Machine Learning

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practitioner

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• Split data into train, validation, and test sets without leakage, preserving structure and time order
• Identify whether a business question needs classification, regression, or clustering
• Run exploratory data analysis to read distributions, correlations, and anomalies
• Clean data and engineer features: handle missing values, outliers, scaling, and encoding in a pipeline
• Map each problem type to the right scikit-learn family (linear models, trees, SVMs, ensembles)
• Select a model systematically: baseline first, validate with cross-validation, and decide when to ship

• Url: https://aiacademy.anthropos.work/chapters/data-splitting-train-validation-test/ · Data Splitting — Train / Validation / Test the Right Way · Position: 1 · 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.
• Url: https://aiacademy.anthropos.work/chapters/best-practices-model-selection/ · Best Practices for Model Selection — Match Algorithm to Data + Objective · Position: 2 · 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.
• Url: https://aiacademy.anthropos.work/chapters/machine-learning-intro/ · Machine Learning: Start Here · Position: 3 · 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.
• Url: https://aiacademy.anthropos.work/chapters/algorithm-overview-sklearn-families/ · Algorithm Overview — sklearn Families, When to Reach for Each · Position: 4 · 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.
• Url: https://aiacademy.anthropos.work/chapters/data-cleaning-feature-engineering/ · Data Cleaning & Feature Engineering · Position: 5 · Missing values, outliers, scaling, encoding, and pipeline assembly — turn raw EDA findings into model-ready features.
• Url: https://aiacademy.anthropos.work/chapters/exploratory-data-analysis/ · Exploratory Data Analysis — Distributions, Correlations, Anomalies · Position: 6 · Master the systematic investigation of datasets before modeling — distributions reveal shape, correlations reveal signal, and anomalies reveal what needs fixing.
• Url: https://aiacademy.anthropos.work/chapters/ml-problem-types/ · ML Problem Types — Classification, Regression, Clustering · Position: 7 · Learn to identify whether your business question demands a classifier, a regressor, or a clustering algorithm — the decision that shapes every downstream choice.

• Split data into train, validation, and test sets without leakage, preserving structure and time order
• Identify whether a business question needs classification, regression, or clustering
• Run exploratory data analysis to read distributions, correlations, and anomalies
• Clean data and engineer features: handle missing values, outliers, scaling, and encoding in a pipeline
• Map each problem type to the right scikit-learn family (linear models, trees, SVMs, ensembles)
• Select a model systematically: baseline first, validate with cross-validation, and decide when to ship

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.

Course Mode: online · Course Workload: PT140M · Mode: online