Learning notes from DLH projects — machine learning, Python programming, and AI
Fetch pages with `requests`, parse them with BeautifulSoup, log in with sessions and CSRF tokens, then drive a headless browser with Selenium — from static HTML to infinite-scroll pages.
Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor
Clustering in NumPy from scratch: K-means, Gaussian Mixture Models, the
Linear models with scikit-learn — ordinary least squares through regularized regression (Ridge, Lasso), logistic regression and SVM classifiers, with SHAP for explaining predictions.
Build, train, tune, and compare tree-based classifiers — from a single decision tree through ensemble methods (random forest and boosting) — using scikit-learn, XGBoost, and LightGBM on the Wine datas…
End-to-end preprocessing of the Telco Customer Churn dataset — from raw CSV inspection through cleaning, exploratory visualization, statistical testing, feature engineering, encoding, scaling, and tra…
Data manipulation with pandas — from DataFrame creation through transformation, cleaning, indexing, concatenation, descriptive statistics, and time-series visualization, using real Bitcoin trade data.
A progressive implementation of multivariate statistics — from mean vectors and covariance matrices to correlation and the multivariate normal distribution, building toward the Gaussian foundations of…
Yesterday I was in a workshop about building AI-powered personal brands using platforms like bolt.new, Canva, Lovable and other similar tools. The premise of these companies is to make one-shot prompt…
A progressive implementation of Bayes' theorem — building the complete pipeline from likelihood through posterior probability, using a drug trial scenario with NumPy vectorization, then extending to c…
A progressive study of four fundamental probability distributions implemented as Python classes — binomial, normal (Gaussian), Poisson, and exponential — with parameter estimation from data and PMF/PD…
A progressive study of data visualization with Matplotlib — from basic line plots to 3D PCA, covering line graphs, scatter plots, histograms, bar charts, subplots, color mapping, and dimensionality re…
A progressive journey through single-variable and multivariable calculus — from summation and product notation to derivatives, integrals, and Python implementations of polynomial differentiation and i…
A structured template for designing and documenting relational databases. Use this as a guide when planning your schema, defining entities and relationships, and considering optimizations and limitati…
> *A deep dive into the two kinds of attributes every Python developer must understand — what they are, how to create them the Pythonic way, how `__dict__` stores them, and when to use each.*
A progressive journey from manually computing determinants with Python lists to classifying matrix definiteness with NumPy — building deep intuition for the linear algebra pipeline: determinant → mino…
Foundational mathematics for machine learning — linear algebra, calculus, probability, multivariate statistics, and visualization progressing from manual Python implementations to NumPy and Matplotlib…
Data engineering and database operations — SQL from table creation through complex joins, aggregations, constraints, and triggers.
A progressive journey through SQL and MongoDB — from basic table creation and CRUD operations to stored procedures, user-defined functions, indexes, views, and NoSQL document operations with Python/Py…
A progressive journey from manual matrix operations using Python lists to vectorized NumPy computations — building intuition for the linear algebra foundations of machine learning.
A structured curriculum building the mathematical and data engineering foundations for machine learning.
A progressive study of marshaling, pickling, CSV/JSON/XML serialization, and deserialization — transforming Python objects into storable, transmittable formats and back.
A progressive study of file I/O, JSON serialization/deserialization, and object introspection in Python.
A progressive study of advanced OOP concepts: dunder methods, class variables, static methods, class methods, and the object lifecycle.
A progressive study of Object-Oriented Programming in Python: class definition, encapsulation, properties, data validation, and data structures built from classes.
A progressive study of error handling in Python: `try`/`except`/`finally`, raising exceptions, writing to stderr, and building resilient functions.
A progressive study of sets, dictionaries, `map()`/`lambda`, sorting with keys, and advanced data manipulation in Python.
A progressive study of Python's core sequence types: list iteration, indexing, mutation, copying, tuple operations, and matrix traversal.
A progressive study of Python's import system, command-line arguments, module introspection, and the `if __name__ == "__main__"` pattern.
A progressive study of control flow, iteration, the ASCII character set, and function definitions in Python.
A structured curriculum in Python programming — from printing strings to object-oriented design, data structures, exception handling, file I/O, and serialization.
A progressive introduction to printing, string manipulation, formatting, and module imports in Python.
A progressive study of Linux file permissions: user management, ownership, file modes (symbolic and octal), directory permissions, and conditional operations.
A progressive study of Linux shell basics and file permissions — from user management and file operations to ownership, symbolic/octal permission modes, and conditional commands.
A smart music recommendation system powered by multiple embedding models.