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Complete Machine Learning Course — From Foundations to Production [2026]

Master Machine Learning with Python and Scikit-learn. Learn data wrangling, EDA, preprocessing, regression, classification, ensemble methods, model evaluation, unsupervised learning, feature engineering, ML pipelines, and production deployment — with hands-on projects in every module.

12
Modules
35
Hours
100
Topics
12
Projects
🎯

Who This Course Is For

Built for data scientists and engineers who want to build, train, and deploy their own models. Covers scikit-learn to neural networks with real datasets.

📋

Prerequisites

Python proficiency, linear algebra basics, and statistics fundamentals.

First published June 2024 · Updated 2026

What You'll Learn

  • ML fundamentals: supervised, unsupervised, and reinforcement learning
  • Data wrangling with NumPy and Pandas
  • Exploratory data analysis and preprocessing
  • Regression and classification algorithms
  • Ensemble methods: Random Forest, XGBoost, LightGBM
  • Model evaluation, validation, and selection
  • Feature engineering and selection techniques
  • Production ML deployment with FastAPI and MLOps

Career Opportunities

Machine Learning Engineer
Data Scientist
ML Operations Engineer
Data Analyst
AI Engineer
Research Scientist
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Course Modules Overview

1
ML Foundations & the Data Ecosystem

9 topics

2
Data Wrangling with NumPy & Pandas

9 topics

3
Exploratory Data Analysis (EDA)

8 topics

4
Data Preprocessing & Feature Scaling

8 topics

5
Supervised Learning — Regression

8 topics

6
Supervised Learning — Classification

8 topics

7
Ensemble Methods & Gradient Boosting

8 topics

8
Model Evaluation & Validation

8 topics

9
Unsupervised Learning

8 topics

10
Feature Engineering & Selection

8 topics

11
ML Pipelines & Hyperparameter Tuning

8 topics

12
ML Deployment & Production Systems

8 topics

Keep Learning!

Complete all 12 modules to unlock your course completion certificate

Course Curriculum

12 comprehensive modules covering everything from basics to advanced topics

Beginner (4)Intermediate (4)Advanced (4)
Module 1

ML Foundations & the Data Ecosystem

Build a solid ML foundation: paradigms, workflow, bias-variance tradeoff, scikit-learn API, and build your first classifier.

Beginner2.5 hours
What is Machine Learning?Types of Machine LearningThe ML Workflow — From Problem to PredictionBias-Variance Tradeoff — The Core ML DilemmaData Types & DatasetsPython ML Environment SetupScikit-learn API — The Universal InterfaceTrain-Test Split & Avoiding Data LeakageMini Project: Your First ML Model — Iris Classifier
Start Module
Module 2

Data Wrangling with NumPy & Pandas

Master data manipulation: NumPy arrays, Pandas DataFrames, data cleaning, missing values, outliers, and end-to-end data wrangling.

Beginner3 hours
NumPy Arrays — Creation, Indexing & BroadcastingNumPy for ML — Matrix Operations & StatisticsPandas Series & DataFrames — Core OperationsReading and Writing Data with PandasCleaning Dirty Data — Real-World MessinessHandling Missing Values — Strategies & ImputationPandas GroupBy, Merge & ReshapeDetecting & Handling OutliersMini Project: Cleaning a Real-World Messy Dataset
Start Module
Module 3

Exploratory Data Analysis (EDA)

Master exploratory data analysis: distributions, correlations, pair plots, class imbalance, categorical features, and build a complete EDA report.

Beginner2.5 hours
EDA Overview — Why Explore Before ModelingDistribution Analysis — Histograms and Density PlotsCorrelation Analysis — Feature RelationshipsPair Plots & Scatter AnalysisClass Imbalance Detection & VisualizationCategorical Feature AnalysisTime-Series & Datetime EDAMini Project: EDA Report on a Real Dataset
Start Module
Module 4

Data Preprocessing & Feature Scaling

Master data preprocessing: feature scaling, encoding, ColumnTransformer, pipelines, transformations, SMOTE, and build a production pipeline.

Beginner3 hours
Feature Scaling — Why and WhenCategorical Encoding — OneHot, Ordinal, TargetColumnTransformer — Apply Different Preprocessing per ColumnBuilding a Full Preprocessing PipelineFeature Scaling Impact on Different AlgorithmsFeature Transformations — Log, Power, QuantileHandling Imbalanced Data — SMOTE & Class WeightsMini Project: Production-Ready Preprocessing Pipeline
Start Module
Module 5

Supervised Learning — Regression

Master regression: linear, polynomial, Ridge/Lasso regularization, regression metrics, feature importance, and build a house price predictor.

Intermediate3 hours
Linear Regression — How It WorksResidual Analysis — Diagnosing Linear RegressionPolynomial Regression & Non-linearityRidge (L2) and Lasso (L1) RegularizationRegression Metrics — MAE, MSE, RMSE, R², MAPEMultiple Regression & Feature ImportanceComparing Regression AlgorithmsMini Project: House Price Prediction
Start Module
Module 6

Supervised Learning — Classification

Master classification: logistic regression, decision trees, KNN, SVM, Naive Bayes, decision boundaries, and build a Titanic survival predictor.

Intermediate3 hours
Logistic Regression — Classification with ProbabilitiesDecision Trees — Interpretable Non-linear ClassificationK-Nearest Neighbors (KNN)Support Vector Machines (SVM)Naive Bayes — Probabilistic ClassificationDecision Boundaries — Visualizing ClassifiersAlgorithm Selection Cheat SheetMini Project: Titanic Survival Classifier
Start Module
Module 7

Ensemble Methods & Gradient Boosting

Master ensembles: Random Forest, gradient boosting, XGBoost, LightGBM, stacking, SHAP explanations, and predict customer churn.

Intermediate3 hours
Ensemble Learning — Wisdom of Crowds in MLRandom Forest — Bagging at ScaleGradient Boosting — Sequential Error CorrectionXGBoost & LightGBM — Production Gradient BoostingHyperparameter Tuning for EnsemblesModel Stacking — Meta-LearningSHAP Values — Explaining Ensemble PredictionsMini Project: Customer Churn Prediction
Start Module
Module 8

Model Evaluation & Validation

Master model evaluation: confusion matrix, ROC/AUC, cross-validation, learning curves, calibration, and build an evaluation dashboard.

Intermediate3 hours
Confusion Matrix — Beyond AccuracyROC Curve & AUC — Threshold-Independent EvaluationCross-Validation — Reliable Performance EstimationLearning Curves — Diagnosing Overfitting & UnderfittingModel Calibration — Are Probabilities Trustworthy?Evaluation for Regression — Beyond R²Model Selection FrameworkMini Project: Building a Model Evaluation Dashboard
Start Module
Module 9

Unsupervised Learning

Master unsupervised learning: K-Means, DBSCAN, PCA, t-SNE, hierarchical clustering, anomaly detection, GMMs, and segment customers.

Advanced3 hours
K-Means Clustering — Grouping Without LabelsDBSCAN — Density-Based ClusteringPCA — Dimensionality Reductiont-SNE & UMAP — High-Dimensional VisualizationHierarchical Clustering — DendrogramsAnomaly Detection — Isolation Forest & LOFGaussian Mixture Models — Soft ClusteringMini Project: Customer Segmentation
Start Module
Module 10

Feature Engineering & Selection

Master feature engineering: interaction features, target encoding, time-series features, feature selection, SHAP, and build a credit risk pipeline.

Advanced3 hours
Why Feature Engineering MattersInteraction Features, Binning & Polynomial TermsTarget Encoding & High-Cardinality CategoricalsTime-Series Feature Engineering for MLStatistical Feature Selection MethodsSHAP-Based Feature SelectionHandling Skewed Targets in RegressionMini Project: Feature Engineering for Credit Risk
Start Module
Module 11

ML Pipelines & Hyperparameter Tuning

Master ML pipelines: custom transformers, GridSearch, RandomizedSearch, Optuna, nested CV, experiment tracking, and build an AutoML pipeline.

Advanced3 hours
Advanced Pipeline ArchitectureCustom Transformers — Extending SklearnGridSearchCV — Exhaustive Hyperparameter SearchRandomizedSearchCV — Smarter Search for Large SpacesOptuna — Bayesian Hyperparameter OptimizationNested Cross-Validation — Honest Hyperparameter EvaluationPipeline Versioning & Experiment TrackingMini Project: Complete AutoML-Style Pipeline
Start Module
Module 12

ML Deployment & Production Systems

Deploy ML to production: model serialization, FastAPI serving, drift detection, monitoring, batch inference, A/B testing, and build a full production system.

Advanced3 hours
Model Serialization & VersioningFastAPI — Serving ML Models as REST APIsData Drift Detection — Is My Model Still Valid?Model Monitoring DashboardBatch Inference & Scheduled ScoringA/B Testing — Evaluating Models in ProductionRetraining Strategies & MLOpsCapstone: End-to-End Production ML System
Start Module

Your Learning Roadmap

Follow this structured path — from first concepts to production-ready mastery

🌱
Step 01 · Beginner

ML foundations, data wrangling, EDA, and preprocessing pipelines

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Step 02 · Intermediate

Regression, classification, ensembles, and model evaluation

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Step 03 · Advanced

Unsupervised learning, feature engineering, pipelines, and production deployment

Tools & Technologies

Essential tools you'll master during this course

Scikit-learn

Industry-standard ML library for Python

ML Framework

Pandas

Data analysis and manipulation tool

Data Manipulation

NumPy

Array computing and linear algebra

Numerical Computing

XGBoost

Optimized gradient boosting library

Gradient Boosting

Optuna

Bayesian optimization framework

Hyperparameter Tuning

FastAPI

High-performance API framework

Model Serving

Ready to Start Learning?

Begin your journey with Module 1 and build your skills step by step. Completely free, no registration required.

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