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Core ML: Algorithms from Scratch

Build every classic ML algorithm by hand, then verify against scikit-learn

4 phases. 18 lessons. 18 labs. 1 capstone. The classical ML algorithms every data scientist uses — linear regression, logistic regression, SVM, decision trees, random forests, KNN, K-Means, DBSCAN, Naive Bayes, boosting, and ensemble methods — every one implemented from scratch in pure Python, then benchmarked against scikit-learn to prove they work. You build a complete ML library and graduate understanding what scikit-learn does under the hood.

4 phases · 18 lessons · 18 labs · 1 capstone

Take Math for AI & ML first — this course builds on it.

Outcomes you will have by the end

• A complete ML library — A tested, documented Python package implementing every core ML algorithm from scratch — linear models, trees, forests, SVM, KNN, clustering, Naive Bayes, boosting — with a unified API, benchmarked against scikit-learn.
• A working mental model of what scikit-learn does under the hood — When you call sklearn.fit(), you'll know exactly what algorithm runs, what loss it optimizes, and what the parameters control — because you built the same thing yourself.
• Benchmarked implementations that prove your code works — Every algorithm is tested against scikit-learn on real datasets — if your from-scratch implementation matches sklearn's output, you know it's correct.
• Verified certificate of completion — Issued by DeVenture Academy, tied to your completion record. Lists the supervised, unsupervised, ensemble, and pipeline skills you built.

What you will be able to do

Linear & Logistic Regression from Scratch · SVM & Kernel Methods · Decision Trees & Random Forests · K-Means & DBSCAN Clustering · Naive Bayes Classifiers · Boosting & Ensemble Methods · Model Evaluation & Cross-Validation · Feature Engineering & Selection

Every phase, every lesson, every project

• ML Foundations (2 lessons) — free — What is ML, the learning lifecycle, and linear regression from scratch.
  • What Is Machine Learning
  • Linear Regression
• Classification Algorithms (5 lessons) — free — Logistic regression, decision trees, SVMs, KNN, and naive Bayes — all from scratch.
  • Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines
  • K-Nearest Neighbors and Distances
  • Naive Bayes
• Ensembles & Evaluation (5 lessons) — Unsupervised learning, feature engineering, model evaluation, bias-variance, and ensemble methods.
  • Unsupervised Learning
  • Feature Engineering & Selection
  • Model Evaluation
  • Bias-Variance Tradeoff
  • Ensemble Methods
• Production ML Practices (6 lessons) — Hyperparameter tuning, ML pipelines, time series, anomaly detection, imbalanced data, and feature selection.
  • Hyperparameter Tuning
  • ML Pipelines
  • Time Series Fundamentals
  • Anomaly Detection
  • Handling Imbalanced Data
  • Feature Selection

The technologies you will use

NumPy · scikit-learn · pandas · Matplotlib · SciPy

Roles this course prepares you for

• ML Engineer ($110k–$170k) — Build, train, and deploy ML models for real business problems. Understanding the algorithms from scratch means you can debug when scikit-learn produces unexpected results and extend algorithms for custom requirements.
• Applied ML Engineer ($115k–$170k) — Apply ML to specific business problems — recommendation systems, fraud detection, content moderation. Knowing the algorithms from scratch means you can choose the right one and tune it intelligently.
• Data Scientist ($100k–$160k) — Analyze data, build models, and communicate insights. The from-scratch understanding lets you explain model behavior to stakeholders instead of treating the model as a black box.

What core ML from scratch actually is

It's the practice of implementing every classical ML algorithm — linear regression, logistic regression, SVM, decision trees, random forests, KNN, K-Means, Naive Bayes, boosting — in pure Python, without using scikit-learn's implementations. You build the algorithms, test them on real data, and benchmark against scikit-learn to prove they work.

What you do every day

You build ML models for real problems, but you understand every algorithm at the implementation level. When a model underperforms, you know whether it's the algorithm, the data, or the hyperparameters — because you built the algorithm yourself. When you need a custom variant, you can modify the implementation instead of waiting for scikit-learn to add it.

Why companies hire for this

Every company uses scikit-learn, but most engineers treat it as a black box. The engineer who can implement the algorithms from scratch is the one who can debug production ML issues, tune models intelligently, and build custom solutions when off-the-shelf libraries don't fit. That's the difference between an ML practitioner and an ML engineer.

What this course is not

It is not a theory course. Every algorithm is implemented in code and tested on real data. It is not a scikit-learn tutorial — you'll use scikit-learn to verify your from-scratch implementations, not to replace them. And it is not a deep learning course — that's the next course in the chain.

Common questions

Do I need Math for AI & ML first?

Yes — this course uses linear algebra (matrix operations), calculus (gradient descent), and probability (Naive Bayes, distributions) throughout. Math for AI & ML builds those foundations; this course applies them to build real ML algorithms.

Why implement algorithms from scratch when scikit-learn exists?

Because the gap between someone who calls fit() and someone who can debug, tune, and extend the algorithm is the gap between junior and senior. When scikit-learn produces unexpected results, the engineer who built the algorithm from scratch knows why. When a custom requirement doesn't fit any library, the engineer who built from scratch can modify the algorithm.

How is this different from the ML phase in ML & AI Engineering?

ML & AI Engineering covers ML in 9 lessons as part of a broader curriculum — enough to use scikit-learn effectively. This course spends 18 lessons implementing every algorithm from scratch, building a complete library, and benchmarking against scikit-learn. It's the difference between using a tool and understanding the tool.

What do I end up with?

A complete, tested Python ML library with every core algorithm implemented from scratch and benchmarked against scikit-learn. Plus the ability to read any ML algorithm's source code and understand what it does, why it works, and how to modify it.

Continue your learning path

Math for AI & ML · Deep Learning from Scratch · ML & AI Engineering · Reinforcement Learning

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