Machine Learning & Deep Learning

This course covers both the theoretical foundations and practical implementation of Machine Learning and Deep Learning, helping you understand not just how models work, but also how to build them from scratch.

Topics covered:

• Python for Machine Learning
• Mathematics for ML (Linear Algebra, Probability, Statistics)
• Data preprocessing and feature engineering
• Supervised & Unsupervised Learning
• Model evaluation and optimization
• Neural Networks and Backpropagation
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Transfer Learning and modern DL concepts
• Hands-on implementation using Scikit-learn, PyTorch, and TensorFlow
• Real-world projects and model deployment basics

The sessions combine concept-building, coding exercises, practical projects, and doubt-solving to provide a complete learning experience suitable for beginners and intermediate learners.