Machine Learning Roadmap: Beginner to Advanced GuideLast updated: Apr 3, 2026Author :Jitendra KumarLinear Algebra Basics (Vectors, Matrices)Calculus for ML (Derivatives, Gradients)Information Theory (Entropy, KL Divergence)Mathematical FoundationsProbability & StatisticsOptimization Basics (Gradient Descent)Python for ML (NumPy, Pandas)Jupyter NotebooksProgramming & ToolsData Visualization (Matplotlib, Seaborn)Scikit-learn BasicsTypes of ML (Supervised, Unsupervised, Reinforcement)Overfitting vs UnderfittingFeature Engineering & ScalingCore ML ConceptsBias-Variance TradeoffCross-ValidationModel Evaluation Metrics (Accuracy, Precision, Recall, F1, AUC)Linear RegressionDecision TreesSupport Vector Machines (SVM)Naive BayesSupervised LearningLogistic RegressionRandom Forestsk-Nearest Neighbors (kNN)Gradient Boosting (XGBoost, LightGBM, CatBoost)Clustering (k-Means, Hierarchical, DBSCAN)Anomaly DetectionUnsupervised LearningDimensionality Reduction (PCA, t-SNE, UMAP)Association Rule Learning (Apriori, FP-Growth)Introduction to Neural NetworksForward & BackpropagationRNNs & LSTMsGANs (Generative Adversarial Networks)Deep LearningActivation FunctionsCNNs (Convolutional Neural Networks)Transformers & Attention MechanismsTransfer LearningTensorFlow BasicsModel Deployment (Flask, FastAPI, Docker)Experiment TrackingML Engineering & ToolsPyTorch BasicsML Pipelines & Workflow (Airflow, MLflow, Kubeflow)Model Serving & APIsReinforcement Learning (Q-Learning, Deep Q Networks)Natural Language Processing (Word2Vec, BERT, GPT)Time Series ForecastingEthics & Fairness in AIAdvanced TopicsRecommendation SystemsComputer Vision (Image Classification, Object Detection)AutoMLHouse Price PredictionMovie Recommendation SystemSentiment Analysis on TweetsPractical ProjectsSpam Email ClassifierImage Classification (Cats vs Dogs)Stock Price PredictionML Conceptual Q&AFeature Engineering ScenariosCoding ML Problems (Kaggle-style)Interview PreparationCase Studies (Fraud Detection, Ads Ranking, Search)System Design for ML (Data Pipelines, Model Serving)
