Artificial Intelligence and Machine Learning
( 25 Modules )

Module #1
Introduction to Artificial Intelligence
Overview of AI, its history, and applications

Module #2
Machine Learning Fundamentals
Introduction to machine learning, types, and supervised/unsupervised learning

Module #3
Math and Statistics for ML
Linear algebra, calculus, probability, and statistics for machine learning

Module #4
Python for Machine Learning
Introduction to Python, NumPy, Pandas, and data manipulation

Module #5
Data Preprocessing
Data cleaning, feature scaling, and feature selection

Module #6
Supervised Learning
Regression, classification, and model evaluation metrics

Module #7
Linear Regression
Simple and multiple linear regression, cost function, and gradient descent

Module #8
Logistic Regression
Binary classification, logistic function, and decision boundaries

Module #9
Decision Trees
Introduction to decision trees, entropy, and information gain

Module #10
Random Forests
Ensemble learning, bagging, and random forests

Module #11
Support Vector Machines
Maximum-margin classification, soft margin, and kernel trick

Module #12
Unsupervised Learning
Clustering, dimensionality reduction, and anomaly detection

Module #13
K-Means Clustering
K-means algorithm, centroid initialization, and convergence

Module #14
Principal Component Analysis
PCA, feature extraction, and dimensionality reduction

Module #15
Deep Learning Fundamentals
Introduction to neural networks, perceptron, and multilayer perceptron

Module #16
Convolutional Neural Networks
CNNs, convolutional layers, and image classification

Module #17
Recurrent Neural Networks
RNNs, LSTM, and sequence modeling

Module #18
Natural Language Processing
Text preprocessing, tokenization, and word embeddings

Module #19
Transfer Learning
Pre-trained models, fine-tuning, and model zoo

Module #20
Model Evaluation and Selection
Model selection, hyperparameter tuning, and cross-validation

Module #21
Handling Imbalanced Datasets
Class imbalance, oversampling, and undersampling techniques

Module #22
Model Deployment
Model deployment, API integration, and Docker containerization

Module #23
AI Ethics and Fairness
Bias detection, fairness metrics, and ethical considerations

Module #24
Special Topics in AI
Generative models, attention mechanisms, and explainable AI

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Artificial Intelligence and Machine Learning career

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