Machine Learning from scratch in depth (2 Years validity with Monthly live Doubt Sessions)

📌 Course Description — Machine Learning for Deep Learning and Generative AI

Machine Learning forms the critical bridge between mathematical foundations and advanced AI systems such as Deep Learning and Generative AI. This course is designed to provide a strong conceptual, mathematical, and practical grounding in machine learning, enabling learners to understand not just how algorithms work, but why they work — and how they scale into modern deep and generative models.

The course begins with the fundamentals of learning from data: problem formulation, feature engineering, data preprocessing, and evaluation strategies. You will explore supervised learning paradigms.

You will then dive into core algorithms such as linear and logistic regression. Each method is developed from an optimization and statistical perspective, showing how loss functions shape learning behavior.

A central focus of the course is to connect classical machine learning to deep learning and generative AI. You will see how concepts like gradient-based optimization, representation learning and probabilistic modeling naturally extend to neural networks, autoencoders, variational methods, and large-scale generative models. This perspective ensures that learners can meaningfully progress into CNNs, transformers, and diffusion models with clarity.

By the end of the course, you will have a robust ML foundation that allows you to understand advanced deep learning architectures and make principled design choices in real-world AI systems.

💡 What You Will Learn

🎯 Who This Course Is For

🚀 Learning Outcomes

After completing this course, learners will be able to: