Udemy Courses

Personal notes, notebooks, and exercises from Udemy courses on machine learning, deep learning, and reinforcement learning — plus related coursework and side projects that grew out of them (university AI coursework, a quantum-kernel research paper, and a couple of simulation side projects).

Structure

Directory	Content	Course
ML A-Z/	"Machine Learning A-Z" course — data preprocessing, regression (simple/multiple linear, polynomial, SVR, decision tree, random forest), classification (KNN, SVM, kernel SVM, Naive Bayes, decision trees, random forest, logistic regression), clustering (k-means), and reinforcement learning basics (Upper Confidence Bound, Thompson Sampling, in part-6-intro-to-rl/), organized by course part with notebooks, datasets, and notes (code_in_r/, notes/)	Machine Learning A-Z (Udemy)
Deep_learning_cnns/	Deep learning course work, including a full clone of Lazy Programmer's machine_learning_examples repo (ANNs, CNNs, RNNs, NLP, recommenders, reinforcement learning, TensorFlow/PyTorch examples, etc.)	Lazy Programmer's Deep Learning series
hyperparameter_optimization_for_ml/	Hyperparameter optimization course — a clone of the hyperparameter-optimization course repo (grid/random search, Bayesian optimization, Scikit-Optimize, Hyperopt, Optuna), a Kaggle digit-recognizer dataset, personal notes, and a gaussian_basics/ side project including a from-scratch Cholesky decomposition simulator (C++ backend/frontend + Python)	Hyperparameter Optimization for Machine Learning (Udemy)
rl_course/	Reinforcement learning fundamentals — Markov decision processes, dynamic programming (policy/value iteration), on-policy and off-policy Monte Carlo control, with notebooks, LaTeX notes, and a fun_projects/ folder (e.g. an Arsenal vs. PSG RL demo with 2D/3D frontends)	Artificial Intelligence: Reinforcement Learning in Python (Udemy)
ultimate_rag_bootcamp/	RAG (Retrieval-Augmented Generation) bootcamp notes, starting with an intro-to-RAG writeup	Ultimate RAG Bootcamp Using LangChain, LangGraph & LangSmith (Udemy)
ECE469_Artificial_Intelligence/	Coursework for ECE-469 Artificial Intelligence at Cooper Union — homeworks (hw1–hw3), lecture slides, and two programming projects (a game-playing AI checker, an artificial neural network)	— (university course)
quantum_kernel_classification/	Independent research project/paper: "When Do Quantum Kernels Help?" — a from-scratch NumPy simulation of the Havlíček et al. ZZ feature-map quantum kernel, benchmarked against classical kernels, with a NeurIPS-format writeup in paper/	— (independent research)
quantum_machine_learning_qiskit/	"Quantum Machine Learning with Qiskit 2.x" course — Qiskit primitives (SamplerV2/EstimatorV2), classical-to-quantum data encoding/feature maps, variational quantum classifiers, and quantum-kernel SVMs, with notebooks numbered in build order (1 → 4c) and LaTeX notes on encoding schemes	Quantum Machine Learning using Qiskit 2.x (Maven)
brownian_motion_sim/	C++ Brownian motion (Wiener process) simulator producing particle trajectory data	— (side project)
assets/	Standalone images (e.g. STEM-GNN architecture diagrams) referenced from notes	—

Notes

• Some subfolders (Deep_learning_cnns/machine_learning_examples, hyperparameter_optimization_for_ml/hyperparameter-optimization, quantum_machine_learning_qiskit) are git submodules pointing at external course repositories — see .gitmodules.
• Course notes are largely written in LaTeX (.tex, compiled to .pdf) alongside Jupyter notebooks (.ipynb) and R scripts.
• quantum_kernel_classification/ and ECE469_Artificial_Intelligence/ aren't Udemy material — they're adjacent coursework/research kept here alongside it.
• Deep_learning_cnns/ draws on Lazy Programmer's full course catalog (they share one machine_learning_examples repo), not a single course — the link above points to his course listing rather than one title. quantum_machine_learning_qiskit/ is a Maven cohort course, not Udemy, despite living in this repo.
• This is a study/reference repo, not a packaged library — there's no single install step or entry point; open the relevant notebook, notes file, or project README.md for a given topic.