Machine Learning

Machine Learning

Instructor: Dr. Mohit Bhatnagar

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3,031 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

Build machine learning models with Python using NumPy, pandas, scikit-learn, and TensorFlow for real-world data problems.
Apply machine learning and deep learning techniques including classification, regression, clustering, neural networks, and model evaluation.
Analyze text and images with AI using NLP, word embeddings, sentiment analysis, convolutional neural networks, and image classification.

Details to know

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Assessments

16 assignments

Taught in English

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There are 11 modules in this course

Ready to turn data into smarter decisions? This advanced Machine Learning course is designed for learners with basic Python skills and experience with predictive tools who want to build practical machine learning expertise and move toward roles in business analytics, data engineering, or data science.

You’ll learn how to frame real-world business problems as machine learning tasks and build, evaluate, and improve models using Python. Start with essential data science tools—including NumPy, pandas, and Matplotlib—then progress through classification, regression, clustering, and model evaluation with scikit-learn. Go beyond traditional machine learning with hands-on neural networks and deep learning using TensorFlow. Explore regression and classification, hyperparameter tuning, overfitting, sentiment analysis, natural language processing (NLP), word embeddings, convolutional neural networks (CNNs), and image classification. By the end, you’ll be equipped to apply machine learning algorithms to structured and unstructured data, interpret datasets, and draw meaningful management insights. Build the practical Python, machine learning, and deep learning skills you need to tackle data-driven business challenges and advance your career in data science.

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 videosTotal 33 minutes

Course Introduction3 minutes
Working with Google Colab5 minutes
Python Basics: Basic Data Structures and Functions5 minutes
Plotting Libraries for Python: Matplotlib4 minutes
Plotting Libraries for Python: Seaborn3 minutes
Working with Arrays: NumPy - Part 15 minutes
Working with Arrays: NumPy - Part 23 minutes
Python Data Frames: pandas - Part 13 minutes
Python Data Frames: pandas - Part 2 3 minutes

5 readingsTotal 180 minutes

Essential Reading: Getting Started with Google Colaboratory15 minutes
Essential Reading: Learn Python in 7 Days: Learn Efficient Python Coding Within 7 Days60 minutes
Essential Reading: Visualization of Data15 minutes
Essential Reading: Numerical Computing with NumPy45 minutes
Essential Reading: Data Manipulation and Analysis with pandas45 minutes

2 assignmentsTotal 30 minutes

Python Basics15 minutes
Python for Data Science15 minutes

1 discussion promptTotal 20 minutes

Applications of NumPy and pandas in Business Problems20 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 videosTotal 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 Business6 minutes
ML Algorithms for Classification Part 1: Decision Tree and KNN9 minutes
ML Algorithms for Classification Part 2: Naive-Bayes8 minutes
ML Algorithms for Prediction (Regression) 7 minutes
Clustering Using ML: k-means Clustering7 minutes
Understanding the Bias-Variance Trade-Off 7 minutes

2 readingsTotal 240 minutes

Essential Reading: Origins and Development of Machine Learning120 minutes
Essential Reading: Machine Learning in Business120 minutes

2 assignmentsTotal 30 minutes

Origins of Machine Learning12 minutes
Machine Learning in Business18 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 videosTotal 33 minutes

Introduction to Sklearn4 minutes
Pre-Processing Tasks: Dimensionality Reduction, Normalization, and Train Test Split5 minutes
Implementation of a Linear Regression Model3 minutes
Evaluation of the Regression Model and Making Predictions3 minutes
Stepwise Regression and Regularization for Model Simplification4 minutes
Implementation of a Logistic Regression Model 3 minutes
Evaluation of Classification Models: AUC, Recall, and Precision 4 minutes
Evaluating Other Classifiers for Model Improvement3 minutes
Clustering Using Sklearn3 minutes

4 readingsTotal 285 minutes

Essential Reading: Getting Started with Scikit-learn15 minutes
Essential Reading: Machine Learning with Scikit-learn Quick Start90 minutes
Essential Reading: Machine Learning with Scikit-Learn90 minutes
Recommended Reading: Machine Learning by Examples90 minutes

2 assignmentsTotal 30 minutes

Introduction to Machine Learning Using Python15 minutes
Classification and Clustering Using Python15 minutes

1 discussion promptTotal 20 minutes

Applications of Simple Linear and Multiple Linear Regression20 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 videosTotal 47 minutes

Origins of ANN and the Perceptron7 minutes
Using ANNs to Solve Business Use-Cases8 minutes
Learning with a Perceptron8 minutes
Activation Functions8 minutes
Cost Function and Gradient Descent10 minutes
Challenges in Using ANNs7 minutes

2 readingsTotal 240 minutes

Essential Reading: Introduction of Artificial Neural Network (ANN)120 minutes
Essential Reading: ANNs and Their Issues120 minutes

2 assignmentsTotal 30 minutes

Introduction of Artificial Neural Network (ANN)18 minutes
ANNs and Their Issues12 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 videosTotal 54 minutes

Recap of the Artificial Neural Network5 minutes
Design decisions for an ANN5 minutes
Introduction to TensorFlow7 minutes
Defining a Regression Model for Prediction6 minutes
Hyperparameter Tuning for Performance Improvement5 minutes
Saving Models and Using them in Production2 minutes
Revisiting the Bag of Words Model7 minutes
Implementing a Sentiment Analysis Application6 minutes
TensorFlow model for Classification4 minutes
Identifying Overfitting and Overcoming It5 minutes
Performance Evaluation of a Classification Model4 minutes

4 readingsTotal 240 minutes

Essential Reading: Deep Learning with Python and TensorFlow60 minutes
Recommended Reading: TensorFlow Tutorials60 minutes
Essential Reading: Deep Learning with Python and TensorFlow60 minutes
Recommended Reading: TensorFlow Tutorials60 minutes

2 assignmentsTotal 30 minutes

Regression Modeling Using TensorFlow15 minutes
Implementing a Sentiment Classifier15 minutes

1 discussion promptTotal 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 videosTotal 61 minutes

Natural Language Processing: An Overview5 minutes
Introduction to the Concept of Word Embeddings 5 minutes
Generating Word Embeddings7 minutes
Hands-On with Word Embeddings10 minutes
Transformers: The State of the Art in NLP5 minutes
The Hugging Face Transformer Pipelines6 minutes
Image Files and Their Processing4 minutes
Convolutional Filters8 minutes
Convolutional Neural Networks for Image Classification4 minutes
Hands-On with Convolutional Neural Networks for Classification4 minutes
Introduction to Vision Transformers4 minutes

4 readingsTotal 180 minutes

Essential Reading: Deep Learning with Python and TensorFlow60 minutes
Recommended Reading: Resources for Learning Natural Language Processing30 minutes
Essential Reading: Deep Learning with Python and TensorFlow60 minutes
Recommended Reading: Are Transformers Better Than CNNs at Image Recognition?30 minutes

2 assignmentsTotal 30 minutes

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

1 discussion promptTotal 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