Machine Learning From Scratch: A Beginner-Friendly Guide for Software Engineers
I finally turned my Machine Learning notes into an actual eBook.
When I started learning ML more seriously, I noticed something:
A lot of resources either start too theoretically or jump straight into libraries and code.
As a software engineer, I wanted something that built the concepts step by step before throwing a bunch of libraries and algorithms at the reader.
So I put together Machine Learning From Scratch — Part One: Foundations & Classical ML.
What's inside?
The eBook contains 9 lessons covering the foundations of Machine Learning and some of the most important classical ML algorithms.
Foundations
- What Is Machine Learning?
- Three Types of Machine Learning
- The Math Actually Needed for ML
- Data — Features, Labels & Train/Test Splits
Classical ML
- Linear Regression
- Logistic Regression — Predicting Categories
- Model Evaluation — ROC, AUC & Cross-Validation
- Decision Trees, Random Forests & Ensemble Learning
- Gradient Boosting — XGBoost & LightGBM
The lessons are designed to build on each other rather than treating each algorithm as an isolated topic.
Who is it for?
I wrote this primarily for:
- Software engineers getting into Machine Learning
- Developers transitioning toward AI/ML
- Beginners who find traditional ML resources too theoretical
- AI developers who want to strengthen their fundamentals
- Anyone who wants a structured introduction before diving deeper into ML
The goal isn't to replace a full ML course or textbook.
It's to give you a clear path through the fundamentals without making the starting point unnecessarily complicated.
It's completely free
I've made Part One completely free to download.
You can get it here:
📘 Machine Learning From Scratch — Part One
The download is available through the FactsByte eBook library.
I'd genuinely love to hear your feedback, especially from developers who are learning ML or have recently gone through the same transition.
Which Machine Learning concept gave you the most trouble when you were starting?
Top comments (0)