Machine Learning-Syllabus
Module 1: Introduction to Machine Learning
- Definition and types of machine learning: supervised, unsupervised, reinforcement
- Overview of applications and case studies
- Ethical considerations in machine learning
Module 2: Data Preprocessing and Exploration
- Data cleaning and handling missing values
- Exploratory Data Analysis (EDA)
- Feature scaling and normalization
- Feature engineering and selection
Module 3: Supervised Learning
- Linear regression and logistic regression
- Decision trees and ensemble methods (Random Forests)
- Support Vector Machines (SVM)
- k-Nearest Neighbors (k-NN)
- Evaluation metrics for classification and regression
Module 4: Unsupervised Learning
- Clustering techniques: K-Means, Hierarchical, DBSCAN
- Dimensionality reduction: Principal Component Analysis (PCA)
- Anomaly detection
- Association rule learning
Module 5: Neural Networks and Deep Learning
- Introduction to neural networks
- Feedforward neural networks and backpropagation
- Convolutional Neural Networks (CNN) for image data
- Recurrent Neural Networks (RNN) for sequential data
Module 6: Model Evaluation and Hyperparameter Tuning
- Cross-validation techniques
- Hyperparameter tuning and grid search
- Model evaluation metrics and confusion matrices
Module 7: Natural Language Processing (NLP)
- Text preprocessing and tokenization
- Word embeddings (Word2Vec, GloVe)
- Sentiment analysis
Module 8: Reinforcement Learning
- Basics of reinforcement learning
- Markov Decision Processes (MDP)
- Q-learning and policy gradient methods
Module 9: Model Deployment and Scaling
- Deployment strategies for machine learning models
- Introduction to cloud services for model deployment
- Scalability considerations and best practices
