About Course
MODULE 1: Introduction to AI
Overview of artificial intelligence
Historical context and milestones
Types of AI: Narrow vs. General AI
MODULE 2: Machine Learning Basics
Introduction to machine learning
Supervised, unsupervised, and reinforcement learning
Linear regression and logistic regression
MODULE 3: More Machine Learning Techniques
Decision trees and random forests
Support vector machines (SVM)
Clustering algorithms: K-means, hierarchical clustering
MODULE 4: Neural Networks and Deep Learning
Introduction to neural networks
Deep learning fundamentals
Building blocks of deep neural networks: neurons, layers, activations
MODULE 5: Convolutional Neural Networks (CNNs)
Understanding CNN architecture
Applications in image recognition and computer vision
MODULE 6: Recurrent Neural Networks (RNNs) and Natural Language Processing (NLP)
Introduction to RNNs
Applications in sequence modeling, text generation, and sentiment analysis
Basics of NLP: tokenization, stemming, lemmatization
MODULE 7: Reinforcement Learning
Introduction to reinforcement learning (RL)
Markov decision processes (MDP) and Bellman equations
Q-learning and policy gradients
MODULE 8: AI Ethics and Responsible AI
Ethical considerations in AI
Bias and fairness in AI systems
Transparency, interpretability, and accountability
Course Content
Module 1
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Introduction To AI
52:34