Machine Learning
( 24 Modules )

Module #1
Introduction to Machine Learning
Overview of machine learning, types of machine learning, and importance of machine learning

Module #2
Mathematical Foundations
Linear algebra, calculus, probability, and statistics

Module #3
Data Preprocessing
Data cleaning, feature scaling, normalization, and feature selection

Module #4
Supervised Learning
Introduction to supervised learning, regression, and classification

Module #5
Linear Regression
Simple and multiple linear regression, cost function, and gradient descent

Module #6
Logistic Regression
Logistic regression, sigmoid function, and cost function

Module #7
Decision Trees
Introduction to decision trees, entropy, and information gain

Module #8
Random Forests
Ensemble learning, random forests, and hyperparameter tuning

Module #9
Support Vector Machines
Introduction to SVMs, kernel trick, and soft margin SVMs

Module #10
Unsupervised Learning
Introduction to unsupervised learning, clustering, and dimensionality reduction

Module #11
K-Means Clustering
K-means clustering algorithm, cost function, and Lloyds algorithm

Module #12
Hierarchical Clustering
Hierarchical clustering, agglomerative and divisive clustering

Module #13
Principal Component Analysis
Introduction to PCA, eigenvalues, and eigenvectors

Module #14
Deep Learning Fundamentals
Introduction to deep learning, neural networks, and perceptron

Module #15
Convolutional Neural Networks
Introduction to CNNs, convolutional layers, and pooling layers

Module #16
Recurrent Neural Networks
Introduction to RNNs, LSTM, and GRU

Module #17
Natural Language Processing
Introduction to NLP, text preprocessing, and word embeddings

Module #18
Model Evaluation and Selection
Metrics for evaluation, overfitting, and model selection techniques

Module #19
Hyperparameter Tuning
Introduction to hyperparameter tuning, grid search, and random search

Module #20
Model Deployment
Deploying machine learning models, model serving, and considerations

Module #21
Ethics and Fairness in Machine Learning
Bias and fairness in machine learning, ethics, and transparency

Module #22
Case Studies in Machine Learning
Real-world applications of machine learning, case studies, and projects

Module #23
Advanced Topics in Machine Learning
Advanced topics in machine learning, including reinforcement learning and generative models

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Machine Learning career

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