AI Fundamentals: A Complete Self-Paced Guide

An original, self-contained journey through the essentials of data science, machine learning, deep learning, and natural language processing. Written from scratch for beginners, each chapter pairs clear explanations with runnable examples and practice exercises. Every lesson builds on the last — from setting up your toolkit to shipping production-grade AI.

Table of Contents

• 1. Foundations: Data, AI & Your Toolchain Data science, ML, DL, NLP; Anaconda; VS Code; virtual environments; fixing conda path errors; Python syntax & semantics
• 2. Python Core: Functions, Lists & One-Liners Python basics, real-world list use, lambda, map, filter, function practice
• 3. Python Drills: Practice Programs Temperature conversion, rectangle area, distance covered, lift rounds, line equation; list challenges; dictionary & tuple challenges
• 4. Modules, Files & Error Handling Imports & packages, standard library tour, file operations, file paths, try/except/else/finally with practice
• 5. Object-Oriented Python Classes & objects, inheritance, polymorphism, encapsulation, abstraction, guided practice
• 6. Advanced Python: Dunders, Iterators & Decorators Magic methods, operator overloading, custom exceptions, iterators, generators, closures, decorators, function copying
• 7. NumPy & Pandas: The Data Workhorses NumPy arrays, Series & DataFrames, data manipulation, reading many data sources, assignments
• 8. Data Visualization Matplotlib storytelling, Seaborn statistical charts, real-world plots, practice
• 9. Databases & Logging SQLite3 CRUD, multiple loggers, real-world logging, assignments
• 10. Concurrency & Memory Management Processes vs threads, multithreading, multiprocessing, thread/process pools, web scraping with threads, garbage collection
• 11. Web Development with Flask App skeleton, HTML templates, GET/POST verbs, dynamic URLs & variables, Jinja2, REST with PUT/DELETE
• 12. Interactive Apps with Streamlit Streamlit basics, building and deploying an ML web app
• 13. Descriptive Statistics Statistics in practice, types of statistics, population vs sample, central tendency, dispersion, n-1 intuition, standard deviation, variables, histograms, percentiles, five-number summary, correlation
• 14. Probability & Distributions Addition & multiplication rules, PDF/PMF/CDF, Bernoulli, Binomial, Poisson, Normal, Z-scores, Uniform, Log-Normal, Power Law, Pareto, Central Limit Theorem
• 15. Hypothesis Testing & Inference Estimates, test mechanics, p-values, Z-test, t-distribution & t-test, Z vs T, Type 1/2 errors, Bayes theorem, confidence intervals, chi-square, goodness of fit, ANOVA
• 16. Data Cleaning & Feature Engineering Missing values, imbalanced data, SMOTE, outliers, one-hot / label / ordinal / target-guided encoding
• 17. EDA Case Studies Red Wine quality, Flight Price, Google Play Store cleaning & analysis
• 18. Machine Learning Foundations Types of ML, line & plane & hyperplane, distance from a point, instance vs model-based learning
• 19. Linear Regression Equations, cost function, gradient descent, multiple regression, MSE/MAE/RMSE, overfitting, OLS, hands-on
• 20. Polynomial & Regularized Regression Polynomial intuition & implementation, pipelines, Ridge, Lasso, ElasticNet, cross-validation, feature selection, tuning, projects & AWS deployment
• 21. Logistic Regression Math intuition, one-vs-rest, metrics, grid & random search, imbalanced data, ROC curves
• 22. Support Vector Machines Hard vs soft margins, SVM mathematics, cost function, kernels, SVC & SVR implementations
• 23. Naive Bayes & K-Nearest Neighbors Bayes theorem, NB variants & practice, KNN intuition, KD-trees & ball trees, classifier & regressor
• 24. Decision Trees Entropy & Gini, information gain, numerical splits, pre/post pruning, regression trees, diabetes prediction project
• 25. Ensemble Methods: Bagging & Boosting Random Forest, Adaboost from stumps to weights, Gradient Boosting, XGBoost classification & regression
• 26. Unsupervised Learning & Dimensionality Curse of dimensionality, PCA, eigen-decomposition, KMeans & K-Means++, hierarchical & agglomerative clustering, DBSCAN, silhouette score
• 27. Anomaly Detection Isolation Forest, DBSCAN for outliers, Local Outlier Factor
• 28. Docker & Git Essentials Containers vs images vs VMs, Docker install & commands, Dockerfile, Docker Hub, Compose; Git init, merge, push, checkout, log; resolving conflicts
• 29. End-to-End ML Projects Repo setup, project structure, logging & exceptions, data ingestion, transformation pipelines, model trainer & tuning, prediction pipeline, AWS Beanstalk / EC2+ECR / Azure
• 30. MLOps & Cloud Pipelines Environment setup, GitHub workflow, setup.py packaging, ETL with MongoDB Atlas, data validation, MLflow & DagsHub tracking, model pusher, S3, GitHub Actions, EC2, versioning, BentoML
• 31. NLP: Text Representation Roadmap & use cases, tokenization, stemming, lemmatization, stopwords, POS tagging, NER, one-hot, BOW, N-grams, TF-IDF, Word2Vec & CBOW & Skip-Gram, AvgWord2Vec, Gensim
• 32. NLP Projects & Applications Spam vs ham with BOW & TF-IDF, text classification with Word2Vec & AvgWord2Vec, Kindle review sentiment analysis
• 33. Deep Learning: Neural Networks Why deep learning, perceptron, ANN learning, backpropagation, activation functions, loss functions, optimizers, weight initialization, dropout
• 34. Convolutional Neural Networks Brain analogy, image fundamentals, convolution, padding, CNN vs ANN, pooling, flattening & fully connected layers, RGB example
• 35. Neural Network Projects & Tuning ANN classification project, feature transformation with sklearn, training with optimizers, Streamlit integration & deployment, regression, tuning hidden layers & neurons
• 36. Recurrent Neural Networks RNN vs ANN, forward & backward propagation through time, RNN pitfalls, embedding layers, IMDB dataset, training a simple RNN, Streamlit app
• 37. LSTM & GRU Networks Why LSTM, forget / input / output gates, LSTM training & variants, GRU, text prediction project, bidirectional RNNs
• 38. Attention & Transformers Encoder-decoder, seq2seq problems, attention mechanism, self-attention, multi-head attention, positional encoding, layer normalization, full encoder & decoder
• 39. Agentic AI, Claude & Your Career Claude ecosystem, installation, building agents, agent views, agent teams, hooks, skills & plugins; real-world case studies & next steps