“This course contains the use of artificial intelligence.” 

Machine Learning is at the core of modern AI - powering everything from recommendation systems and fraud detection to healthcare diagnostics and autonomous vehicles.

What's in this course?

In this course, you'll learn Machine Learning from the ground up using Python - with a strong focus on building real understanding and hands-on skills, not just theoretical knowledge. You'll start with the fundamentals of Machine Learning -understanding what it is, how systems learn from data, and exploring core techniques including Regression, Classification, Decision Trees, and Clustering.

From there, you'll walk through the complete Machine Learning workflow — defining the problem, preprocessing and exploring data, training models, evaluating performance, visualizing results, and tuning for accuracy — all through practical Python demonstrations using Scikit-learn, Pandas, and Matplotlib. You'll then explore Responsible AI — understanding bias, fairness, transparency, hallucinations, and the ethical responsibilities every ML engineer carries when building real-world systems. Finally, you'll bring everything together in a hands-on capstone project where you'll build and evaluate a complete end-to-end Machine Learning solution.

All topics are taught through concept-based lectures and real hands-on demonstrations, so you don't just understand the theory - you know how to apply it.

Course Structure

• Concepts-based lectures

• Hands-on Demonstrations

• End-to-End Capstone project

Course Contents

• Getting started with ML

• Types of ML

• Regression, Classification & Clustering

• Introduction to Decision Trees and Ensemble Learning

• Setting up your environment and common python ML libraries

• Phases of Machine Learning

• Problem definition

• Working with your data

• Data processing and Exploratory Data Analysis [EDA]

• Train-test Split & Model Training

• Evaluation Metrics

• Result Visualization and Interpretation

• Hyperparameter tuning

• Building Responsible ML Models

• Risks and Challenges

• End to End Capstone project

By the end of this course, you'll have a solid foundation in Machine Learning and the practical skills to confidently build, evaluate, and interpret ML models for real-world use cases.