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

Show More

Course Content

Module 1

  • Introduction To AI

Module 2

Module 3

Module 4

Module 5

Module 6

Module 7

Module 8