Machine Learning Foundations on Reinforcement Learning Curriculum

Machine Learning Foundations on Reinforcement Learning Curriculum https://codefrydev.in/Reinforcement/ml-foundations/ Recent content in Machine Learning Foundations on Reinforcement Learning Curriculum Reinforcement Learning Curriculum https://codefrydev.in/Reinforcement/og-default.png https://codefrydev.in/Reinforcement/og-default.png Hugo en-us Fri, 20 Mar 2026 00:00:00 +0000 What is Machine Learning? https://codefrydev.in/Reinforcement/ml-foundations/what-is-ml/ Fri, 20 Mar 2026 00:00:00 +0000 https://codefrydev.in/Reinforcement/ml-foundations/what-is-ml/ Three types of ML: supervised, unsupervised, and reinforcement — and why learning from data beats hand-written rules. Datasets and Features https://codefrydev.in/Reinforcement/ml-foundations/datasets-and-features/ Fri, 20 Mar 2026 00:00:00 +0000 https://codefrydev.in/Reinforcement/ml-foundations/datasets-and-features/ Features, labels, and how ML data is structured as rows (samples) and columns (features) in a DataFrame. Linear Regression https://codefrydev.in/Reinforcement/ml-foundations/linear-regression/ Fri, 20 Mar 2026 00:00:00 +0000 https://codefrydev.in/Reinforcement/ml-foundations/linear-regression/ Predict a continuous value with ŷ = wx + b. Derive the MSE loss and compute one gradient descent step from scratch. Gradient Descent https://codefrydev.in/Reinforcement/ml-foundations/gradient-descent/ Fri, 20 Mar 2026 00:00:00 +0000 https://codefrydev.in/Reinforcement/ml-foundations/gradient-descent/ The optimization algorithm behind every trained ML model: iteratively follow the negative gradient to minimize a loss. Multiple Regression https://codefrydev.in/Reinforcement/ml-foundations/multiple-regression/ Fri, 20 Mar 2026 00:00:00 +0000 https://codefrydev.in/Reinforcement/ml-foundations/multiple-regression/ Extend linear regression to multiple features using matrix form ŷ = Xw + b and vectorized NumPy operations. Classification Concepts https://codefrydev.in/Reinforcement/ml-foundations/classification-concepts/ Fri, 20 Mar 2026 00:00:00 +0000 https://codefrydev.in/Reinforcement/ml-foundations/classification-concepts/ Predict categories instead of numbers. Decision boundaries, sigmoid activation, and binary probability outputs. Logistic Regression https://codefrydev.in/Reinforcement/ml-foundations/logistic-regression/ Fri, 20 Mar 2026 00:00:00 +0000 https://codefrydev.in/Reinforcement/ml-foundations/logistic-regression/ Binary classifier from scratch: sigmoid + cross-entropy loss + gradient update. The building block of softmax policies. Model Evaluation https://codefrydev.in/Reinforcement/ml-foundations/model-evaluation/ Fri, 20 Mar 2026 00:00:00 +0000 https://codefrydev.in/Reinforcement/ml-foundations/model-evaluation/ Train/test split, accuracy, precision, recall, and F1 — evaluating classifiers honestly. Cross-Validation and Overfitting https://codefrydev.in/Reinforcement/ml-foundations/cross-validation/ Fri, 20 Mar 2026 00:00:00 +0000 https://codefrydev.in/Reinforcement/ml-foundations/cross-validation/ K-fold cross-validation, overfitting vs underfitting, and the bias-variance tradeoff. K-Nearest Neighbors https://codefrydev.in/Reinforcement/ml-foundations/knn/ Fri, 20 Mar 2026 00:00:00 +0000 https://codefrydev.in/Reinforcement/ml-foundations/knn/ Classify new points by majority vote among K closest training examples. Decision Trees https://codefrydev.in/Reinforcement/ml-foundations/decision-trees/ Fri, 20 Mar 2026 00:00:00 +0000 https://codefrydev.in/Reinforcement/ml-foundations/decision-trees/ If/else questions on features, entropy, and information gain as splitting criteria. K-Means Clustering https://codefrydev.in/Reinforcement/ml-foundations/clustering/ Fri, 20 Mar 2026 00:00:00 +0000 https://codefrydev.in/Reinforcement/ml-foundations/clustering/ Unsupervised grouping of data by alternating assignment and centroid-update steps. Scikit-Learn Workflow https://codefrydev.in/Reinforcement/ml-foundations/sklearn-workflow/ Fri, 20 Mar 2026 00:00:00 +0000 https://codefrydev.in/Reinforcement/ml-foundations/sklearn-workflow/ The full sklearn pipeline: fit, predict, score, and comparing multiple models. ML Mini-Project: Wine Classification https://codefrydev.in/Reinforcement/ml-foundations/ml-mini-project/ Fri, 20 Mar 2026 00:00:00 +0000 https://codefrydev.in/Reinforcement/ml-foundations/ml-mini-project/ End-to-end ML project combining loading, exploration, preprocessing, training, and evaluation. ML Foundations Drills https://codefrydev.in/Reinforcement/ml-foundations/drills/ Fri, 20 Mar 2026 00:00:00 +0000 https://codefrydev.in/Reinforcement/ml-foundations/drills/ 15 short drill problems covering supervised learning, gradient descent, evaluation, and sklearn. ML Foundations Review & Bridge to Deep Learning https://codefrydev.in/Reinforcement/ml-foundations/review-and-bridge/ Fri, 20 Mar 2026 00:00:00 +0000 https://codefrydev.in/Reinforcement/ml-foundations/review-and-bridge/ Review ML Foundations and see why linear models fail on complex patterns — motivation for neural networks.