance of functions.
34:11
In this lecture session we learn about string in R programming and also talk about features of string function in R.
25:18
In this lecture session we learn about why we need strings in R programming and also talk about the importance of strings.
26:51
In this lecture session we learn about packages in R programming and also talk about features of packages in R.
35:17
In this lecture session we learn about data and file management in R programming and also talk about functions of data and file management.
32:45
In this lecture session we learn about how we manage the data and file in R programming and also talk about the importance of data and file management.
23:18
In this lecture session we learn about Line chart in R programming and also talk about features of line chart in brief.
33:06
In this lecture session we learn about scatterplot in R language and also talk about functions of scatters plot.
26:38
In this lecture session we learn about Pie chart in R programming and also talk about features of Pie Chart in brief.
33:32
In this lecture session we learn about bar charts in R language and also talk about features of Bar chart in brief.
09:43
In this lecture session we learn about how we use bar charts in R programming and also talk about features of Bar charts.
29:50
In this lecture session we learn about histogram in R programming and also talk about features of histogram.
26:34
In this lecture session we learn about Boxplots in R programming and also talk about features of Boxplot in R language.
21:49

Course/Topic 8 - Data Science with Python - all lectures

In this video tutorial we will get introduced to Data Science and the integration of Python in Data Science. Furthermore, we will look into the importance of Data Science and its demand and the application of Data Science.
1:01:14
In this video we will learn, all the concepts of Python programming related to Data Science. We will also learn about the Introduction to Python Programing, what is Python Programming and its History, Features and Application of Python along with its setup. Further we will see how to get started with the first python program.
59:19
This video talks about the Variable and Data Types in Python Programming. In this session we will learn What is variable, the declaration of variable and variable assignment. Further we will see the data types in python, checking data types and data type conversions.
27:05
This tutorial will help you to understand Data Types in python in depth. This video talks about the data types such as numbers, sequence type, Boolean, set and dictionary.
55:27
This tutorial talks about the Identifier, keyword, reading input and output formatting in Data Science. We will learn about what is an identifier and keywords. Further we will learn about reading input and taking multiple inputs from a user, Output formatting and Python end parameter.
49:19
This tutorial talks about taking multiple inputs from user and output formatting using format method, string method and module operator.
44:09
This tutorial talks about the Operators and type of operators. In this session we will learn about the types of operators such as arithmetic, Relational and Assignment Operators.
27:52
This tutorial talks further about the part 2 of operators and its types. In this session we will learn about the types of operators such as Logical, Membership, Identity and Bitwise Operators.
31:22
In this video you will learn about the process of decision making in Data Science. Furthermore, this tutorial talks about different types of decision-making statements and its application in Data Science.
45:23
In this video tutorial we will learn about the Loops in Python programing. We will cover further the different types of Loops in Python, starting with: For Loop.
32:47
In this session we will cover the further part of loops in Python programming. The type of loops explained in this video is: While loop and nested loop.
39:43
In this session we will cover the further part of loops in Python programming. The type of loops explained in this video is: break, continue and pass loops
23:13
In this video tutorial we will start explaining about the lists in Python Programming. This tutorial talks about accessing values in the list and updating the list in Data Science.
46:54
In this video tutorial we will look into the further parts about the lists in Python Programming. Deleting list elements, basic list operations, built in functions and methods and the features which are covered in this session.
40:30
This tutorial will cover the basics on Tuples and Dictionary function in Data Science. We will learn about accessing and deleting tuple elements. Further we will also cover the basic tuples operations and the built in tuple functions and its methods. At the end we will see the differences in list and tuple.
53:32
This tutorial will cover the advanced topics on Tuples and Dictionary function in Data Science. Further in this session we will learn about the Python Dictionary, how to access, update and delete dictionary elements. Lastly we will cover built in functions and methods.
51:22
In this session we will learn about the functions and modules used in Data science. After watching this video, you will be able to understand what is a function, the definition of function and calling a function.
44:01
In this session we will learn about the further functions and modules used in Data science. After watching this video, you will be able to understand the ways to write a function, Types of functions, Anonymous Functions and Recursive functions.
43:16
In this session we will learn about the advanced functions and modules used in Data science. After watching this video, you will be able to understand what is a module, creating a module, import statement and locating modules.
48:21
This tutorial talks about the features of working with files. In this video we will learn about opening and closing file, the open function, the file object Attributes, the close method, reading and writing files.
1:05:09
This tutorial talks about the advanced features of working with files. In this video we will learn about file positions, renaming and deleting files.
26:50
In this session we will learn about the regular expression. After this video you will be able to understand what is a regular expression, meta characters, match function, search function, Re- match vs research, split function and sub function.
1:02:45
This video introduces you to the Data Science Libraries. In this video you will learn about the Data science libraries: libraries for data processing, modelling and data visualization.
45:35
In this session we will teach about the components of python ecosystem in Data Science. This video talks about the Components of Python Ecosystem using package Python distribution Anaconda and jupyter notebook.
54:24
This tutorial talks about the basics of analyzing data using numpy and pandas. The Pandas module mainly works with the tabular data, whereas the NumPy module works with the numerical data. We will further see what is Numpy and why we use numpy.
56:58
This tutorial talks about the later part of analyzing data using numpy and pandas. In this tutorial we will learn how to install numpy.
43:37
This tutorial talks about the advanced part of analyzing data using numpy and pandas. In this session we will learn what is Pandas and the key features of Pandas. We will also learn about the Python Pandas environment setup.
37:21
This tutorial talks about the advanced part of analyzing data using numpy and pandas. In this session we will learn about Pandas data structure with example.
1:12:53
This the last session on Analysing Data using Numpy and Pandas. In this session we will learn data analysis using Pandas
28:31
In this video tutorial we will learn about the Data Visualization using Matpotlib. This video talks about what is data visualisation, introduction to matplotlib and installation of matplotlib.
37:45
In this session we will see the part 2 of Data Visualization with Matplotlib. This video talks about the types of data visualization charts and line chart scatter plot
43:41
This tutorial covers part 3 of Data Visualization with Matplotlib. This session covers the types of data visualisation charts: bar chart histogram, area plot pie chart and box plot contour plot.
1:09:26
This session talks about the Three-Dimensional Plotting with Matplotlib . In this we will learn about plot 3D scatter, plot 3D contour and plot 3D surface plot.
1:03:43
In this tutorial we will cover basics of Data Visualisation with Seaborn. Further we will cover Introduction to seaborn, seaborn functionalities, how to install seaborn and the different categories of plot in seaborn
41:53
In this tutorial we will cover the advanced topics of Data Visualisation with Seaborn. In this video we will see about exploring seaborn plots.
59:16
Introduction to Statistical Analysis is taught in this video. We will learn what is statistical analysis and introduction to math and statistics for data science. Further we will learn about the terminologies in statistics for data science and categories in statistics, its correlation and lastly mean median and mode quartile.
1:15:05
This video course talks about the basics of Data Science methodology. We will learn how to reach from problem to approach.
47:33
In this session we will see Data Science Methodology from requirements to collection and from understanding to preparation.
44:12
In this session we will learn advanced Data Science Methodology from modelling to evaluation and from deployment to feedback.
39:25
This video tutorial talks about the - Introduction to Machine Learning and its Types. In this session we will learn what is machine learning and the need for machine learning. Further we will see the application of machine learning and different types of machine learning. We will also cover topics such as supervised learning, unsupervised learning and reinforcement learning.
56:17
This video tutorial talks about the basics of regression analysis. We will cover in this video linear regression and implementing linear regression.
1:11:51
This video tutorial talks about the further topics of regression analysis. In this video we will learn about multiple linear regression and implementing multiple linear regression.
54:25
This video tutorial talks about the advanced topics of regression analysis. In this video we will learn about polynomial regression and implementing polynomial regression.
38:18
In this session we will learn about the classification in Data science. We will see what is classification, classification algorithms and Logistic regression. Also we will learn about implementing Logistic regression.
1:08:43
In this session we will learn about the further topics of classification in Data science, such as decision tree and implementing decision tree.
38:36
In this session we will learn about the advanced topics of classification in Data science, such as support vendor machine and implementing support vector machine.
25:37
This tutorial will teach you about what is clustering and clustering algorithms. Further we will learn what K means clustering and how does K means clustering work and also about implementing K means clustering.
53:10
In this session we will see the further topics of clustering, such as hierarchical clustering, agglomerative hierarchical clustering, how does agglomerative hierarchical clustering Work and divisive hierarchical clustering.
28:55
This video tutorial talks about the advanced topics of clustering, such as implementation of agglomerative hierarchical clustering.
33:58
This video will help you to understand basics of Association rule learning. In this session we will learn about the Apriori algorithm and the working of Apriori algorithm.
53:30
This video will help you to understand advanced topics of Association rule learning such as implementation of Apriori algorithm.
58:45
This is a session on the practical part of Data Science application. In this example we will see problem statement, data set, exploratory data analysis.
56:40
This is a session on the practical part of Data Science application.
42:39
This is a session on the practical part of Data Science application. In this we will see the implementation of the project.
50:54
This is a session on the practical part of Data Science application
38:18
This is a session on the practical part of Data Science application
1:02:31

Course/Topic 9 - Data Science with R - all lectures

In this lecture session we learn about introduction of data science and also talk about features of data science in R.
54:03
In this lecture session we learn about data collection and management and also talk about features of data collection and management in data science with R.
29:32
In this lecture session we learn about model deployment and maintenance and also talk about functions of model deployment and maintenance in data science with R.
12:47
In this lecture session we learn about setting expectations and also talk about factors of setting expectations in brief.
10:18
In this lecture session we learn about loading data into R and also talk about features of loading data into R and also talk about the importance of loading data into R.
42:20
In this lecture session we learn about exploring data in data science and machine learning and also talk about features of exploring data in data science and machine learning.
08:01
In this lecture session we learn about features of exploring data using R and also talk about factors of exploring data using R.
45:36
In this lecture session we learn about benefits of data cleaning and also talk about features of benefits of data cleaning.
22:44
In this lecture session we learn about cross validation in R and also talk about features of validation in data science with R.
17:32
In these lecture sessions we learn about data transformation in data science with R and also talk about features of data transformation in brief.
1:35:26
In this lecture session we learn about modeling methods in data science with R and also talk about the importance of modeling methods.
20:13
In this lecture session we learn about solving classification problems and also talk about features of solving classification problems in brief.
11:55
In this lecture session we learn about working without known targets in data science with r and also talk about features of working without known targets.
19:58
In this lecture session we learn about evaluating models in data science with R and also talk about features of evaluating models in brief.
28:11
In this lecture session we learn about confusion matrix in indian accounting standards and also talk about features of confusion matrix.
34:03
In this lecture session we learn about introduction to linear regression and also talk about features of linear regression in indian accounting standards.
1:25:24
In this lecture session we learn about linear regression in R and also talk about features and functions of linear regression in brief.
26:51
In this lecture session we learn about linear regression in R in data science with r and also talk about features of linear regression in R language.
41:22
In this lecture session we learn about simple and multiple regression in data science with r and also talk about the basic difference between simple and multiple regression in brief.
56:54
In this lecture session we learn about linear and logistic regression in data science with r language and also talk about functions of linear and logistics regressions.
29:09
In this lecture session we learn about support vector machines (SVM) in R and also talk about features of support vector machines in data science with R language.
45:18
In this lecture session we learn about factors of support vectors machines in data science with R and also talk about features of support vectors machines.
1:30:55
In this lecture session we learn about unsupervised methods in data science with R and also talk about functions of unsupervised methods in data science.
24:44
In this lecture session we learn about clustering in data science with R language and also talk about features of clustering in data science.
50:44
In this lecture session we learn about K-means algorithms in R and also talk about all types of algorithms in data science with R language.
1:09:44
In this lecture session we learn about hierarchical clustering in data science with R language and also talk about features of hierarchical clustering.
33:41
In this lecture session we learn about libraries in data science with R and also talk about libraries of hierarchical clustering in brief.
23:15
In this lecture session we learn about the dendrogram of diana and also talk about all types of clustering in data science with R.
41:05
In this lecture session we learn about market basket analysis in data science with R and also talk about features of market basket analysis in data science with R.
05:08
In this lecture session we learn about MBA and association rule mining in data science with r language.
23:52
In this lecture session we learn about implementing MBA in data science with R and also talk about implementing MBA.
09:18
In this lecture session we learn about association rule learning in data science with R and also talk about features of association rule learning.
24:02
In this lecture session we learn about decision tree algorithms in data science with R and also talk about features of tree algorithms.
36:29
In this lecture session we learn about exploring advanced methods in tree algorithms in data science with R and also talk about features of exploring advanced methods.
48:16
In this lecture session we learn about using kernel methods and also talk about features of using kernel methods in data science with R.
47:43
In this lecture session we learn about documentation and deployment and also talk about features of documentation and deployment in data science with R.
30:09

Course/Topic 10 - Machine Learning (basic to advanced) - all lectures

In this session we will learn about introduction to Machine Learning. We will start by learning about the basics of Linear Algebra required to learn Machine Language. Further we will learn about Linear equations represented by Matrices and Vectors.
32:51
In this module we will learn about the computational roots of matrices. We will learn how to multiply matrix with scalar and vector. We will learn about addition and subtraction of matrices.
27:08
In this module we will learn about Num-Pie Linear Algebra to work on Python. It further includes the understanding of the use of functions - #dot, #vdot, #inner, #matmul, #determinant, #solve, #inv. Basic examples of the #dot, #vdot functions will be discussed.
13:55
In this module we will learn about how the #inner function work in a two-dimension array. We will also learn its usage in #dot and #vdot. We will see explanation of the functions solving examples.
13:39
In this module we will learn about using #matmul function. We will learn about normal product and stack of arrays. We will also learn how to check the dimensions of the array and how to make it compatible.
11:20
In this module we will learn about the #determinant function. The basics of the #determinant function will be explained. Examples will be solved with explanations to understand it.
06:46
In this module we will learn what a Determinant is. We will also learn about how to find a Determinant. We will further learn how to find the Determinant of a 2*2 and 3*3 matrix learn about the basics of #inv function.
08:40
In this module we will learn about the #inv function. We will learn about how to find the inverse of a matrix. We will also learn how to find the Identity matrix for the inverse.
16:32
In this module we will discuss about the inverse of a matrix. We will understand what an Inverse is. We will further learn how the Inverse of a matrix is found.
16:44
In this module we will learn about the difference of the dot( ) and the inner( ). We will see examples of dot( ) and inner( ), We will also learn about the dissimilarities between the dot( ) and inner( ) with the help of examples.
27:37
In this module we will learn about numpy matrix. We will learn the different ways of creating a matrix. We will also learn about a vector as a matrix and its multiplication with matrix.
16:27
In this module we will learn about the #numpy.vdot( ) function. This module is a continuation of the previous module. We will also learn about the #numpy,inner( ) function.
16:24
In this module we will understand the different concepts like Rank, Determinant, Trace, etc, of an array. Then we will learn how to find the item value of a matrix. We will also learn about the matrix and vector products.
25:27
In this module we will learn about the matrix and vector products. We will learn about how it works on imaginary and complex numbers. We will also get an understanding of matmul( ), inner( ), outer( ), linalg.multi_dot( ), tensordot( ), einsom( ), einsum_path( ),linalg.matrix_power( ).
25:02
In this module we will learn about the basics of #inverse of a matrix. We will understand what an Inverse is. We will also see examples of inverse of a matrix and learn how to calculate it.
16:44
In this module we will learn about the basics of Python. We will also learn about the Packages needed by the machine language. We will further learn the basics of numpy, scipy, pandas, skikit-learn, etc. needed machine learning and data science.
16:18
In this module we will understand about SciPy. We will also learn about SkiKit-learn and Theano. We will further learn about TensorFlow, Keras, PyTorch, Pandas, Matplotlib.
19:19
In this module we will see examples of the topics discussed in the previous module. We will also start the basics of Python. We will also solve some basic problems.
18:50
In this module we will continue the basic problems of Python. We will also understand about Operators. We will also see the different operators and its applications.
21:36
In this module we will continue learning the different Operators. We will also learn about Advanced Data types. We will learn and understand the different data types and about Sets.
18:39
In this module we will learn about list. We will see the different functions of list. We will also learn about Jupyter notebook.
17:46
In this module we will learn about #condition statements in Python in brief. We will also learn about the applications of #condition statements We will solve some examples to understand the #condition statements.
25:44
In this module we will learn about the Loop in Pyhton. We will also learn about the different kinds of loops. We will see examples of For loop, and break keyword.
16:48
In this module we will continue with the #for loop. We will also learn about the continue keyword. We will solve examples for the usage of the keywords.
20:48
In this module we will learn about Functions in Python. We will solve examples using different functions. We will understand how functions work.
16:53
In this module we will learn about arguments in functions. We will also solve examples to understand the usage of arguments in functions. We will also learn about #call by reference in Python.
18:40
In this module we will learn about strings. We will also learn about types of arguments for functions in python. We will also see the usage of the different types of arguments.
10:36
In this module we will learn about default arguments. We will also learn about variable arguments. We will solve examples to understand it better.
06:37
In this module we will learn about the remaining arguments. We will understand about default and variable arguments better. We will also learn about keyword arguments.
32:18
In this module we will learn about built-in functions. We will also learn about the different built-in functions in python. We will solve examples to understand the functions better.
37:15
In this module we will continue the previous functions. We will also learn about other built-in functions. We will also learn about bubble sort in python.
09:44
In this module we will learn about the scope of variable in function. We will also learn about the different variables and its usage. We will solve examples using the different variables to understand it better.
13:15
In this module we will learn about the math module in python. We will learn about the different inbuilt functions that deal with math functions. We will solve problems using the different math functions.
12:52
In this module we will continue with the previous lecture. We will also learn about the different arguments in functions. We will also learn about call by reference in python.
18:40
In this module we will continue with the previous lecture. We will also start mathplotlib in python. We will learn the different types of mathplotlib by using jupyter.
1:04:09
In this module we will learn about loan calculator using tkinter. We will also learn how to use the loan calculator. We will solve an example to understand its usage.
33:36
In this module we will continue with the previous lecture. We will learn how to compute payments using functions. We will also learn about the function getmonthlypayment.
36:44
In this module we will learn about numpy function. We will also learn about mathematical and logical operations using numpy. We will also be explained about different numpy arrays.
17:05
In this module we will continue with the previous lecture. We will learn about different numpy attributes. We will solve examples using the different attributes and slicing an array.
24:43
In this module we will learn about advanced slicing of an array. We will use jupyter to do array slicing. We will understand detail how array slicing works.
29:55
In this module we will learn about using jupyter notebook online. We will also learn about ranges. We will learn about creating arrays from ranges. We will also learn about linear space.
28:43
In this module we will learn about the average function. We will also learn about the different averages. We will solve examples to understand the function.
21:25
In this module we will learn about generating random strings and passwords. We will also learn about generating a string of lower and upper case letters. We will solve examples using the different strings.
21:25
In this module we will learn about generating strings. We will also learn about upper case letters and only printing specific letter. We will also learn about alpha numeric letters.
22:46
In this module we will learn about the unique function. We will continue using arrays. We will solve example using unique functions in arrays.
16:52
In this module we will learn about array manipulation function, We will learn about the delete function in numpy. We will solve examples for better understanding.
10:24
In this module we will learn about the insert function in numpy. We will also learn about flattened array. We will solve examples.
10:22
In this module we will learn about examples with two dimension arrays. We will also learn about the ravel function. We will also learn about the rollaxis function, swapaxes function.
14:43
In this module we will learn about statistical functions. We will also learn about min and max values. We will solve examples using the functions.
06:22
In this module we will learn about functions for rounding. We will also learn about round off function, floor function and ceil function. We will solve examples using the functions.
14:16
In this module we will learn about numpy append function. We will also learn about resize function. We will solve examples.
25:20
In this module we will learn about numpy nonzero function. We will also learn about the where function. We will solve examples using the different functions.
14:23
In this module we will learn about matrix library. We will also learn about the different matlib functions We will solve different examples using the matlib function. vvvv
18:25
In this module we will learn about the basic operations that can be done on numpy arrays. We will also learn about arithmetic operations and functions. We will do examples with arithmetic operations.
14:40
In this module we will learn about numpy filter array. We will do programs on numpy filter array. We will solve examples using the filter array.
16:35
In this module we will learn about array manipulation functions. We will see how the array manipulation functions work. We will learn about the different manipulation functions.
29:18
In this module we will learn about broadcasting function in numpy. We will also learn about reshape in numpy. We will also learn about removing function in numpy.
24:23
In this module we will learn about indexing. We will also learn about slicing. We will solve examples to understand the concept.
15:39
In this module we will learn about numpy append function. We will also learn about resize function. We will solve examples using the functions.
25:20
In this module we will learn about conversion of numpy dtypes to native python types. We will also learn to create 4*4 matrix in which 0 and 1 are staggered with zero on the main diagonal. We will also learn to create 10*10 matrix elements on the borders will be equal to 1 and inside 0.
22:11
In this module we will learn how to use a python program to find the maximum and minimum value of a flattened array. We will also see the function called flat and flatten to make the array flattened. We will learn about function import numpy as np and array-np.arrange( )
19:44
In this module we will learn how to generate a random string of a given length. Tutor will address the issues faced in generating random strings. Further in the video, we will discuss the various ways in which generation of a random staring can be performed.
21:46
In this video we will be covering on creating a simple project. We will see the practical on how tutor creates a simple project. We will also see some examples on how to create a simple project. The video talks about how to get common items between 2 python numpy arrays.
21:23
In this video we will talk about another function in python programming called the split function. The function split divides the arrays into sub arrays. The split() method splits a string into an array of substrings. The split() method returns the new array. The split() method does not change the original string. If (" ") is used as separator, the string is split between words.
11:53
This video is a sequel of explanation of spilt function. We will discuss the three types of split functions – 1. Normal split, 2. Horizontal split and 3. Vertical Split. Further we will discuss the roles of split function and what do they do.
12:18
In this video we will learn about the numpy filter array. We will further see what is filtering of array. Getting some elements out of an existing array and creating a new array out of them is called filtering of array, using a bullion index list.
16:35
In this video we will learn about an important topic in Python, i.e Python file handling. We will see what is a file and the type of executable files. Further we will see what is output and how to view the output. Different access modes that can be opened with the file.
14:08
In this video we will see an example on how to open and file in view mode, by giving the name of the file. File statement in Python is used in exception handling to make the code cleaner and much more readable. It simplifies the management of common resources like file streams. ... with open ( 'file_path' , 'w' ) as file : file .
23:38
This video is a continuation of file system tutorial. Here we will see to use the append mode and what is append mode. Python has a built-in open() function to open a file. This function returns a file object, also called a handle, as it is used to read or modify the file accordingly. We can specify the mode while opening a file. In mode, we specify whether we want to read r , write w or append a to the file.
28:56
In this module we will start a new topic known as random module which is a very important part in numpy. Further we will discuss the functionalities of random module to generate random numbers.
14:48
In this module we will see how to generate the arrays on float and hot generate a single floating value from 0 to 1. Further we will see taking array as a parameter and randomly return one of the values.
19:02
In this module we will learn the random module in continuations. The random is the module present in the numpy library. This module contains simple random generation methods.
22:41
In this module how random module contains functions used to generate random numbers. We will also see some permutations and distribution functions.
22:41
In this module we will see the choice functions and the different variants of choice function. Further we will see how to randomly select multiple choices from the list. Random.sample or random.choices are the functions used to select multiple choices or set.
10:03
In this module we will see the difference between the sample function and the choices functions. Further, we will do a random choice from asset with Python, by converting it to tuple.
09:21
In this module we will learn about the random Boolean in Python, using random.choice. In order to generate Random boolean, we use the nextBoolean() method of the java. util. Random class. This returns the next random boolean value from the random generator sequence
43:00
In this module we will learn about the library available in python that is called Pandas. We will see how Pandas is one of the important tools available in Python. Further we will see how Pandas makes sense to list the things.
15:57
In this module we will learn about the basics of Pandas. Further we will see how this an important tool for Data scientist and Analysts and how pandas is the back bone of most of the data projects.
05:53
This module is a sequel of the previous tutorial on Pandas. In this module we will see practical project on pandas using series and dataframes. Lastly we will learn how to handle duplicate and how to handle information method and shape attribute.
32:00
In this video we will see about column clean and how to clean the column. Further we will see how to rename the columns by eliminating symbols and other different ways.
20:58
In this module we will learn about how to work with the missing values or null values. Further we will see if the dataset is inconsistent or has some missing values then how to deal with the missing values when exploring the data.
28:10
In this video we will see how to perform the imputation on column, i.e., metascore which has some null values. Further we will see how to use describe function on the genre column of the dataset.
15:19
In this module we will learn about the frequency of columns. Further we will see about the functio0n called value counts. The value counts function when used on the genre column tells us the frequency of all the columns.
08:09
In this video we will learn about the methods of slicing, selecting and extracting. If these methods are not followed properly then we will receive attribute errors. Further we will learn to manipulate and extract data using column headings and index locations.
18:57
2.7 MATPLOTLIB BASICS
20:22
2.7.1 MATPLOTLIB BASICS
20:38
2.7.2 MATPLOTLIB BASICS
17:32
2.7.3 MATPLOTLIB BASICS
04:00
2.7.4 MATPLOTLIB BASICS
11:31
2.7.5 MATPLOTLIB BASICS
07:23
2.7.6 MATPLOTLIB BASICS
16:55
2.7.7 MATPLOTLIB BASICS
11:53
2.7.8 MATPLOTLIB BASICS
17:16
2.7.9 MATPLOTLIB BASICS
17:40
2.7.9.1 MATPLOTLIB BASICS
16:55
2.7.9.11 MATPLOTLIB BASICS
20:38
2.8 AGE CALCULATOR APP
26:46
2.8.1 AGE CALCULATOR APP
12:22
2.8.2 AGE CALCULATOR APP
33:00
2.8.3 AGE CALCULATOR APP
37:56
3.1 MACHINE LEARNING BASICS
27:27
3.1.1 MACHINE LEARNING BASICS
17:31
3.1.2 MACHINE LEARNING BASICS
17:36
3.1.3 MACHINE LEARNING BASICS
15:38
3.1.4 MACHINE LEARNING BASICS
13:53
3.1.5 MACHINE LEARNING BASICS
11:55
3.1.6 MACHINE LEARNING BASICS
18:51
3.1.7 MACHINE LEARNING BASICS
23:55
3.1.8 MACHINE LEARNING BASICS
22:38
3.1.9 MACHINE LEARNING BASICS
29:13
3.1.9.1 MACHINE LEARNING BASICS
17:36
3.2 MACHINE LEARNING BASICS
08:08
4.1 TYPES OF MACHINE LEARNING
35:11
4.1.1 TYPES OF MACHINE LEARNING
15:03
4.1.2 TYPES OF MACHINE LEARNING
16:18
4.1.3 TYPES OF MACHINE LEARNING
13:58
4.1.4 TYPES OF MACHINE LEARNING
19:45
4.1.5 TYPES OF MACHINE LEARNING
05:12
4.1.6 TYPES OF MACHINE LEARNING
31:39
5.1 TYPES OF MACHINE LEARNING
28:19
5.1.1 TYPES OF MACHINE LEARNING
31:56
5.1.2 TYPES OF MACHINE LEARNING
25:08
5.1.3 TYPES OF MACHINE LEARNING
46:37
5.1.4 TYPES OF MACHINE LEARNING
31:00
5.1.5 TYPES OF MACHINE LEARNING
24:21
5.1.6 TYPES OF MACHINE LEARNING
16:20
5.1.7 TYPES OF MACHINE LEARNING
32:53
5.1.8 TYPES OF MACHINE LEARNING
56:20
5.2 MULTIPLE REGRESSION
34:30
5.2.1 MULTIPLE REGRESSION
37:35
5.2.2 MULTIPLE REGRESSION
40:56
5.2.3 MULTIPLE REGRESSION
56:04
5.2.4 MULTIPLE REGRESSION
46:41
5.2.5 MULTIPLE REGRESSION
38:14
5.2.6 MULTIPLE REGRESSION
37:48
5.2.7 MULTIPLE REGRESSION
1:01:26
5.3 KNN INTRO
26:49
5.3.1 KNN ALGORITHM
48:57
5.3.2 KNN ALGORITHM
11:17
5.3.3 INTRODUCTION TO CONFUSION MATRIX
42:22
5.3.4 INTRODUCTION TO SPLITTING THE DATASET USING TRAINTESTSPLIT
24:37
5.3.5 KNN ALGORITHM
50:29
5.3.6 KNN ALGORITHM
56:10
5.4 INTRODUCTION TO DECISION TREE
44:37
5.4.1 INTRODUCTION TO DECISION TREE
39:32
5.4.2 DECISION TREE ALGORITHM
36:41
5.4.3 DECISION TREE ALGORITHM
20:10
5.4.4 DECISION TREE ALGORITHM
55:37
5.5 UNSUPERVISED LEARNING
23:26
5.5.1 UNSUPERVISED LEARNING
09:16
5.5.2 UNSUPERVISED LEARNING
18:28
5.5.3 UNSUPERVISED LEARNING
29:50
5.5.4 AHC ALGORITHM
46:30
5.5.5 AHC ALGORITHM
19:55
5.6 KMEANS CLUSTERING
23:08
5.6.1 KMEANS CLUSTERING
30:25
5.6.2 KMEANS CLUSTERING
1:01:04
5.6.3 DBSCAN ALGORITHM
37:09
5.6.4 DBSCAN PROGRAM
32:45
5.6.5 DBSCAN PROGRAM
49:56

Course/Topic 11 - Machine Learning with Python - all lectures

In this lecture session we learn about basic introduction to machine learning and also talk about This machine learning process starts with feeding them good quality data and then training the machines by building various machine learning models using the data and different algorithms.
40:25
In this lecture session we learn about types of machine learning in machine learning and also talk about their primary three types of machine learning we also explore and understand the different types of machine learning.
43:26
In this lecture session we learn about Supervised, Unsupervised, and Reinforcement Learning in brief and also talk about some features and factors of Supervised, Unsupervised, and Reinforcement machine Learning.
49:33
In this lecture session we learn about The primary rationale for adopting Python for machine learning is because it is a general purpose programming language that you can use both for research and development and in production. In this post you will discover the Python ecosystem for machine learning.
28:23
In this tutorial we learn about components of python ML Ecosystem in machine learning and also talk about features and factors of Object-Oriented Language: One of the key features of python is Object-Oriented programming. Python supports object-oriented language and concepts of classes, object encapsulation, etc.
38:59
In this tutorial we learn about what pandas is in machine learning and also talk about the pandas package of the most important tool in machine learning and all different tools in brief.
1:01:47
In this lecture session we learn about The most common data format for ML projects is CSV and it comes in various flavors and varying difficulties to parse. In this section, we are going to discuss three common approaches in Python to load CSV data files .
13:13
In this tutorial we learn about regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed and also talk about different types of Regression analysis.
38:11
In this tutorial we learn about how Linear regression is used to predict the value of a continuous dependent variable with the help of independent variables. Logistic and also talk about linear regression is both a statistical and a machine learning algorithm. Linear regression is a popular and uncomplicated algorithm used in data science and machine learning.
1:08:44
In this lecture session we learn about the scikit-learn library in machine learning and also talk about what Scikit-Learn is, how it’s used, and what its basic terminology is. While Scikit-learn is just one of several machine learning libraries available in Python, it is one of the best known. The library provides many efficient versions of a diverse number of machine learning algorithms.
33:20
In this lecture session we learn about creating a train and test dataset in machine learning and also talk about The test data set contains data you are going to apply your model to. In contrast, this data doesn’t have any "expected" output. During the test phase of machine learning, this data is used to estimate how well your model has been trained and to estimate model properties.
48:09
In this tutorial we learn about multiple regression is the extension of ordinary least-squares (OLS) regression because it involves more than one explanatory variable. Multiple linear regression (MLR), also known simply as multiple regression, is a statistical technique that uses several explanatory variables to predict the outcome of a response variable.
29:27
In this lecture session we learn about examples of multiple linear regression in machine learning and also talk about features and functions of Linear regression that can only be used when one has two continuous variables—an independent variable and a dependent variable.
56:30
In this tutorial we learn about Polynomial Regression is a regression algorithm that models the relationship between a dependent (y) and independent variable (x) as nth degree polynomial. The Polynomial Regression equation is given below: It is also called the special case of Multiple Linear Regression in ML.
1:01:15
In this lecture session we learn about classification in machine learning as a supervised learning approach and also talk about attempts to learn between a set of variables and a target set of variables of a test.
47:34
In this tutorial we learn about Logistic regression models to help you determine a probability of what type of visitors are likely to accept the offer — or not. As a result, you can make better decisions about promoting your offer or make decisions about the offer itself and also talk about The three types of logistic regression are: Binary logistic regression is the statistical technique used to predict the relationship between the dependent variable (Y) and the independent variable (X), where the dependent variable is binary in nature. For example, the output can be Success/Failure, 0/1 , True/False, or Yes/No.
1:07:05
In this lecture session we learn about what KNN K-nearest neighbors (KNN) is a type of supervised learning algorithm used for both regression and classification. KNN tries to predict the correct class for the test data by calculating the KNN
35:53
In this lecture session we learn about encoding data columns in machine learning Encoding is the process of converting the data or a given sequence of characters, symbols, alphabets etc., into a specified format, for the secured transmission of data. Decoding is the reverse process of encoding which is to extract the information from the converted format. Data Encoding.
41:41
In this tutorial we learn about decision trees in machine learning. Decision tree is the most powerful and popular tool for classification and prediction. A Decision tree is a flowchart like tree structure, where each internal node denotes a test on an attribute, each branch represents an outcome of the test, and each leaf node (terminal node) holds a class label.
44:02
In this lecture session we learn about Support Vector Machine Algorithm. Support Vector Machine (SVM) is a supervised machine learning algorithm used for both classification and regression. Though we say regression problems as well, it's best suited for classification.
35:02
In this lecture session we learn about An Overview of Clustering in the Cloud. Computer clusters, and in particular Kubernetes clusters, have seen a substantial rise in adoption in the last decade. Startups and tech giants alike are leveraging cluster-based architectures to deploy and manage their applications in the cloud.
57:03
In this lecture session we learn about Cluster analysis is an essential human activity. Cluster analysis is used to form groups or clusters of the same records depending on various measures made on these records. The key design is to define the clusters in ways that can be useful for the objective of the analysis.
57:32
In this lecture session we learn about Hierarchical clustering begins by treating every data point as a separate cluster. Then, it repeatedly executes the subsequent steps: Merge the 2 maximum comparable clusters. We need to continue these steps until all the clusters are merged together. In Hierarchical Clustering, the aim is to produce a hierarchical series of nested clusters.
1:01:57
In this tutorial we learn about implementation of Agglomerates hierarchical clusters in machine learning and also talk about features of hierarchical clusters.
45:13
In this tutorial we learn about Association Rule Learning is a rule-based machine learning technique that is used for finding patterns (relations, structures etc.) in datasets. By learning these patterns we will be able to offer some items to our customers.
1:11:02
In this tutorial we learn about Data Mining enables users to analyze, classify and discover correlations among data. One of the crucial tasks of this process is Association Rule Learning. An important part of data mining is anomaly detection, which is a procedure of search for items or events that do not correspond to a familiar pattern.
1:16:49
In this lecture session we learn that Recommender systems are so commonplace now that many of us use them without even knowing it. Because we can't possibly look through all the products or content on a website, a recommendation system plays an important role in helping us have a better user experience, while also exposing us to more inventory we might not discover otherwise.
32:55
In this lecture session we learn about Recommender Function. An important component of any of these systems is the recommender function, which takes information about the user and predicts the rating that user might assign to a product, for example. Predicting user ratings, even before the user has actually provided one, makes recommender systems a powerful tool.
1:00:18
In this lecture session we learn about Collaborative filtering is a difference of memory-based reasoning especially well appropriated to the application of supporting personalized recommendations. A collaborative filtering system begins with a history of personal preferences.
20:44
In this tutorial we learn about implementation of move recommender systems in machine learning and also talk about features and functions of implementation of move recommender systems in brief.
1:17:16

Course/Topic 12 - Deep Learning Foundation - all lectures

In this session we will learn about the introduction to Deep Learning. This video talks about Deep Learning as a series introduction and what is a neural network. Furthermore, we will talk about the 3 reasons to go deep and your choice of Deep net.
52:39
In this video tutorial we will discuss about the neural networks and the 3 reasons to go Deep. Further we will also learn about the use of GPU in artificial intelligence and your choice of deep net.
30:06
In this session we will learn about the deep learning models basics. After this video you will be able to understand the concept of restricted Boltzmann machines and deep belief network. Furthermore, you will learn about the convolution neural network and recurrent neural network.
43:00
In this video course further topics of Deep learning models. After this video you will be able to understand the convolution neural network and its characteristics in detail.
1:26:51
In this video course further topics of Deep learning models. After this video you will be able to understand the recurrent neural network and its characteristics.
15:43
In this session the tutor talks about the basic Additional Deep Learning Models. In this video you will learn about Auto encoders, Recursive neural tensor network and generative adversarial networks
44:28
This session is in continuation to the previous session. In this video we will learn about the Recursive Neural Tensor Network in detail and hierarchical structure of data.
31:44
In this Additional Deep Learning Models tutorial, we will proceed with the Generative Adversarial Networks (GAN) and its uses.
22:22
In this video the tutor explains the Platforms and Libraries of Deep Learning. We will start with what is a deep net platform, H2O.ai and Dato Graph Lab. Further we will see what is a Deep Learning Library and Theano and Caffe. We will also cover a bit of Keras and TensorFlow.
53:43
This tutorial will cover the further part of DatoGraph Lab and its history. Further we wil see the benefits and uses of DatoGraph Lab.
28:19
This tutorial will cover the further part of DatoGraph Lab and its history.
28:15
In this video we will cover the further topics of Deep Learning platform and Libraries such as what is a Deep Learning Library? when and how to use Theano and Caffe as Deep Learning Library.
29:19
In previous video we have leant about Theano and Caffe Deep Learning Library. In this video we will learn about the TensorFlow (free and open source library) as a Deep Learning Library and building Deep Learning Models.
40:18
In this video we will learn about the last type of Library i.e. Keras. Keras is an open source neural network library and runs on top of Theano or TensorFlow. We will further see the advantages of Keras in Deep Learning.
25:34

Course/Topic 13 - CISSP (Cybersecurity) - all lectures

In this lecture session we learn about the basics of cybersecurity and also cover basic functions and factors of cybersecurity in brief.
1:10:40
In this lecture session we learn about CISSP certification guide and also talk about factors of CISSP certification guide in cybersecurity.
33:09
In this lecture session we learn about cyber information systems security professional certification domain and talk about overview of domain in brief.
2:19:49
In this lecture session we learn about CISSP exam preparation guide in cyber security and also talk about more guides for exam preparation.
31:24
In this lecture session we learn about CISSP preparation techniques and also talk about cyber security function and importance.
1:04:33
In this lecture session we learn about risk analysis in cyber information systems security professionals and also talk about risk analysis factors in brief.
54:10
In this lecture session we learn about goals of risk analysis and also talk about risk analysis factors in cybersecurity in brief.
39:20
In this lecture session we learn about cybersecurity goals the object of cybersecurity is to prevent the risk and also cover all types of goals in cyber security.
22:03
In this lecture session we learn about types of cyber attacks in cybersecurity and also talk about how we prevent us from thes cyber attacks.
15:54
In this lecture session we learn about types of cyber attackers in cybersecurity and also cover all attackers in brief.
53:16
In this lecture session we learn about cybersecurity archival storage and also talk about storage factors in brief.
54:48
In this lecture session we learn about cybersecurity VPNS and also talk about other VPNs of cybersecurity and importance of VPNs.
54:04
In this lecture session we learn about cyber security standards in system security professionals and also talk about standard security.
56:36
In this lecture session we learn about cyber security challenges in cyber security in cyber attacks.
1:02:03
In this lecture session we learn about different mail service providers and also talk about mail service providers factors.
1:11:00
In this lecture session we learn about the security and risk management domain and also talk about functions of security and risk management.
59:12
In this lecture session we learn about the importance of security and risk management in brief.
23:10
In this lecture session we learn about factors of security and risk management in brief.
34:05
In this lecture session we learn about implementation of confidentiality and also talk about implementation of integrity in brief.