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Machine Learning

Discover how machines learn, adapt, and make intelligent predictions.

Computer Science Courses

54 lessons / quizzes

Topics

Exam‑style questions with instant feedback.

Section 1: Foundations

1: What is Machine Learning

2: Supervised vs. Unsupervised vs. Reinforcement Learning

3: Key Terminology: features, labels, models, training, inference

4: The Machine Learning Workflow

5: Real-world Applications and Examples

Section 2: Math Prerequisites

1: Linear Algebra: vectors, matrices, operations

2: Calculus: derivatives, gradients, partial derivatives, chain rule

3: Probability & Statistics: probability distributions, Bayes' theorem, mean, variance, standard deviation

4: Optimization Concepts: cost functions, convexity, local/global minima

Section 3: Data Preprocessing

1: Data Collection and Sourcing

2: Handling Missing Data

3: Categorical Data Encoding: one-hot, label encoding

4: Feature Scaling: normalization, standardization

5: Train-Validation-Test Split

6: Introduction to Data Leakage

Section 4: Linear Models

1: Linear Regression: hypothesis, cost function, gradient descent

2: Regularization: Ridge (L2) and Lasso (L1) Regression

3: Logistic Regression for Classification

4: The Concept of Decision Boundaries

Section 5: Beyond Linearity

1: Polynomial Regression

2: Support Vector Machines (SVM): linear and kernel trick

3: k-Nearest Neighbors (k-NN)

4: Naive Bayes Classifier

5: Introduction to Non-Parametric Models

Section 6: Tree-Based Models

1: Decision Trees: nodes, splitting criteria (Gini, Entropy), pruning

2: Ensemble Methods: bagging, boosting, stacking

3: Random Forests

4: Gradient Boosted Machines (e.g., XGBoost, LightGBM)

Section 7: Model Evaluation

1: Evaluation Metrics: accuracy, precision, recall, F1-score, ROC-AUC

2: Confusion Matrix

3: Bias-Variance Tradeoff

4: Overfitting and Underfitting

5: Cross-Validation: k-fold, stratified

Section 8: Unsupervised Learning

1: Clustering: k-Means, hierarchical clustering

2: Dimensionality Reduction: PCA, t-SNE

3: Anomaly Detection

4: Association Rule Learning

Section 9: Introduction to Neural Networks

1: Biological Inspiration

2: Perceptron and Activation Functions

3: Multi-Layer Perceptrons (MLP)

4: Forward Propagation and Backpropagation

5: Introduction to Deep Learning

Section 10: Deep Learning Fundamentals

1: Convolutional Neural Networks (CNNs) for images

2: Recurrent Neural Networks (RNNs) for sequences

3: Transformers and Attention Mechanisms

4: Common Architectures and Frameworks (TensorFlow/PyTorch)

Section 11: Specialized Topics

1: Natural Language Processing (NLP): tokenization, embeddings, transformers

2: Reinforcement Learning: agents, environments, rewards, Q-learning

3: Recommender Systems: collaborative and content-based filtering

4: Ethical AI: fairness, bias, interpretability

Section 12: ML Systems & Deployment

1: Introduction to MLOps

2: Model Deployment Strategies

3: Monitoring and Maintenance

4: Building End-to-End ML Pipelines

What you’ll achieve

Learn the core principles of supervised, unsupervised, and reinforcement learning.

Apply machine learning algorithms to real-world datasets.

Gain hands-on experience with popular libraries like scikit-learn and TensorFlow.

Understand key concepts like overfitting, bias-variance trade-off, and model evaluation.

Prepare for careers in AI, data science, and advanced computing fields.

Course illustration

Course overview

Everything you need to excel in Machine Learning

Machine Learning explores algorithms and techniques that enable computers to learn patterns from data and make decisions without being explicitly programmed. Students will study supervised, unsupervised, and reinforcement learning, along with model evaluation, overfitting, and optimisation methods. The course blends theory with practical applications, giving learners experience with libraries such as scikit-learn or TensorFlow. By the end, students will understand how to design, train, and evaluate models for tasks such as classification, regression, clustering, and recommendation systems.

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