AI & Machine LearningIntermediateFREE

Machine Learning Fundamentals

Learn how machines learn from data. Covers the core supervised and unsupervised algorithms, model evaluation, overfitting, and ensemble methods. By the end you will be able to train, evaluate, and improve your own ML models using scikit-learn.

12 hours

12 lessons

Certificate included

Instructor

JP Instructor

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This course includes:

12 hours of comprehensive lessons
Hands-on coding exercises and challenges
Code examples with syntax highlighting
Certificate of completion
Lifetime access to all content
Mobile-friendly, works offline

Course Curriculum

Module 1: Supervised Learning

Learn how models learn from labelled examples to make predictions.

3 lessons

What is Supervised Learning?

Preview

12 minutes

Preview Lesson

Linear Regression

15 minutes

Classification and Logistic Regression

12 minutes

Module 2: Unsupervised Learning

Discover patterns in data without labels.

3 lessons

What is Unsupervised Learning?

10 minutes

K-Means Clustering

14 minutes

Dimensionality Reduction with PCA

12 minutes

Module 3: Model Evaluation

Know whether your model actually works — and why it might fail.

3 lessons

Train/Test Split and Cross-Validation

12 minutes

Classification Metrics

15 minutes

Overfitting and Regularisation

14 minutes

Module 4: Decision Trees and Ensemble Methods

From single trees to powerful ensembles — the algorithms behind most Kaggle winners.

3 lessons

Decision Trees

12 minutes

Random Forests

14 minutes

Gradient Boosting

15 minutes

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