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25 Hours


Machine Learning with Python

Learn about Statistics, Artificial Intelligence, Deep Learning and Data mining.
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Machine Learning is a pathway to the most exciting careers in data analysis today. As data sources multiply along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions. Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions.

What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, etc.


Python is one of the most commonly used languages to build machine learning systems. Most of the resources in this learning path are drawn from top-notch Python conferences such as PyData and PyCon, and created by highly regarded data scientists. Machine learning is the kind of programming which gives computers the capability to automatically learn from data without being explicitly programmed. This means in other words that these programs change their behaviour by learning from data.

Python for Machine Learning is a potent programming language that helps build algorithms for smart and intelligent machines that work without human intervention and continuously learn, evolve, and improve by taking in new data. Python-based machine learning has found a wide variety of use cases in healthcare, insurance, banking, software, and several other industries. With the machine learning industry growing at an exponential rate, it is a trend that will bend the world in the near future. Master ML with Python and become part of the technology revolution that will shape the future world.


In Uplatz's Machine Learning with Python course taught by our elite trainer Imad Jaweed, you will learn various aspects of machine learning and how to use Python programming for machine learning.

In this Machine Learning course, you will learn about training data, and how to use a set of data to discover potentially predictive relationships. You will gain knowledge on how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.

At the end of the course, you will receive a Course Completion Certificate by Uplatz.

Course/Topic - 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
Objectives Back to Top

The difference between the two main types of machine learning methods: supervised and unsupervised

Supervised learning algorithms, including classification and regression

Unsupervised learning algorithms, including Clustering and Dimensionality Reduction

How statistical modeling relates to machine learning and how to compare them

Real-life examples of the different ways machine learning affects society

Syllabus Back to Top

Machine Learning with Python - Course Syllabus


1. Introduction to Machine Learning

  • What is Machine Learning?

  • Need for Machine Learning

  • Why & When to Make Machines Learn?

  • Challenges in Machines Learning

  • Application of Machine Learning

2. Types of Machine Learning

  • Types of Machine Learning

       a) Supervised learning

       b) Unsupervised learning

       c) Reinforcement learning

  • Difference between Supervised and Unsupervised learning

  • Summary

3. Components of Python ML Ecosystem

  • Using Pre-packaged Python Distribution: Anaconda

  • Jupyter Notebook

  • NumPy

  • Pandas

  • Scikit-learn

4. Regression Analysis (Part-I)

  • Regression Analysis

  • Linear Regression

  • Examples on Linear Regression

  • scikit-learn library to implement simple linear regression

5. Regression Analysis (Part-II)

  • Multiple Linear Regression

  • Examples on Multiple Linear Regression

  • Polynomial Regression

  • Examples on Polynomial Regression

6. Classification (Part-I)

  • What is Classification

  • Classification Terminologies in Machine Learning

  • Types of Learner in Classification

  • Logistic Regression

  • Example on Logistic Regression

7. Classification (Part-II)

  • What is KNN?

  • How does the KNN algorithm work?

  • How do you decide the number of neighbors in KNN?

  • Implementation of KNN classifier

  • What is a Decision Tree?

  • Implementation of Decision Tree

  • SVM and its implementation

8. Clustering (Part-I)

  • What is Clustering?

  • Applications of Clustering

  • Clustering Algorithms

  • K-Means Clustering

  • How does K-Means Clustering work?

  • K-Means Clustering algorithm example

9. Clustering (Part-II)

  • Hierarchical Clustering

  • Agglomerative Hierarchical clustering and how does it work

  • Woking of Dendrogram in Hierarchical clustering

  • Implementation of Agglomerative Hierarchical Clustering

10. Association Rule Learning

  • Association Rule Learning

  • Apriori algorithm

  • Working of Apriori algorithm

  • Implementation of Apriori algorithm

11. Recommender Systems

  • Introduction to Recommender Systems

  • Content-based Filtering

  • How Content-based Filtering work

  • Collaborative Filtering

  • Implementation of Movie Recommender System

Certification Back to Top

The course will explain reinforcement learning using a real world case study to ensure that learning is practical and hands-on. 

In this Machine Learning with Python Certification Course, you'll use the TensorFlow framework to build several neural networks and explore more advanced techniques like natural language processing and reinforcement learning. This certification prepares students to expand their career prospects or change career paths by developing foundational data science skills.

Get an industry recognized Certificate by Uplatz upon successful completion of course.

Career & Jobs Back to Top

Datajobs reports that data scientists – including machine learning experts - can earn from $85,000 to $170,000. Glassdoor lists average salaries at individual companies.  Airbnb pays $123,724.  Twitter pays $135,402. Some of the major skills required for this are Programming, Probability, and Statistics, Data Modeling, Machine Learning Algorithms, System Design, etc.

Job Titles:

Entry-Level Software Developer.

Quality Assurance Engineer.

Junior Python Developer.

Python Full Stack Developer.

GIS Analyst.

Senior Python Developer.

Data Scientist.

Machine Learning Engineer


Interview Questions Back to Top

1. Explain the terms Artificial Intelligence (AI), Machine Learning (ML and Deep Learning?

Artificial Intelligence (AI) is the domain of producing intelligent machines. ML refers to systems that can assimilate from experience (training data) and Deep Learning (DL) states to systems that learn from experience on large data sets. ML can be considered as a subset of AI. Deep Learning (DL) is ML but useful to large data sets. The figure below roughly encapsulates the relation between AI, ML, and DL

Additional Information: ASR (Automatic Speech Recognition) & NLP (Natural Language Processing) fall under AI and overlay with ML & DL as ML is often utilized for NLP and ASR tasks.


2. What are the different types of Learning/ Training models in ML?

ML algorithms can be primarily classified depending on the presence/absence of target variables.

A. Supervised learning: [Target is present]
The machine learns using labelled data. The model is trained on an existing data set before it starts making decisions with the new data.
The target variable is continuous: Linear Regression, polynomial Regression, quadratic Regression.
The target variable is categorical: Logistic regression, Naive Bayes, KNN, SVM, Decision Tree, Gradient Boosting, ADA boosting, Bagging, Random forest etc.

B. Unsupervised learning: [Target is absent]
The machine is trained on unlabelled data and without any proper guidance. It automatically infers patterns and relationships in the data by creating clusters. The model learns through observations and deduced structures in the data.
Principal component Analysis, Factor analysis, Singular Value Decomposition etc.

C. Reinforcement Learning:
The model learns through a trial and error method. This kind of learning involves an agent that will interact with the environment to create actions and then discover errors or rewards of that action.


3. What is the difference between deep learning and machine learning?

Machine Learning involves algorithms that learn from patterns of data and then apply it to decision making. Deep Learning, on the other hand, is able to learn through processing data on its own and is quite similar to the human brain where it identifies something, analyse it, and makes a decision.
The key differences are as follow:

  • The manner in which data is presented to the system.
  • Machine learning algorithms always require structured data and deep learning networks rely on layers of artificial neural networks.

4. What is the main key difference between supervised and unsupervised machine learning?

Supervised learning technique needs labeled data to train the model. For example, to solve a classification problem (a supervised learning task), you need to have label data to train the model and to classify the data into your labeled groups. Unsupervised learning does not  need any labelled dataset. This is the main key difference between supervised learning and unsupervised learning.

5. How do you select important variables while working on a data set? 

There are various means to select important variables from a data set that include the following:

  • Identify and discard correlated variables before finalizing on important variables
  • The variables could be selected based on ‘p’ values from Linear Regression
  • Forward, Backward, and Stepwise selection
  • Lasso Regression
  • Random Forest and plot variable chart
  • Top features can be selected based on information gain for the available set of features.

6. There are many machine learning algorithms till now. If given a data set, how can one determine which algorithm to be used for that?

Machine Learning algorithm to be used purely depends on the type of data in a given dataset. If data is linear then, we use linear regression. If data shows non-linearity then, the bagging algorithm would do better. If the data is to be analyzed/interpreted for some business purposes then we can use decision trees or SVM. If the dataset consists of images, videos, audios then, neural networks would be helpful to get the solution accurately.

So, there is no certain metric to decide which algorithm to be used for a given situation or a data set. We need to explore the data using EDA (Exploratory Data Analysis) and understand the purpose of using the dataset to come up with the best fit algorithm. So, it is important to study all the algorithms in detail.

7. How are covariance and correlation different from one another?

Covariance measures how two variables are related to each other and how one would vary with respect to changes in the other variable. If the value is positive it means there is a direct relationship between the variables and one would increase or decrease with an increase or decrease in the base variable respectively, given that all other conditions remain constant.

Correlation quantifies the relationship between two random variables and has only three specific values, i.e., 1, 0, and -1.

1 denotes a positive relationship, -1 denotes a negative relationship, and 0 denotes that the two variables are independent of each other.

8. State the differences between causality and correlation?

Causality applies to situations where one action, say X, causes an outcome, say Y, whereas Correlation is just relating one action (X) to another action(Y) but X does not necessarily cause Y.

9. We look at machine learning software almost all the time. How do we apply Machine Learning to Hardware?

We have to build ML algorithms in System Verilog which is a Hardware development Language and then program it onto an FPGA to apply Machine Learning to hardware.

10. Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?

One-hot encoding is the representation of categorical variables as binary vectors. Label Encoding is converting labels/words into numeric form. Using one-hot encoding increases the dimensionality of the data set. Label encoding doesn’t affect the dimensionality of the data set. One-hot encoding creates a new variable for each level in the variable whereas, in Label encoding, the levels of a variable get encoded as 1 and 0.

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