Getting Started
Python for Machine Learning
- NumPy Essentials for MLComing soon
- Pandas for Data ManipulationComing soon
- Data Visualization with Matplotlib & SeabornComing soon
- Introduction to Scikit-learnComing soon
- Jupyter Notebook Workflow TipsComing soon
Math & Statistics Foundations
- Linear Algebra Basics: Vectors & MatricesComing soon
- Probability FundamentalsComing soon
- Descriptive Statistics: Mean, Median, VarianceComing soon
- Distributions (Normal, Binomial, Poisson)Coming soon
- Calculus for ML: Derivatives & GradientsComing soon
- Hypothesis Testing BasicsComing soon
Data Preprocessing
- Handling Missing DataComing soon
- Encoding Categorical VariablesComing soon
- Feature Scaling: Normalization vs StandardizationComing soon
- Outlier Detection & TreatmentComing soon
- Train/Test/Validation SplitsComing soon
- Data Leakage — What It Is & How to Avoid ItComing soon
Supervised Learning — Regression
- Simple & Multiple Linear RegressionComing soon
- Polynomial RegressionComing soon
- Ridge & Lasso Regression (Regularization)Coming soon
- Evaluating Regression Models (MAE, MSE, RMSE, R²)Coming soon
- Assumptions of Linear RegressionComing soon
Supervised Learning — Classification
- Logistic RegressionComing soon
- K-Nearest Neighbors (KNN)Coming soon
- Decision TreesComing soon
- Support Vector Machines (SVM)Coming soon
- Naive BayesComing soon
- Evaluating Classifiers (Accuracy, Precision, Recall, F1, ROC-AUC)Coming soon
Model Evaluation & Tuning
- Overfitting vs UnderfittingComing soon
- Cross-Validation TechniquesComing soon
- Bias-Variance TradeoffComing soon
- Hyperparameter Tuning (Grid Search, Random Search)Coming soon
- Confusion Matrix Deep DiveComing soon
Unsupervised Learning
- K-Means ClusteringComing soon
- Hierarchical ClusteringComing soon
- DBSCANComing soon
- Dimensionality Reduction with PCAComing soon
- Anomaly Detection BasicsComing soon
Feature Engineering
- Feature Selection TechniquesComing soon
- Feature Extraction & TransformationComing soon
- Handling Imbalanced DatasetsComing soon
- Working with Text Features (Intro to NLP Preprocessing)Coming soon
Ensemble Methods
- Bagging & Random ForestsComing soon
- Boosting: AdaBoost, Gradient BoostingComing soon
- XGBoost, LightGBM & CatBoostComing soon
- Stacking & Voting ClassifiersComing soon
Neural Networks & Deep Learning Basics
- Perceptrons & the Basic Neuron ModelComing soon
- Activation FunctionsComing soon
- Feedforward Neural Networks & BackpropagationComing soon
- Introduction to TensorFlow & PyTorchComing soon
- Convolutional & Recurrent Networks — A First LookComing soon
Real-World Projects & Deployment
- End-to-End Project: House Price PredictionComing soon
- End-to-End Project: Customer Churn ClassificationComing soon
- Saving & Loading Models (Pickle, Joblib)Coming soon
- Deploying ML Models with Flask/FastAPIComing soon
- Model Monitoring & Retraining BasicsComing soon