🤖Core AI
Machine Learning
Algorithms, models and deployment
Comprehensive ML course covering supervised and unsupervised learning, model evaluation, feature engineering, and deploying ML models to production using scikit-learn and Python.
50 lessons
25 hrs
Intermediate
What you will learn
Build real-world projects from scratch
Write clean, production-ready code
Understand core concepts deeply with diagrams
Follow industry best practices
Get hands-on with code in every lesson
Access lifetime updates as the tech evolves
Curriculum
Module 1What is Machine Learning4 lessons
Module 2Mathematics for ML3 lessons
Linear Algebra for ML22 min
Statistics for ML20 min
Probability and Calculus Intuition20 min
Module 3NumPy Deep Dive for ML3 lessons
Arrays, Shapes and Indexing20 min
Broadcasting and Linear Algebra with NumPy18 min
Random Numbers and Reproducibility12 min
Module 4Pandas Deep Dive for ML3 lessons
DataFrames and Series18 min
Data Cleaning with Pandas20 min
GroupBy, Merge and Aggregate18 min
Module 5Exploratory Data Analysis4 lessons
Understanding Your Data Before Modeling18 min
Univariate Analysis20 min
Bivariate Analysis and Correlation18 min
Complete EDA Template22 min
Module 6Handling Missing Data and Outliers4 lessons
Types of Missing Data and Detection18 min
Simple Imputation Strategies16 min
Advanced Imputation — KNN and Iterative Imputer18 min
Outlier Detection and Handling20 min
Module 7Feature Encoding5 lessons
What is Encoding and Why ML Needs Numbers14 min
Label Encoding and Ordinal Encoding16 min
One Hot Encoding18 min
Target Encoding and Binary Encoding18 min
Handling Unseen Categories at Inference14 min
Module 8Feature Scaling4 lessons
Why Scaling Matters and When It Does Not16 min
StandardScaler — Z-Score Normalization14 min
MinMaxScaler and RobustScaler16 min
Normalizer, MaxAbsScaler and Choosing the Right Scaler16 min
Module 9Feature Selection5 lessons
Why Feature Selection Matters14 min
Filter Methods20 min
Wrapper Methods — RFE and RFECV18 min
Embedded Methods — Lasso and Tree Importance18 min
Avoiding Data Leakage in Feature Selection14 min
Module 10Feature Creation and Transformation5 lessons
Interaction Features and Polynomial Features18 min
Mathematical Transformations16 min
Date and Time Feature Engineering16 min
Binning and Discretization14 min
Train Test Split with Stratification14 min
Module 11Linear Regression4 lessons
What is Linear Regression16 min
How Linear Regression Learns — Cost Function and Gradient Descent18 min
Training and Evaluating Linear Regression18 min
Ridge and Lasso Regularization20 min
Module 12Logistic Regression5 lessons
What is Logistic Regression16 min
How Logistic Regression Learns — Binary Cross-Entropy16 min
Training Logistic Regression with sklearn18 min
Classification Evaluation Metrics20 min
Decision Threshold Tuning16 min
Module 13Decision Trees5 lessons
What is a Decision Tree14 min
How a Decision Tree Learns — Gini Impurity and Information Gain18 min
Training Decision Trees with sklearn18 min
Overfitting and Pruning — Controlling Tree Growth18 min
Decision Tree Strengths, Weaknesses and When to Use Them12 min
Module 14Ensemble Methods — Bagging and Random Forest5 lessons
What is Ensemble Learning14 min
Bagging — Bootstrap Aggregating16 min
Random Forest — Bagging Plus Feature Randomness18 min
Training Random Forest with sklearn20 min
Hyperparameter Tuning and Best Practices18 min
Module 15Ensemble Methods — Boosting (XGBoost, LightGBM, CatBoost)5 lessons
What is Boosting16 min
Gradient Boosting from First Principles18 min
XGBoost — Extreme Gradient Boosting22 min
LightGBM — Light Gradient Boosting Machine18 min
CatBoost — Categorical Boosting16 min
Module 16Support Vector Machines5 lessons
What is a Support Vector Machine16 min
The Kernel Trick — Handling Non-Linear Data18 min
Soft Margin SVM — The C Parameter16 min
Training SVMs with sklearn18 min
SVM Strengths, Weaknesses and When to Use Them12 min
Module 17Unsupervised Learning — K-Means, DBSCAN, PCA, t-SNE5 lessons
What is Unsupervised Learning12 min
K-Means Clustering20 min
DBSCAN — Density-Based Clustering18 min
PCA — Principal Component Analysis20 min
t-SNE — Non-Linear Dimensionality Reduction for Visualisation16 min
Module 18Evaluation Metrics — Measuring Model Performance Correctly5 lessons
Regression Metrics18 min
Classification Metrics20 min
Ranking and Probability Metrics16 min
Cross Validation Strategies18 min
Choosing the Right Metric for Your Business Problem14 min
Module 19Bias, Variance and Hyperparameter Tuning5 lessons
Bias and Variance — The Core Trade-off18 min
Diagnosing Bias and Variance with Learning Curves16 min
Hyperparameter Tuning — GridSearchCV16 min
RandomizedSearchCV and Bayesian Optimisation18 min
Practical Hyperparameter Tuning Workflow14 min
Module 20Sklearn Pipelines and ColumnTransformer5 lessons
What is a Pipeline and Why You Need One16 min
Building Pipelines — Syntax and Patterns14 min
ColumnTransformer — Different Processing for Different Features20 min
Custom Transformers14 min
Production Pipeline Best Practices14 min
Module 21Handling Real World Challenges5 lessons
Handling Imbalanced Datasets20 min
Handling Missing Data in Production18 min
Handling Outliers16 min
Feature Engineering for Real Data18 min
Data Drift and Distribution Shift14 min
Module 22Model Explainability — SHAP and LIME5 lessons
Why Model Explainability Matters12 min
Feature Importance and Partial Dependence Plots16 min
SHAP — SHapley Additive exPlanations22 min
LIME — Local Interpretable Model-agnostic Explanations14 min
Practical Explainability in Production14 min
Module 23Saving and Serving ML Models5 lessons
Saving Models with joblib and pickle14 min
Model Cards — Documenting Models for Production12 min
Serving Models with FastAPI20 min
MLflow for Experiment Tracking16 min
Production Deployment Patterns14 min
Module 24Monitoring and Retraining ML Models5 lessons
Why Models Degrade in Production12 min
Feature Monitoring — Detecting Input Drift16 min
Prediction Monitoring — Watching Model Outputs14 min
Performance Monitoring with Delayed Labels14 min
Automated Retraining Pipeline18 min
Module 25End to End ML Project — Customer Churn Prediction6 lessons
Lesson 1 — Understanding the Business Problem18 min
Lesson 2 — Data Understanding and Gathering20 min
Lesson 3 — Feature Engineering for Churn18 min
Lesson 4 — Building, Selecting and Evaluating the Model22 min
Lesson 5 — Production Deployment and Monitoring20 min
Lesson 6 — Project Structure, Best Practices and Career Roadmap18 min
115 total lessons across 25 modulesPreview Module 1 free