Course · 7 chapters
Machine Learning
Build classical ML models that hold up in production. With scikit-learn, learn data splitting, EDA, feature engineering, and algorithm selection across 7 practitioner chapters.
What you'll be able to do
- Frame problems as classification or regression
- Split data without leakage
- Run exploratory data analysis
- Engineer features in clean pipelines
- Pick the right scikit-learn algorithm
- Validate models with cross-validation
What's inside
- 1Data Splitting — Train / Validation / Test the Right Way
Learn why a three-way split is non-negotiable, how to choose ratios, preserve structure, respect time, and prevent the leakage that makes models look great in notebooks but fail in production.
- 2Best Practices for Model Selection — Match Algorithm to Data + Objective
Learn the systematic process for choosing the right ML algorithm: baseline first, match to data characteristics, validate with cross-validation, and know when to ship.
- 3Machine Learning: Start Here
The orientation map for the Machine Learning path — why these six chapters live together, what you will be able to do after completing them, and where to begin.
- 4Algorithm Overview — sklearn Families, When to Reach for Each
Map each ML problem type to the concrete algorithm families in scikit-learn — linear models, trees, SVMs, ensembles, and more — so you pick the right tool before you write a line of code.
- 5Data Cleaning & Feature Engineering
Missing values, outliers, scaling, encoding, and pipeline assembly — turn raw EDA findings into model-ready features.
- 6Exploratory Data Analysis — Distributions, Correlations, Anomalies
Master the systematic investigation of datasets before modeling — distributions reveal shape, correlations reveal signal, and anomalies reveal what needs fixing.
- 7ML Problem Types — Classification, Regression, Clustering
Learn to identify whether your business question demands a classifier, a regressor, or a clustering algorithm — the decision that shapes every downstream choice.
Frequently asked questions
- What will I learn in this machine learning course?
- You learn the classical ML workflow end to end: data splitting, exploratory data analysis, data cleaning, feature engineering, identifying the problem type, and choosing the right scikit-learn algorithm. The 7 chapters are built around practical, production-minded decisions.
- Who is this course for?
- It is aimed at engineers at a practitioner level who want to build reliable machine learning models with scikit-learn. It suits software or data engineers moving into ML who already write code and want a systematic modeling process.
- Do I need prior experience or to know how to code?
- Yes, this is a practitioner-level path, so you should be comfortable coding in Python and working with data. It assumes you can run code rather than teaching programming from scratch.
- How long is the course and is there a certificate?
- The path has 7 chapters totaling about 140 minutes (roughly 2.3 hours). Completing the chapters in the AI Academy earns a certificate you can share.
- Is this course free?
- No, this is a paid path included with an AI Academy by Anthropos subscription. Once subscribed, you get full access to all 7 chapters.
Earn a certificate
Complete all chapters to receive your certificate of completion.