Syntax and Semantic Errors

CNN Important Case Studies - Lecture 11

New Frontiers in AI and ML

Uncertainty in Deep Learning

Introduction to Large Scale ML/AI and Experimentation

Bias, Interpretability, Fairness in ML and AI - Lecture 22

Intro to Association Rule Learning, Recommender Systems - Lecture 21

Introduction to Boltzmann Machines - Lecture 20

Open AI Gym , Applications in Reinforcement Learning

Deep Q-Learning (DQN) - Lecture 18

Bellman Equation and Optimality (Reinforcement Learning) - Lecture 17

Introduction to Reinforcement Learning

Radial basis Function Networks - Lecture 15

Self Organizing Maps (SOMs) - Lecture 14

Generative Adversarial Networks (GANs)

Introduction to Autoencoders (SAEs, DAEs ,VAEs)

CNN Architectures: GoogLeNet, ResNets, VGG16

Introduction to Convolution Neural Networks (CNNs)

Intro to Attention Mechanisms, Transformers in NLP-Lecture 10

Intro to Natural Language Processing (NLP)-Word2Vec Embeddings-Lecture 8.

Introduction to Sequence Models (RNNs)-Lecture 7

Lecture 6: Machine learning Projects-Important Considerations

Lecture 5 - Regularization, Early Stopping, Dropout.

Lecture 4: Introduction to Artificial Neural Networks

Decision Trees and Ensemble Learning