Machine Learning

MODULE 1: Introduction to Machine Learning

Overview of machine learning concepts and applications
Types of machine learning: supervised, unsupervised, reinforcement learning
Introduction to Python programming language and libraries (NumPy, Pandas, Matplotlib)

MODULE 2: Supervised Learning

Linear regression: theory and implementation
Logistic regression: theory and implementation
Model evaluation metrics: MSE, RMSE, MAE, confusion matrix, ROC curve

MODULE 3: Supervised Learning (cont.)

Decision trees and ensemble methods (bagging, boosting)
Random forests: theory and implementation
Gradient boosting machines (GBM)

MODULE 4: Unsupervised Learning

Clustering algorithms: K-means, hierarchical clustering
Dimensionality reduction techniques: PCA (Principal Component Analysis), t-SNE (t-distributed Stochastic Neighbor Embedding)

MODULE 5: Neural Networks and Deep Learning

Introduction to artificial neural networks (ANNs)
Deep learning fundamentals: activation functions, backpropagation
Building and training deep neural networks using TensorFlow/Keras

MODULE 6: Convolutional Neural Networks (CNNs)

Introduction to CNNs
CNN architecture and layers
Image classification and object detection using CNNs

MODULE 7: Recurrent Neural Networks (RNNs) and Natural Language Processing (NLP)

Introduction to RNNs
Long Short-Term Memory (LSTM) networks
Text classification and sentiment analysis using RNNs