Chapter 1: Introduction to Machine Learning
- Lesson 1: What is Machine Learning?
- Lesson 2: Types of Machine Learning (Supervised, Unsupervised, Semi-Supervised, Reinforcement Learning)
- Lesson 3: Applications and Real-World Use Cases
- Lesson 4: ML Workflow (Data, Model, Evaluation, Deployment)
- Lesson 5: History and Evolution of ML
Chapter 2: Basics of Data and Preprocessing
- Lesson 1: Understanding Data Types and Structures
- Lesson 2: Data Cleaning and Missing Values
- Lesson 3: Data Transformation and Encoding
- Lesson 4: Feature Scaling (Normalization & Standardization)
- Lesson 5: Handling Outliers and Imbalanced Data
Chapter 3: Exploratory Data Analysis (EDA)
- Lesson 1: Visualizing Data Distributions
- Lesson 2: Pairwise Relationships (Correlation, Scatterplots)
- Lesson 3: Detecting Patterns in Data
- Lesson 4: Dimensionality Reduction (Intro to PCA)
- Lesson 5: Using Tools like Pandas, Matplotlib, and Seaborn
Chapter 4: Supervised Learning Basics
- Lesson 1: Introduction to Regression and Classification
- Lesson 2: Linear Regression: Concept and Applications
- Lesson 3: Logistic Regression: Binary Classification
- Lesson 4: Overfitting and Regularization (Ridge, LASSO, Elastic Net)
- Lesson 5: Model Evaluation Metrics (MAE, MSE, RMSE, Accuracy)
Chapter 5: Decision Trees and Variants
- Lesson 1: Concept of Decision Trees
- Lesson 2: CART (Classification and Regression Trees)
- Lesson 3: Pruning and Overfitting in Trees
- Lesson 4: CHAID and M5 Model Trees
- Lesson 5: C4.5 and C5.0 Decision Trees
Chapter 6: Ensemble Learning Techniques
- Lesson 1: What is Ensemble Learning?
- Lesson 2: Bagging Algorithms (Random Forest, Bootstrap Aggregation)
- Lesson 3: Boosting Algorithms (AdaBoost, Gradient Boosting)
- Lesson 4: Stacking and Blending Techniques
- Lesson 5: Comparison of Ensemble Methods
Chapter 7: Support Vector Machines (SVM)
- Lesson 1: Concept of SVM for Classification
- Lesson 2: Kernel Functions in SVM
- Lesson 3: Soft Margin and Hyperparameters
- Lesson 4: Support Vector Regression (SVR)
- Lesson 5: Applications of SVM in Real-World Problems
Chapter 8: Instance-Based Learning
- Lesson 1: K-Nearest Neighbors (KNN) Algorithm
- Lesson 2: Choosing the Right K
- Lesson 3: Distance Metrics and Weighting
- Lesson 4: Locally Weighted Learning (LWL)
- Lesson 5: Applications and Challenges
Chapter 9: Probabilistic Models
- Lesson 1: Naïve Bayes Classifier
- Lesson 2: Gaussian Naïve Bayes
- Lesson 3: Bayesian Linear Regression
- Lesson 4: Assumptions and Limitations
- Lesson 5: Case Studies with Probabilistic Models
Chapter 10: Unsupervised Learning Basics
- Lesson 1: Introduction to Clustering
- Lesson 2: K-Means Clustering
- Lesson 3: Hierarchical Clustering
- Lesson 4: Gaussian Mixture Models (GMM)
- Lesson 5: Applications of Clustering Techniques
Chapter 11: Dimensionality Reduction
- Lesson 1: Principal Component Analysis (PCA)
- Lesson 2: t-SNE for Visualization
- Lesson 3: Linear Discriminant Analysis (LDA)
- Lesson 4: Feature Selection vs Feature Extraction
- Lesson 5: Applications of Dimensionality Reduction
Chapter 12: Association Rule Learning
- Lesson 1: Concept of Association Rules
- Lesson 2: Apriori Algorithm
- Lesson 3: Eclat Algorithm
- Lesson 4: Market Basket Analysis
- Lesson 5: Challenges and Limitations
Chapter 13: Cross-Validation and Model Evaluation
- Lesson 1: Train-Test Split and Validation
- Lesson 2: K-Fold Cross-Validation
- Lesson 3: Stratified Sampling in Cross-Validation
- Lesson 4: Performance Metrics for Classification
- Lesson 5: Performance Metrics for Regression
Chapter 14: Time Series Analysis Basics
- Lesson 1: Introduction to Time Series Data
- Lesson 2: ARIMA Model
- Lesson 3: Decomposition and Seasonal Patterns
- Lesson 4: Dynamic Time Warping
- Lesson 5: Prophet for Time Series Forecasting
Chapter 1: Advanced Ensemble Methods
- Lesson 1: XGBoost: Concepts and Implementation
- Lesson 2: LightGBM and CatBoost
- Lesson 3: Advanced Hyperparameter Tuning in Boosting
- Lesson 4: Comparison of Gradient Boosting Variants
- Lesson 5: Applications and Limitations
Chapter 2: Semi-Supervised and Active Learning
- Lesson 1: Introduction to Semi-Supervised Learning
- Lesson 2: Self-Training and Co-Training Approaches
- Lesson 3: Active Learning for Data Labeling
- Lesson 4: Applications of Semi-Supervised Learning
- Lesson 5: Case Studies
Chapter 3: Advanced Clustering Techniques
- Lesson 1: Density-Based Clustering (DBSCAN)
- Lesson 2: Mean Shift Clustering
- Lesson 3: Spectral Clustering
- Lesson 4: Fuzzy C-Means Clustering
- Lesson 5: Comparison of Advanced Clustering Algorithms
Chapter 4: Bayesian Networks and Probabilistic Graphical Models
- Lesson 1: Introduction to Bayesian Networks
- Lesson 2: Markov Random Fields
- Lesson 3: Conditional Random Fields
- Lesson 4: Applications in Real-World Problems
- Lesson 5: Challenges in Building Bayesian Models
Chapter 5: Advanced Time Series Models
- Lesson 1: Long-Term Forecasting Techniques
- Lesson 2: Prophet in Depth
- Lesson 3: Regularization in Time Series
- Lesson 4: Dynamic Time Warping for Sequence Alignment
- Lesson 5: Use Cases in Finance and Healthcare
Chapter 6: Transformers and Variants
- Lesson 1: Introduction to Transformer Models
- Lesson 2: Applications of Transformers in ML
- Lesson 3: Vision Transformers (ViT)
- Lesson 4: Comparing Transformers with Traditional Methods
- Lesson 5: Advanced Research Trends
Chapter 7: Regularization Techniques
- Lesson 1: Advanced Regularization (Elastic Net, etc.)
- Lesson 2: L1 and L2 Regularization in Depth
- Lesson 3: Impact of Regularization on Overfitting
- Lesson 4: Applications in Sparse Data
- Lesson 5: Case Studies
Chapter 8: Hyperparameter Optimization
- Lesson 1: Grid Search and Random Search
- Lesson 2: Bayesian Optimization
- Lesson 3: Genetic Algorithms for Optimization
- Lesson 4: Tuning Models with Optuna
- Lesson 5: Best Practices in Hyperparameter Tuning
Chapter 9: Emerging Topics in ML
- Lesson 1: Explainable AI (XAI)
- Lesson 2: Fairness and Bias in Machine Learning
- Lesson 3: Ethical Considerations in ML Models
- Lesson 4: Automated Machine Learning (AutoML)
- Lesson 5: Research Directions in ML
Your Message
