university levels of education Python I — X
Python, University Course and Deep Dives — handmade, no ads, no sign-ups. Every lecture is kept whole and shown whole: nothing is summarised, so what you see is the lecture as it was written. 66,211 words across 86 lectures, from computational thinking and the first program to CPython internals, distributed computing and a capstone project. Twenty-one ways through the same text, forty-six palettes on a white default with dark a click away, twelve layouts and colours you can set yourself. Assembled from a fifty-lecture university course and thirty-six deeper treatments of single topics, renumbered into one continuous sequence so nothing overlaps and nothing was dropped.
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Foundations
Lectures 1 to 13. Introduction to Python and Computational Thinking, Variables, Numerical Data Types, Input, and Precision, Operators, Expressions, and Boolean Logic, Input, Output, Strings, and Formatted Printing, Conditional Statements: if, elif, and else and 8 more, ending on Advanced Object-Oriented Programming: Inheritance, Polymorphism, Encapsulation, and Abstraction. 15,411 words across 13 lectures.
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Scientific Python
Lectures 14 to 21. Introduction to NumPy: Arrays, Vectorization, and Numerical Computing, NumPy Matrix Algebra and Linear Algebra Fundamentals, Data Visualization with Matplotlib, Introduction to pandas: Series, DataFrames, and Data Analysis, Advanced pandas: Merging, Joining, Grouping, Pivot Tables, and Time Series and 3 more, ending on Scientific Computing with SciPy: Numerical Methods and Optimization. 3,960 words across 8 lectures.
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Software Engineering
Lectures 22 to 28. Professional Python Development: Project Organization, Virtual Environments, Testing, and Logging, Concurrency, Parallel Programming, and Asynchronous Programming, Network Programming: Sockets, HTTP, REST APIs, and Client-Server Applications, Database Programming with SQLite and SQL, Web Development with Flask: Routes, Templates, Forms, and SQLite and 2 more, ending on Python Automation: Files, Email, PDFs, Excel, Web Scraping, and Task Scheduling. 3,613 words across 7 lectures.
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Machine Learning
Lectures 29 to 33. Introduction to Machine Learning with scikit-learn, Advanced Machine Learning: Preprocessing, Pipelines, Cross-Validation, and Ensemble Methods, Introduction to Deep Learning with TensorFlow and Keras, Computer Vision with Convolutional Neural Networks (CNNs) and Natural Language Processing (NLP): Text Processing, Transformers, and Large Language Models. 2,316 words across 5 lectures.
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Systems and Performance
Lectures 34 to 41. Software Design Patterns in Python, Performance Optimization and Profiling in Python, Cybersecurity and Secure Python Programming, Packaging, Distribution, Docker, and Deployment, Cloud Computing with Python: Object Storage, Serverless Computing, and Distributed Systems and 3 more, ending on Large-Scale Software Architecture: Layered Design, Microservices, and Event-Driven Systems. 4,071 words across 8 lectures.
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Capstone and Mastery
Lectures 42 to 50. Software Engineering and Capstone Project Planning, Capstone Project I: System Design and Core Implementation, Capstone Project II: Scaling, Monitoring, Security, and Production Deployment, Open-Source Development, GitHub Collaboration, and Professional Python Practices, Advanced Python Internals: CPython, Bytecode, Descriptors, Metaclasses, and Introspection and 4 more, ending on Professional Python Masterclass and Course Review. 4,368 words across 9 lectures.
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Foundations in Depth
Lectures 51 to 59. Values, Names, and Types, Numbers, Text, and Formatting, Lists, Tuples, Dictionaries, and Sets, Control Flow and Functions, Booleans, Comparison, and Logical Operators and 4 more, ending on Files, Paths, and Exceptions. 8,566 words across 9 lectures.
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Objects and Iteration in Depth
Lectures 60 to 68. Classes and Objects, Iterators, Generators, and Lazy Evaluation, Modules, Packages, and Project Layout, Testing and Debugging, Inheritance, MRO, and Composition and 4 more, ending on Regular Expressions. 8,108 words across 9 lectures.
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Advanced Python in Depth
Lectures 69 to 77. Decorators and Closures, Context Managers and Resource Safety, Type Hints, Concurrency, Descriptors and Attribute Access and 4 more, ending on Async Patterns in Practice. 7,429 words across 9 lectures.
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Applied Python in Depth
Lectures 78 to 86. Files, Paths, and Data Formats, Working With Data, Databases with SQLite, Packaging, Environments, and Shipping, HTTP, APIs, and Network Clients and 4 more, ending on Deployment and Operations. 8,369 words across 9 lectures.
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The whole course
All 86 lectures, opened without a filter. 66,211 words, 8,755 blocks, 63 tables and 0 paired examples, with a glossary of 341 entries and a search index of 5,648 words. Twenty-one ways through the same text. Six read it — one lecture entire, an index of every lecture number down the left, a reader that hands you a single section at a time, a continuous scroll, an outline of the section headings, and a syllabus of the whole course. Eight take it apart — every Spanish line beside its English, the glossary lifted out of the tables, the tables on their own, flashcards Spanish side first, the practice questions, a drill that pulls a random example in either direction, and the objectives and closing summaries. Seven keep track — homework, cultural notes, an accent-insensitive search across every block, starred lines, a page of your own notes per lecture, a progress board, and a printable handout. Forty-six colour palettes on a white default with dark one click away, eight colour roles editable by hand, twelve page layouts from a narrow reading column through ruled notebook paper and two magazine columns to a monospaced terminal, seven typefaces with a separate face for the Spanish, and sliders for text size, leading and line length. The English can be hidden to read the Spanish cold, or the Spanish hidden to work back the other way. Progress, stars and notes autosave, with JSON export and import.
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