Machine Learning for Deep Learning and Generative AI

Master machine learning foundations that power deep learning and fuel the intelligence behind generative AI systems.

📌 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

ML problem formulation and data preprocessing
Supervised learning paradigms
Linear & logistic regression from optimization view
Loss functions and model selection
ML foundations behind DL and GenAI models

🎯 Who This Course Is For

Students preparing for Deep Learning and GenAI tracks
Data Science and AI aspirants seeking strong ML foundations
Engineering and CS students building applied ML skills
Professionals transitioning into AI/ML roles
Anyone who wants to understand AI beyond black-box usage

🚀 Learning Outcomes

After completing this course, learners will be able to:

Choose appropriate algorithms based on data and goals
Understand how ML principles scale to deep networks
Confidently progress to Deep Learning and Generative AI systems

Module 1 - Naive Bayes Classification
13 Resources

Module 2 - All Performance Metrics used for Classification Models
8 Resources

Module 3 - Intuition of Naive Bayes for Logistic Regression
8 Resources

Module 4 - Linear Discriminant Analysis (LDA) as Supervised Dimensionality Reduction
4 Resources

Module 5 - Generalized Linear Model for performing Binary Classification
6 Resources

Module 6 - Generalized Linear Model for performing Multi Classification
4 Resources

Module 7 - Generalized Linear Models for performing Regression
7 Resources

₹ 7,999

ONE TIME PAYMENT

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Machine Learning for Deep Learning and Generative AI

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

💡 What You Will Learn

🎯 Who This Course Is For

🚀 Learning Outcomes

Module 1 - Naive Bayes Classification13 Resources

Module 2 - All Performance Metrics used for Classification Models8 Resources

Module 3 - Intuition of Naive Bayes for Logistic Regression8 Resources

Module 4 - Linear Discriminant Analysis (LDA) as Supervised Dimensionality Reduction4 Resources

Module 5 - Generalized Linear Model for performing Binary Classification6 Resources

Module 6 - Generalized Linear Model for performing Multi Classification4 Resources