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Machine Learning Short Course

Learn machine learning to build intelligent models that find patterns in data and make predictions automatically. Machine learning is the technology behind recommendation systems, fraud detection, self-driving cars, and medical diagnosis. This course covers supervised versus unsupervised learning, linear regression, classification, model training, cross-validation, and feature engineering using Python and scikit-learn.. Master machine learning through bite-sized, swipeable learning tiles — designed for busy people who want to learn on the go.

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About This Machine Learning Course

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
3

Unsupervised Learning

Find patterns without labels

k-means clusteringhierarchical clusteringPCA dimensionality reductionDBSCANanomaly detectionassociation rulest-SNE visualization
4

Feature Engineering

Prepare data for better models

handling missing dataencoding categorical variablesfeature scaling and normalizationfeature selection techniquespolynomial featurestext feature extractionfeature importance analysis
5

Model Evaluation

Measure and compare models

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

What You'll Learn in This Machine Learning Course

supervised vs unsupervised
linear regression
logistic classification
train/test split
overfitting and underfitting
cross-validation
feature engineering basics
training and test sets
linear regression deep dive
logistic regression
decision trees
random forests
gradient boosting (XGBoost)
model evaluation metrics

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Ready to master Machine Learning?

Start this machine learning short course today. Swipe through AI-powered tiles, earn badges, and build a daily learning habit — no experience needed.

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