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Bundle Multi (2-in-1) - Machine Learning
Learn how to develop machine learning models using Python programming language, grasp basics of Python and how to use it for ML. Become an ML engineerPreview Bundle Multi (2-in-1) - Machine Learning course
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Courses included in Multi (2-in-1) Machine Learning bundle package are:
1) .Machine Learning with Python
2) .Machine Learning (basic to advanced)
Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information. Machine Learning is a subset of AI that is mainly concerned with the development of algorithms which allow a computer to learn from the data and past experiences on their own.
Handling multi-dimensional and multi-variety data – Machine learning algorithms are good at handling data that are multi-dimensional and multi-variety in dynamic or uncertain environments.
Easy identifies trends and patterns - Machine Learning can review large volumes of data and discover specific trends and patterns that would not be apparent to humans.
Machine learning with Python is a popular and powerful approach to building and deploying machine learning models. Python, with its rich ecosystem of libraries and tools, has become one of the most widely used programming languages for machine learning due to its simplicity, readability, and extensive community support. Here's an overview of the key steps involved in machine learning with Python:
1).Install Required Libraries: Before starting, make sure you have Python installed on your machine. You can use the Python package manager, pip, to install the necessary libraries for machine learning, such as NumPy (for numerical computing), Pandas (for data manipulation), Matplotlib (for data visualization), and scikit-learn (for machine learning algorithms).
2).Data Preprocessing: Data preprocessing is a crucial step in machine learning. It involves cleaning and transforming raw data to make it suitable for training models. Common preprocessing steps include handling missing values, scaling features, and encoding categorical variables.
3).Splitting the Data: Split your data into two sets: a training set and a testing/validation set. The training set is used to train the machine learning model, while the testing/validation set is used to evaluate the model's performance on unseen data.
4).Choosing a Model: Select an appropriate machine learning algorithm for your task. scikit-learn offers a variety of supervised and unsupervised learning algorithms, including decision trees, random forests, support vector machines (SVM), k-nearest neighbors (KNN), and more.
5).Training the Model: Use the fit() function in scikit-learn to train the model on your training data. During this step, the model will learn from the input data and adjust its parameters to make accurate predictions.
6).Model Evaluation: After training the model, evaluate its performance on the testing/validation set using appropriate metrics such as accuracy, precision, recall, F1 score, etc., depending on the nature of your problem.
7).Hyperparameter Tuning: Many machine learning models have hyperparameters that need to be set before training. Fine-tune these hyperparameters to optimize the model's performance. Techniques like cross-validation can help in finding the best hyperparameters.
8).Predictions: Once you have a trained and tuned model, you can use it to make predictions on new, unseen data.
9).Model Deployment: Depending on your use case, you might want to deploy your trained model for real-world applications. Popular options for deployment include web services, APIs, or integrating the model into other applications.
Benefits of Python for Machine Learning purposes
Python is simple. It is extremely readable language. The code is clear and decipherable both for those who have written the strings and for those who are just random passerby. Python is open-source. The ML calls for constantly growing and evolving use cases and the benefits of pythons open-sources allow customization according to the developer's needs.
Python's versatility extends beyond scikit-learn. For more complex tasks like deep learning, you can leverage libraries like TensorFlow, PyTorch, or Keras. These libraries provide powerful tools for building and training deep neural networks.
Machine learning with Python offers a wide range of possibilities and applications, from simple linear regression tasks to complex deep learning projects. The wealth of available libraries and active community support make Python an excellent choice for both beginners and experienced data scientists and machine learning practitioners.
Course/Topic 1 - Machine Learning with Python - all lectures
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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.
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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.
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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.
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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.
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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.
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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.
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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 .
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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
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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.
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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.
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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.
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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.
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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.
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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.
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In this tutorial we learn about implementation of Agglomerates hierarchical clusters in machine learning and also talk about features of hierarchical clusters.
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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.
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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.
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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.
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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.
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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.
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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.
Course/Topic 2 - Machine Learning (basic to advanced) - all lectures
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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( ).
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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.
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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.
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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.
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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.
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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.
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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.
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In this module we will learn about list. We will see the different functions of list. We will also learn about Jupyter notebook.
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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.
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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.
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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.
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In this module we will learn about Functions in Python. We will solve examples using different functions. We will understand how functions work.
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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.
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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.
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In this module we will learn about default arguments. We will also learn about variable arguments. We will solve examples to understand it better.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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In this module we will learn about the unique function. We will continue using arrays. We will solve example using unique functions in arrays.
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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.
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In this module we will learn about the insert function in numpy. We will also learn about flattened array. We will solve examples.
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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.
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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.
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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.
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In this module we will learn about numpy append function. We will also learn about resize function. We will solve examples.
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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.
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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
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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.
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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.
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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.
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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.
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In this module we will learn about indexing. We will also learn about slicing. We will solve examples to understand the concept.
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In this module we will learn about numpy append function. We will also learn about resize function. We will solve examples using the functions.
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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.
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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( )
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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.
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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.
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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.
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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.
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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.
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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.
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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 .
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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.
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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.
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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.
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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.
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In this module how random module contains functions used to generate random numbers. We will also see some permutations and distribution functions.
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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.
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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.
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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
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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2.7 MATPLOTLIB BASICS
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2.7.1 MATPLOTLIB BASICS
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2.7.2 MATPLOTLIB BASICS
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2.7.3 MATPLOTLIB BASICS
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2.7.4 MATPLOTLIB BASICS
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2.7.5 MATPLOTLIB BASICS
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2.7.6 MATPLOTLIB BASICS
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2.7.7 MATPLOTLIB BASICS
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2.7.8 MATPLOTLIB BASICS
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2.7.9 MATPLOTLIB BASICS
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2.7.9.1 MATPLOTLIB BASICS
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2.7.9.11 MATPLOTLIB BASICS
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2.8 AGE CALCULATOR APP
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2.8.1 AGE CALCULATOR APP
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2.8.2 AGE CALCULATOR APP
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2.8.3 AGE CALCULATOR APP
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3.1 MACHINE LEARNING BASICS
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3.1.1 MACHINE LEARNING BASICS
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3.1.2 MACHINE LEARNING BASICS
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3.1.3 MACHINE LEARNING BASICS
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3.1.4 MACHINE LEARNING BASICS
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3.1.5 MACHINE LEARNING BASICS
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3.1.6 MACHINE LEARNING BASICS
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3.1.7 MACHINE LEARNING BASICS
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3.1.8 MACHINE LEARNING BASICS
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3.1.9 MACHINE LEARNING BASICS
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3.1.9.1 MACHINE LEARNING BASICS
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3.2 MACHINE LEARNING BASICS
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4.1 TYPES OF MACHINE LEARNING
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4.1.1 TYPES OF MACHINE LEARNING
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4.1.2 TYPES OF MACHINE LEARNING
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4.1.3 TYPES OF MACHINE LEARNING
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4.1.4 TYPES OF MACHINE LEARNING
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4.1.5 TYPES OF MACHINE LEARNING
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4.1.6 TYPES OF MACHINE LEARNING
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5.1 TYPES OF MACHINE LEARNING
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5.1.1 TYPES OF MACHINE LEARNING
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5.1.2 TYPES OF MACHINE LEARNING
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5.1.3 TYPES OF MACHINE LEARNING
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5.1.4 TYPES OF MACHINE LEARNING
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5.1.5 TYPES OF MACHINE LEARNING
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5.1.6 TYPES OF MACHINE LEARNING
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5.1.7 TYPES OF MACHINE LEARNING
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5.1.8 TYPES OF MACHINE LEARNING
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5.2 MULTIPLE REGRESSION
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5.2.1 MULTIPLE REGRESSION
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5.2.2 MULTIPLE REGRESSION
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5.2.3 MULTIPLE REGRESSION
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5.2.4 MULTIPLE REGRESSION
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5.2.5 MULTIPLE REGRESSION
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5.2.6 MULTIPLE REGRESSION
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5.2.7 MULTIPLE REGRESSION
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5.3 KNN INTRO
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5.3.1 KNN ALGORITHM
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5.3.2 KNN ALGORITHM
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5.3.3 INTRODUCTION TO CONFUSION MATRIX
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5.3.4 INTRODUCTION TO SPLITTING THE DATASET USING TRAINTESTSPLIT
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5.3.5 KNN ALGORITHM
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5.3.6 KNN ALGORITHM
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5.4 INTRODUCTION TO DECISION TREE
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5.4.1 INTRODUCTION TO DECISION TREE
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5.4.2 DECISION TREE ALGORITHM
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5.4.3 DECISION TREE ALGORITHM
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5.4.4 DECISION TREE ALGORITHM
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5.5 UNSUPERVISED LEARNING
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5.5.1 UNSUPERVISED LEARNING
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5.5.2 UNSUPERVISED LEARNING
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5.5.3 UNSUPERVISED LEARNING
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5.5.4 AHC ALGORITHM
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5.5.5 AHC ALGORITHM
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5.6 KMEANS CLUSTERING
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5.6.1 KMEANS CLUSTERING
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5.6.2 KMEANS CLUSTERING
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5.6.3 DBSCAN ALGORITHM
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5.6.4 DBSCAN PROGRAM
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5.6.5 DBSCAN PROGRAM
1).Learn how to examine problem
2).Learn how to prepare data
3).Learn how to spot-check a set of algorithms
4).Learn how to examine your results
5).Learn how to double-down on the algorithms that worked best
This course provides a comprehensive introduction to Machine Learning (ML) and its applications, combining theoretical foundations with practical implementations. Participants will learn various ML algorithms, tools, and techniques for building intelligent systems.
Part 1: Introduction to Machine Learning
Week 1: Introduction to Machine Learning
a).What is Machine Learning?
b).Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
c).Key concepts and terminology in ML
d).Overview of the machine learning lifecycle
Week 2: Data Preparation and Preprocessing
a).Importance of data in ML
b).Techniques for data collection and cleaning
c).Handling missing data and outliers
d).Feature selection and engineering
e).Hands-on exercise: Data preprocessing using Python (Pandas)
Week 3: Supervised Learning Algorithms
a).Introduction to supervised learning
b).Linear Regression: Concepts and implementation
c).Classification algorithms: Logistic Regression, Decision Trees, and Support Vector Machines
d).Hands-on exercise: Building a classification model using Scikit-Learn
Week 4: Unsupervised Learning Algorithms
a).Introduction to unsupervised learning
b).Clustering techniques: K-Means, Hierarchical Clustering
c).Dimensionality reduction: PCA (Principal Component Analysis)
d).Hands-on exercise: Implementing clustering algorithms
Week 5: Model Evaluation and Validation
a).Importance of model evaluation
b).Metrics for regression: RMSE, R²
c).Metrics for classification: Accuracy, Precision, Recall, F1 Score
d).Cross-validation techniques and avoiding overfitting
e).Hands-on exercise: Evaluating models using Scikit-Learn
Part 2: Advanced Machine Learning Techniques
Week 6: Ensemble Learning and Boosting
a).Overview of ensemble methods
b).Bagging vs. Boosting
c).Algorithms: Random Forest, AdaBoost, Gradient Boosting
d).Hands-on exercise: Implementing ensemble techniques
Week 7: Neural Networks and Deep Learning
a).Introduction to neural networks
b).Basics of deep learning and its applications
c).Overview of frameworks: TensorFlow and Keras
d).Hands-on exercise: Building a simple neural network
Week 8: Natural Language Processing (NLP)
a).Introduction to NLP and its significance
b).Text preprocessing techniques: Tokenization, stemming, lemmatization
c).Basic NLP tasks: Sentiment analysis, text classification
d).Hands-on exercise: Building a text classification model
Week 9: Model Deployment and Productionization
a).Overview of model deployment concepts
b).Tools for deploying ML models (Flask, Docker)
c).Monitoring and maintaining deployed models
d).Hands-on exercise: Deploying a machine learning model
Week 10: Capstone Project
a).Overview of capstone project objectives
b).Participants will design and implement a complete machine learning solution from data collection to model deployment
c).Presentation of projects and peer reviews
d).Discussion on challenges faced and lessons learned
Recommended Resources:
Textbooks:
1)."Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
2)."Pattern Recognition and Machine Learning" by Christopher M. Bishop
Online Resources:
1).Coursera, edX, and Kaggle for additional exercises and datasets
Tools:
1).Python with libraries: Scikit-Learn, Pandas, NumPy, TensorFlow, Keras
Assessment:
1).Weekly quizzes and assignments
2).Mid-term project focused on a specific machine learning technique
3).Final capstone project showcasing a complete ML solution
The Machine Learning with Python Certification ensures you know planning, production and measurement techniques needed to stand out from the competition.
Python is a general-purpose language, it can do a set of complex machine learning tasks and enable you to build prototypes quickly that allow you to test your product for machine learning purposes.
The scope of Machine Learning with Python is high. Understanding of data structures: The data structure is the core of any software. Thus, it is recommended to have a good grasp of the concepts of data structure. Mathematics: We cannot perform computation without mathematics
Uplatz online training guarantees the participants to successfully go through the Machine Learning with Python certification provided by Uplatz. Uplatz provides appropriate teaching and expertise training to equip the participants for implementing the learnt concepts in an organization.
Course Completion Certificate will be awarded by Uplatz upon successful completion of the Machine Learning with Python online course.
BEST Python Certification Exam in 2022
a).PCAP (Certified Associate in Python Programming)
b).PCPP1 (Certified Professional in Python Programming 1)
c).PCPP2 (Certified Professional in Python Programming 2)
d).CEPP – Certified Expert in Python Programming.
e).Other Important Python Certification:
f).Advantages of Python Certification.
The Machine Learning with Python draw an average salary of $108,080 per year depending on their knowledge and hands-on experience. The Machine Learning with Python job roles are in high demand and make a rewarding career.
Python is undoubtedly the best choice for machine learning. It's easy to understand, which makes data validation quick and practically error-free. By having access to a widely developed library ecosystem, developers can perform complex tasks without extensive coding.
Note that salaries are generally higher at large companies rather than small ones. Your salary will also differ based on the market you work in.
The following are the job titles:
a).Machine learning engineers
b).AI engineers
c).data analysts
d).data scientists
Q1.Explain the terms Artificial Intelligence (AI), Machine Learning (ML and Deep Learning?
Ans-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.
Q2.What are the different types of Learning/ Training models in ML?
Ans-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.
Q3.What is the difference between deep learning and machine learning?
Ans-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:
a).The manner in which data is presented to the system.
b).Machine learning algorithms always require structured data and deep learning networks rely on layers of artificial neural networks.
Q4.What is the main key difference between supervised and unsupervised machine learning?
Ans-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.
Q5.How do you select important variables while working on a data set?
Ans-There are various means to select important variables from a data set that include the following:
a).Identify and discard correlated variables before finalizing on important variables
b).The variables could be selected based on ‘p’ values from Linear Regression
c) Forward, Backward, and Stepwise selection
d) Lasso Regression
e) Random Forest and plot variable chart
f) Top features can be selected based on information gain for the available set of features.
Q6.There are many machine learning algorithms till now. If given a data set, how can one determine which algorithm to be used for that?
Ans-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.
Q7. How are covariance and correlation different from one another?
Ans-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.
Q8. State the differences between causality and correlation?
Ans-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.
Q9. We look at machine learning software almost all the time. How do we apply Machine Learning to Hardware?
Ans-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.
Q10.Explain One-hot encoding and Label Encoding. How do they affect the dimensionality of the given dataset?
Ans-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.