Machine Learning

Machine Learning

Instructor: Dr. Mohit Bhatnagar

Access provided by Lok Jagruti University

2,528 already enrolled

11 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

4 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

11 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

4 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Learn to frame real-world challenges as machine learning problems. Gain hands-on experience using Python to build and evaluate models.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

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Assessments

16 assignments

Taught in English

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There are 11 modules in this course

This Course Machine Learning offers a comprehensive, hands-on introduction to building and deploying machine learning models using Python. It is designed for learners with a foundational understanding of Python programming and familiarity with basic data analysis concepts. The course begins with a quick review of essential Python libraries such as NumPy, pandas, and Matplotlib, which form the foundation for data manipulation and visualization in data science.

Learners are then introduced to core machine learning concepts, including supervised learning techniques such as classification and regression. The course places a strong emphasis on practical implementation using the scikit-learn package, enabling learners to build, train, and evaluate various models effectively. It also covers artificial neural networks and delves into deep learning through TensorFlow, where participants apply regression and classification techniques on real-world datasets. With the growing importance of unstructured data, the course explores neural network-based models for analyzing text and image data, equipping learners to handle diverse data types. By the end of the course, participants will have the ability to design and implement machine learning workflows, drawing actionable business insights from both structured and unstructured data. This skill set supports careers in data analysis, data engineering, and data science across industries.

Welcome to the Machine Learning course! In this course, you will gain an in-depth introduction to building machine-learning models using Python. In this course, you will initially recapitulate the key Python libraries which are useful for Data Science applications. This includes coverage of Python libraries like Matplotlib, NumPy, and pandas. Next, you are introduced to the basics of machine learning, and the various classification and regression techniques are discussed. Also, the implementation of these techniques using the popular scikit-learn package is covered in detail. Artificial neural networks and the concept of deep learning is next explored with hands-on implementation of regression and classification algorithms using TensorFlow. As businesses increasingly draw insights from unstructured data (text, images, etc.), you would also get insights into neural networks-based deep learning models for the analysis of text and images. This is an advanced-level course, intended for learners with a background using predictive tools and techniques, and a basic understanding of Python programming concepts. The knowledge you gain from this course will help your career as a business analyst or a data engineer and even work toward becoming a data scientist. You will gain skills to apply machine learning algorithms to structured and unstructured data to draw management insights. Data science is an exciting new field used by various organizations to perform data-driven decisions. It is a combination of technical knowledge, mathematics, and business. In this module, we will use Python, one of the most popular languages among all the languages used by data scientists. We will also understand various topics of data science and how to apply them in a real-world scenario.

What's included

9 videos5 readings2 assignments1 discussion prompt

9 videos Total 33 minutes

Course Introduction 3 minutes
Working with Google Colab 5 minutes
Python Basics: Basic Data Structures and Functions 5 minutes
Plotting Libraries for Python: Matplotlib 4 minutes
Plotting Libraries for Python: Seaborn 3 minutes
Working with Arrays: NumPy - Part 1 5 minutes
Working with Arrays: NumPy - Part 2 3 minutes
Python Data Frames: pandas - Part 1 3 minutes
Python Data Frames: pandas - Part 2 3 minutes

5 readings Total 180 minutes

Essential Reading: Getting Started with Google Colaboratory 15 minutes
Essential Reading: Learn Python in 7 Days: Learn Efficient Python Coding Within 7 Days 60 minutes
Essential Reading: Visualization of Data 15 minutes
Essential Reading: Numerical Computing with NumPy 45 minutes
Essential Reading: Data Manipulation and Analysis with pandas 45 minutes

2 assignments Total 30 minutes

Python Basics 15 minutes
Python for Data Science 15 minutes

1 discussion prompt Total 20 minutes

Applications of NumPy and pandas in Business Problems 20 minutes

This assessment is a graded quiz based on the modules covered this week.

What's included

1 assignment

In this module, you will learn about the origin and evolution of machine learning. You will also learn the different ways a machine can learn, and the essential components needed to develop a machine-learning model. You will get an overview of different types of algorithms that you can use to train machine-learning models for specific business problems. The nature and type of data needed to train these algorithms will also be discussed. The module also discusses the different real-world and business best practices and challenges one will have to be sensitive to while deploying machine learning to support business operations.

What's included

9 videos2 readings2 assignments

9 videos Total 67 minutes

History and Evolution of Machine Learning 7 minutes
How a Machine Learns? 9 minutes
Types of Machine Learning 6 minutes
Best-Practices for Using Machine Learning in Business 6 minutes
ML Algorithms for Classification Part 1: Decision Tree and KNN 9 minutes
ML Algorithms for Classification Part 2: Naive-Bayes 8 minutes
ML Algorithms for Prediction (Regression) 7 minutes
Clustering Using ML: k-means Clustering 7 minutes
Understanding the Bias-Variance Trade-Off 7 minutes

2 readings Total 240 minutes

Essential Reading: Origins and Development of Machine Learning 120 minutes
Essential Reading: Machine Learning in Business 120 minutes

2 assignments Total 30 minutes

Origins of Machine Learning 12 minutes
Machine Learning in Business 18 minutes

In this module, you will re-examine several machine learning models. We will discuss hands-on tasks that machine learning is commonly applied to, and you will learn to measure the performance of machine learning systems. We will work with a popular library for the Python programming language called scikit-learn, which has assembled state-of-the-art implementations of many machine learning algorithms.

What's included

9 videos4 readings2 assignments1 discussion prompt

9 videos Total 33 minutes

Introduction to Sklearn 4 minutes
Pre-Processing Tasks: Dimensionality Reduction, Normalization, and Train Test Split 5 minutes
Implementation of a Linear Regression Model 3 minutes
Evaluation of the Regression Model and Making Predictions 3 minutes
Stepwise Regression and Regularization for Model Simplification 4 minutes
Implementation of a Logistic Regression Model 3 minutes
Evaluation of Classification Models: AUC, Recall, and Precision 4 minutes
Evaluating Other Classifiers for Model Improvement 3 minutes
Clustering Using Sklearn 3 minutes

4 readings Total 285 minutes

Essential Reading: Getting Started with Scikit-learn 15 minutes
Essential Reading: Machine Learning with Scikit-learn Quick Start 90 minutes
Essential Reading: Machine Learning with Scikit-Learn 90 minutes
Recommended Reading: Machine Learning by Examples 90 minutes

2 assignments Total 30 minutes

Introduction to Machine Learning Using Python 15 minutes
Classification and Clustering Using Python 15 minutes

1 discussion prompt Total 20 minutes

Applications of Simple Linear and Multiple Linear Regression 20 minutes

This assessment is a graded quiz based on the modules covered this week.

What's included

1 assignment

In this module, you will learn about artificial neural networks (ANNs) and their role in machine learning. You will also learn about the perceptron, the first real-world application based on neural networks. The concepts of weights, biases, and activation functions along with their role in analyzing data and training of ANNs will be discussed. We will also discuss how concepts like backpropagation and gradient descent affect the process of learning with ANNs.

What's included

6 videos2 readings2 assignments

6 videos Total 47 minutes

Origins of ANN and the Perceptron 7 minutes
Using ANNs to Solve Business Use-Cases 8 minutes
Learning with a Perceptron 8 minutes
Activation Functions 8 minutes
Cost Function and Gradient Descent 10 minutes
Challenges in Using ANNs 7 minutes

2 readings Total 240 minutes

Essential Reading: Introduction of Artificial Neural Network (ANN) 120 minutes
Essential Reading: ANNs and Their Issues 120 minutes

2 assignments Total 30 minutes

Introduction of Artificial Neural Network (ANN) 18 minutes
ANNs and Their Issues 12 minutes

In this module, you will learn about using neural network technique for predictive tasks. You will also learn how to use the Python open source TensorFlow machine learning library for implementing regression and classification models to draw insights from structured and unstructured text data. The module also discusses methods for hyperparameter tuning for performance improvement. Lastly, this module will help you to define deep learning models and look at the problem of overfitting and look at ways to identify and overcome it.

What's included

11 videos4 readings2 assignments1 discussion prompt

11 videos Total 54 minutes

Recap of the Artificial Neural Network 5 minutes
Design decisions for an ANN 5 minutes
Introduction to TensorFlow 7 minutes
Defining a Regression Model for Prediction 6 minutes
Hyperparameter Tuning for Performance Improvement 5 minutes
Saving Models and Using them in Production 2 minutes
Revisiting the Bag of Words Model 7 minutes
Implementing a Sentiment Analysis Application 6 minutes
TensorFlow model for Classification 4 minutes
Identifying Overfitting and Overcoming It 5 minutes
Performance Evaluation of a Classification Model 4 minutes

4 readings Total 240 minutes

Essential Reading: Deep Learning with Python and TensorFlow 60 minutes
Recommended Reading: TensorFlow Tutorials 60 minutes
Essential Reading: Deep Learning with Python and TensorFlow 60 minutes
Recommended Reading: TensorFlow Tutorials 60 minutes

2 assignments Total 30 minutes

Regression Modeling Using TensorFlow 15 minutes
Implementing a Sentiment Classifier 15 minutes

1 discussion prompt Total 20 minutes

Classification Versus Regression 20 minutes

This assessment is a graded quiz based on the module covered this week.

What's included

1 assignment

In this module, you will be introduced to the concept of word and image embeddings which are transforming natural language and image processing applications. You will learn how to generate word embeddings using a corpus of text and also use pre trained word embeddings like Glove and Fasttext. This module will also discuss convolution neural networks and image vector-based models for image classification tasks.

What's included

11 videos4 readings2 assignments1 discussion prompt

11 videos Total 61 minutes

Natural Language Processing: An Overview 5 minutes
Introduction to the Concept of Word Embeddings  5 minutes
Generating Word Embeddings 7 minutes
Hands-On with Word Embeddings 10 minutes
Transformers: The State of the Art in NLP 5 minutes
The Hugging Face Transformer Pipelines 6 minutes
Image Files and Their Processing 4 minutes
Convolutional Filters 8 minutes
Convolutional Neural Networks for Image Classification 4 minutes
Hands-On with Convolutional Neural Networks for Classification 4 minutes
Introduction to Vision Transformers 4 minutes

4 readings Total 180 minutes

Essential Reading: Deep Learning with Python and TensorFlow 60 minutes
Recommended Reading: Resources for Learning Natural Language Processing 30 minutes
Essential Reading: Deep Learning with Python and TensorFlow 60 minutes
Recommended Reading: Are Transformers Better Than CNNs at Image Recognition? 30 minutes

2 assignments Total 30 minutes

Building Blocks for Natural Language Processing (NLP) 15 minutes
Image Analysis with the Convolutional Neural Network (CNN) 15 minutes

1 discussion prompt Total 20 minutes

Applications of Natural Language Processing 20 minutes

This assessment is a graded quiz based on the modules covered this week.

What's included

1 assignment

This module describes the learning objectives, and submission instructions for the End-term Assignment for the course.

What's included

1 video

Build toward a degree

This course is part of the following degree program(s) offered by O.P. Jindal Global University. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹