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Learning path

Machine Learning Engineer

Implement ML from first principles. Python deep, then build a neural network and a GPT-style transformer from scratch. Plus the linear algebra that makes everything work (Octave / Fortran).

advanced9 courses104 lessons~26 hoursFree
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Course sequence

  1. 01

    Python Fundamentals

    Not started

    Start from zero and build a solid Python foundation. You will learn variables, data types, conditionals, loops, and string manipulation through hands-on exercises with real code execution.

    beginnerpython13 lessons~3h
  2. 02

    Python in Practice

    Not started

    Go beyond the basics. Master lists, dictionaries, functions, and file handling through practical exercises that mirror real-world programming tasks.

    intermediatepython12 lessons~3h
  3. 03

    Advanced Python

    Not started

    Level up with object-oriented programming, generators, decorators, and real-world patterns. This course is for programmers who know the fundamentals and want to write professional Python.

    advancedpython9 lessons~2h
  4. 04

    SQL Fundamentals

    Not started

    Learn SQL: the language for talking to databases. Used by literally every backend that stores anything. SELECT, JOIN, GROUP BY, aggregates, subqueries — once you have these you can answer any question your data can answer.

    beginnersql10 lessons~3h
  5. 05

    SQL Intermediate

    Not started

    Level up from SQL Fundamentals. Subqueries, CTEs, window functions, set operations, transactions, indexes. The SQL features serious analytics and reporting use every day.

    intermediatesql7 lessons~2h
  6. 06

    Octave Fundamentals

    Not started

    Learn GNU Octave: a free, MATLAB-compatible language for numerical computing. Linear algebra, signal processing, statistics — all built into the syntax. If you know MATLAB, you already know 95% of Octave.

    beginneroctave7 lessons~2h
  7. 07

    Octave Intermediate

    Not started

    Level up from Octave Fundamentals. Cell arrays, structs, anonymous functions, file I/O, linear algebra. The features that turn Octave from teaching tool to research-grade computing.

    intermediateoctave7 lessons~2h
  8. 08

    Build a Neural Network

    Not started

    Build an MNIST classifier from scratch in NumPy: neurons, dense layers, sigmoid/ReLU/softmax, MSE/cross-entropy loss, manual backpropagation, gradient descent + Adam, training loop, and the path to PyTorch/TensorFlow.

    advanced25 lessons~6h
  9. 09

    Build a Transformer (GPT-style)

    Not started

    Build a tiny GPT in NumPy: token + position embeddings, layer normalization, scaled dot-product self-attention, multi-head attention with causal mask, feed-forward networks, residual connections, sampling (greedy, top-k, top-p), and the architecture behind ChatGPT, Claude, Llama.

    advanced14 lessons~4h

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Machine Learning Engineer