Introduction to Artificial Intelligence & Deep Learning
Basics of Machine Learning Review
Python Programming Essentials for AI
Mathematics for Deep Learning (Basic Linear Algebra & Probability)
Neural Networks Fundamentals
Activation Functions and Loss Functions
Forward Propagation and Backpropagation
Gradient Descent Optimization
Building First Neural Network using TensorFlow
Introduction to PyTorch
Image Classification with Convolutional Neural Networks (CNNs)
Sequence Models and Recurrent Neural Networks (RNNs)
Natural Language Processing Basics
Transfer Learning and Pretrained Models
Working with Datasets using Kaggle
Model Training using Google Colab
Overfitting, Underfitting, and Regularization
Hyperparameter Tuning
Real-World Deep Learning Applications
Capstone Project (End-to-End Deep Learning System)