Machine Learning Topics

Explore our comprehensive collection of machine learning topics. Click on any topic to dive deeper into its subtopics.

What is Machine Learning?

Types: Supervised, Unsupervised, Reinforcement Learning Differences between AI, ML, DL

ML Workflow & Pipeline

Data → Preprocessing → Model → Evaluation → Deployment Concept of training vs testing

Regression Algorithms

Linear Regression (Simple & Multiple) Polynomial Regression Metrics: MAE, MSE, RMSE, R² Score

Classification Algorithms

Logistic Regression k-Nearest Neighbors (k-NN) Decision Trees Random Forest Naive Bayes Support Vector Machines (SVM) Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, etc.

Clustering Algorithms

K-Means Clustering Hierarchical Clustering DBSCAN Evaluation: Silhouette Score, Elbow Method

Dimensionality Reduction

Principal Component Analysis (PCA) t-SNE Feature Selection vs Feature Extraction

Model Selection & Tuning

Cross Validation (K-Fold) Grid Search & Random Search Bias-Variance Tradeoff Underfitting vs Overfitting Regularization: L1 (Lasso), L2 (Ridge)

Mini Projects

House Price Prediction (Regression) Email Spam Detection (Classification) Customer Segmentation (Clustering) Credit Card Fraud Detection

Model Deployment Basics

Save models with pickle or joblib Simple Flask web app to deploy a model

Bridge to Deep Learning / NLP

Limitations of traditional ML Why Deep Learning is needed Intro to Neural Networks