Learn Programming, AI and Engineering
In-depth tutorials and teaching materials across programming languages, web development, machine learning, algorithms, robotics and hardware — free and open to everyone.
Browse Categories
Programming Languages
Tutorials on Python, C, C++, Java, JavaScript, TypeScript, Go, R and Haskell — from beginner syntax to advanced techniques.
Web Development
HTML5, React, Angular, Next.js, Vite and modern frontend development guides and projects.
Backend Development
Server-side development with Node.js, NestJS, Spring Boot, Python backends, sockets, Nginx and payment integration.
Algorithms & Data Structures
Sorting, trees, hashing, graph algorithms, KMP, LeetCode practice and algorithm analysis teaching materials.
AI & Machine Learning
Machine learning, retrieval-augmented generation (RAG), AI applications and web crawling tutorials.
Databases
SQL and NoSQL database design, queries, optimization and practical exercises.
Computer Science Fundamentals
Computer organization, operating systems, Linux fundamentals and core computer science theory.
Hardware & Embedded Systems
Arduino, RISC-V, MSP430, PLC, Linux drivers and embedded systems engineering.
Robotics & Autonomous Systems
ROS 2, humanoid robots, drones and autonomous vehicle systems.
Networking & DevOps
Computer networking, TCP/IP, CI/CD pipelines and DevOps practice.
Mobile Development
Android and iOS app development tutorials.
Quantitative Finance & Blockchain
Quantitative finance, trading systems and blockchain technology.
Projects & Case Studies
Real-world projects: payment gateway microservices, full-stack apps, Django backends, RAG systems and more.
Latest Articles
Comprehensive Machine Learning & Deep Learning Curriculum
A complete set of professional, textbookquality teaching PDFs covering a depthfirst path from mathematical foundations to production deep learning — written for someone who already codes well but wants real understanding
Read article →Preface: how to use this book
For most of the 1990s and 2000s, if you wanted the best off-the-shelf classifier, you reached for a Support Vector Machine. SVMs combined a beautiful geometric idea — separate the classes with the — with deep optimizatio
Read article →Formulas, Kernels, and Hyperparameter Reference
A compact reference for the formulas, kernels, and knobs used throughout the book.
Read article →What Are Support Vector Machines?
Imagine two groups of points on a page and a ruler you must lay down to separate them. Many positions work — but which is ? A Support Vector Machine answers: the line that leaves the between the groups. That single insti
Read article →The Maximal Margin Classifier
Now we make the widest-street idea precise. For data that be perfectly separated by a line, the maximal margin classifier (the hard-margin SVM) is the cleanest version of the story: a little geometry turns ``make the str
Read article →Soft Margins and Slack
Real data overlaps, contains noise, and is rarely perfectly separable. The — the model people actually use — relaxes the hard constraints by allowing a controlled budget of margin violations. This single change makes SVM
Read article →Hinge Loss and the Primal Problem
We ended the last chapter with a striking reformulation: the soft-margin SVM is just L_2 regularization plus a special loss. That loss is the , and viewing the SVM as ``minimize regularized hinge loss'' (the problem) dem
Read article →Lagrangian Duality and the KKT Conditions
The primal view trains SVMs by gradient descent, but it hides two treasures: support vectors appear, and the kernel trick becomes possible. Both emerge when we rewrite the SVM through . This is the most mathematical chap
Read article →The Kernel Trick
Here is the idea that turned a linear classifier into one of the most powerful tools in machine learning. A linear SVM can only draw straight boundaries — useless for data shaped like rings or spirals. The lets the very
Read article →