Build Your Own X Machine Learning: A Python Tutorial Collection Without Releases
Build your own X - Master machine learning by building everything from scratch. It aims to cover everything from linear regression to deep learning to large language models (LLMs).
At a glance
- What is it?
- Amit Shekhar's repository collects single-file Python reimplementations of classic ML algorithms, from linear regression to activation functions. It is a study aid tied to Outcome School, not a library, and the README is the only documentation you get.
- Who is it for?
- Adopt this repository if you learn by reading short, self-contained Python files and want to see linear regression, KNN, or a decision tree written out without scikit-learn doing the work. Do not adopt it if you need a supported library, versioned releases, or anything with a test suite.
- Can I use it commercially?
- Yes. Apache-2.0 is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 74 days ago.
- What is it written in?
- Mainly Python, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
What Problem the Repository Solves, and for Whom
Most people meet machine learning through an import statement. You call LinearRegression().fit(X, y) and a number comes out. The algorithm stays a black box. This repository exists to remove the box. Its README states the goal directly: master machine learning by building everything from scratch, covering linear regression through deep learning to large language models. Each topic is a separate Python file under a tutorials/ directory, and the table of contents groups them into nine areas: core machine learning algorithms, neural networks and deep learning, recommendation systems, computer vision applications, natural language processing, time series and forecasting, anomaly detection, sentiment and text analysis, and miscellaneous applications. The intended reader is someone preparing for interviews, working through a course, or trying to understand what an optimizer actually updates. It is maintained by Amit Shekhar, identified in the README as the founder of Outcome School, and the README links to that site's AI and machine learning program. That link is the honest framing: the repo functions as a companion to a course, not as a standalone product. If you want a general-purpose ML toolkit, this is the wrong place. If you want to read how gradient boosting or PCA is written when nobody hands you a library, the structure is aimed squarely at you.
One File per Algorithm, and What That Layout Implies
The architecture is flat and predictable. Every tutorial is a single .py file at a path that names its topic, for example tutorials/core-machine-learning-algorithms/linear-regression/linear_regression.py or tutorials/core-machine-learning-algorithms/knn/knn.py. There is no package, no shared module, no __init__.py wiring the pieces together. That choice has consequences worth stating plainly. On the positive side, you can read any file in isolation, and you never have to trace an abstraction across five imports to find the update rule. On the negative side, there is no reuse between tutorials. If linear regression and ridge regression both need a gradient step, the repository's layout suggests each file carries its own copy rather than importing a common helper. For a learner this is arguably correct, because duplication keeps each example self-contained. For anyone hoping to lift code into a project, it means the repository is a set of references, not a codebase. The breadth listed in the README is wide: cost functions (MSE, MAE), activation functions (sigmoid, tanh, ReLU, LeakyReLU, softmax), optimizers (SGD, Adam, RMSprop, Adagrad), clustering (K-Means, mean-shift, spectral), dimensionality reduction (PCA, LDA, independent component analysis, factor analysis), and regression variants including ridge, lasso, ElasticNet, polynomial, and Bayesian. Breadth at this level, delivered one file at a time, tells you the project is organized around reading, not around running a pipeline end to end.
Getting the Tutorials onto Your Machine
The README does not document an install procedure, a package name, or a requirements file. What it gives you is a set of direct links to Python files on GitHub. The practical route is to clone the repository and open the file for the topic you want. The commands below follow the repository's own naming, and the default branch is main. git clone https://github.com/amitshekhariitbhu/build-your-own-x-machine-learning.git then cd build-your-own-x-machine-learning. From there, the relevant entry points are paths like tutorials/core-machine-learning-algorithms/gradient-descent/gradient_descent.py or tutorials/core-machine-learning-algorithms/optimizers/optimizers.py. Run one with python tutorials/core-machine-learning-algorithms/optimizers/optimizers.py. Because no dependency manifest appears in the supplied material, I cannot tell you which third-party packages, if any, the files import. NumPy is the common assumption for this kind of code, but the README does not say so, and I have not run the files to find out. Treat the first execution of any tutorial as a discovery step: read the imports at the top of the file before you run it. There is also no configuration surface to speak of. No config keys, no environment variables, no CLI flags are described. Each file is meant to be read and executed as a script, and any parameters live inside the code itself. That is a deliberate simplicity, and it also means there is nothing to tune from the outside.
No Releases, No Tests, No Version Contract
The repository has no published releases. That single fact shapes how you should treat it. Without versioned tags, there is no stable snapshot to pin, no changelog to read, and no way to know whether a file you studied six months ago still looks the same. The README acknowledges ongoing change with the note that new tutorials will keep being added. For a study resource this is tolerable. For anything resembling a dependency, it is disqualifying. The absence of a test suite compounds it. In a repository whose entire purpose is reimplementing algorithms, tests are the natural way to demonstrate that the from-scratch version matches a known result. Nothing in the supplied material indicates such tests exist. That does not mean the code is wrong. It means correctness is on you to check, and the honest way to check is to compare a from-scratch implementation against a reference library on the same data. The Apache-2.0 licence is permissive and includes an explicit patent grant, so copying a snippet into your own work is straightforward from a licensing standpoint. The usual caveat applies: keep the licence notice and attribution, and if the terms matter to your organization, read the LICENSE file rather than a summary. I am not giving legal advice here, only pointing at where the terms live. The maintenance cost is likewise hard to estimate from the outside. There are no releases to upgrade between, so the cost is not migration but re-reading: if a tutorial changes, your notes go stale silently.
Where This Repository Is the Wrong Tool
Three situations make it a poor fit. The first is production use. Nothing here is packaged, versioned, or tested, so adopting it as a dependency means vendoring code you will maintain yourself. The second is performance work. A from-scratch Python implementation of K-Nearest Neighbors or gradient boosting will not compete with a compiled library, and the repository does not claim otherwise. The third is anyone who needs a documented API. There is no API reference, no docstrings guarantee, no input validation contract. If your goal is to solve a problem rather than to understand one, the from-scratch approach adds work without adding capability. There is also a subtler limitation in the format itself. A single file per algorithm can only show a small slice of the algorithm. Real implementations handle numerical stability, sparse inputs, missing values, and convergence edge cases. A tutorial file optimizes for readability, which usually means it omits exactly those details. That is a reasonable trade for teaching and a real gap for engineering. Read the tutorial, then read a production implementation of the same algorithm, and the difference between the two will teach you more than either alone. The README's own framing supports this reading: the goal is mastery through building, not deployment.
How It Differs from scikit-learn and from a Textbook
scikit-learn is the obvious alternative, and the difference is not quality but purpose. scikit-learn gives you a consistent estimator interface: construct an object, call fit, call predict, and the same three lines work for a hundred algorithms. It is tested, versioned, documented, and optimized. What it does not give you is the arithmetic. You cannot see the gradient update by reading the call site. This repository inverts that trade. It has no interface to speak of, no consistency guarantee across files, and no tests, but every line of the algorithm is visible and the file is short enough to finish in one sitting. The other alternative is a textbook or a course, which offers narrative and derivation but not runnable code. This repository sits between the two: more concrete than a chapter, less complete than a library. The README's link to the Outcome School AI and machine learning program makes the positioning explicit. The repository is the practice material for a taught course, and it is most useful to someone who already has the conceptual explanation and wants to see the code. If you arrive without that context, a from-scratch decision tree file will show you what the code does without telling you why the split criterion is defined that way.
Who Should Clone This, and What to Check First
Clone it if you are preparing for interviews that ask you to derive or implement an algorithm, if you are taking a course and want runnable counterparts to the lectures, or if you simply want to read how Adam differs from RMSprop in code rather than in prose. The optimizers tutorial and the activation functions tutorial are the two entries that most directly reward that kind of reading, because both compare several variants side by side in one file. Do not clone it expecting a library, a benchmark suite, or a maintained dependency, and do not treat the absence of releases as something that will be fixed later; the README describes an ongoing series of tutorials, not a product roadmap. The first thing to verify is the import block at the top of whichever file you pick, since no dependency manifest is provided and I cannot confirm from the supplied material which packages the tutorials rely on. The second is whether the depth matches your level: open tutorials/core-machine-learning-algorithms/linear-regression/linear_regression.py and read it end to end before cloning the rest. If that file teaches you something, the other forty-odd entries in the README's table of contents are likely to as well. If it does not, the format is the problem, and a library or a textbook will serve you better.
Editorial conclusion
Adopt this repository if you learn by reading short, self-contained Python files and want to see linear regression, KNN, or a decision tree written out without scikit-learn doing the work. Do not adopt it if you need a supported library, versioned releases, or anything with a test suite. Before committing study time, open one file such as tutorials/core-machine-learning-algorithms/linear-regression/linear_regression.py and check whether its style and depth match how you learn, because the README is a link index and will not tell you.
Community notes