Build models that learn from data. Master machine learning through bite-sized, swipeable learning tiles — designed for busy people who want to learn on the go.
This Machine Learning short course on SkillTiles breaks down everything you need to know into 10 structured milestones, each packed with bite-sized learning tiles you can swipe through in under 15 seconds each.
Whether you're a complete beginner or looking to refresh your knowledge, this online machine learning course uses AI-powered content to adapt to your learning pace. Each tile delivers a focused concept, example, or quiz — making it perfect for commutes, coffee breaks, or quick study sessions.
Covering 70 key topics across 10 milestones, this course takes you from foundations to practical application. Earn badges as you complete each milestone and track your progress on your learning dashboard.
Course Curriculum
1
ML Foundations
Regression, classification, and training
supervised vs unsupervisedlinear regressionlogistic classificationtrain/test splitoverfitting and underfittingcross-validationfeature engineering basics
2
Supervised Learning
Learn from labeled data
training and test setslinear regression deep divelogistic regressiondecision treesrandom forestsgradient boosting (XGBoost)model evaluation metrics
confusion matrixprecision, recall, F1ROC curves and AUCcross-validation strategiesbias-variance tradeoffhyperparameter tuninggrid search vs random search
6
Scikit-Learn Mastery
The Python ML toolkit
pipeline architecturetransformers and estimatorscustom transformerscolumn transformersmodel persistencegrid search CVensemble methods
7
ML for Text
Apply ML to natural language
bag of words modelTF-IDF representationtext classificationsentiment analysis pipelineword embeddings overviewtopic modeling (LDA)text preprocessing pipeline
8
Time Series & Forecasting
Predict future values
time series componentsmoving averagesARIMA modelsseasonal decompositionprophet libraryfeature engineering for timeforecasting evaluation
9
ML Deployment
Put models into production
model serialization (pickle/joblib)REST API for ML modelsDocker for MLmodel monitoringA/B testing ML modelsMLflow basicsML pipeline automation
10
ML Ethics & Fairness
Build responsible ML systems
bias in ML datasetsfairness metricsexplainable AI (XAI)SHAP and LIMEdata privacy considerationsresponsible AI guidelinesAI regulation overview