Curso · 7 capítulos
Machine Learning
Classical machine learning fundamentals — algorithm selection, data preprocessing, exploratory analysis, feature engineering, model selection, and the sklearn algorithm families.
O que você vai saber fazer
- 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.
- 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.
- 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.
- 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.
- Missing values, outliers, scaling, encoding, and pipeline assembly — turn raw EDA findings into model-ready features.
- Master the systematic investigation of datasets before modeling — distributions reveal shape, correlations reveal signal, and anomalies reveal what needs fixing.
O que tem dentro
- 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.
Ganhe um certificado
Conclua todos os capítulos para receber seu certificado de conclusão.