Foundations of Machine Learning: A Beginner’s Journey

Are you curious about how machines learn, make decisions, and power technologies like self-driving cars, recommendation systems, and chatbots? This course is your friendly introduction to the world of Machine Learning — no prior experience required!

Whether you're a student, a professional, or just someone fascinated by AI, this course will guide you step-by-step through the core ideas behind machine learning. You’ll learn how computers can recognize patterns, make predictions, and even improve themselves over time — all explained in simple, clear language.

We’ll start with the basics and gradually move into more advanced topics like deep learning, neural networks, and reinforcement learning. Along the way, you’ll explore real-world applications, build your own models, and understand the social impact of AI.

By the end of the course, you’ll not only understand how machine learning works — you’ll be able to use it confidently.

Course Flow: Your Journey Through Machine Learning

This course is designed to take you from complete beginner to confident machine learning practitioner — step by step, in a logical and engaging way.

1. Getting Started: What is Machine Learning? We begin with the big picture — what machine learning is, how it works, and why it’s transforming industries. You’ll explore real-world examples and understand the difference between tasks like classification, regression, and clustering.

2. Building the Basics: Linear Models: Next, you’ll learn how machines make predictions using simple models like linear regression. You’ll discover how to train these models, improve them, and evaluate their performance.

3. Making Smarter Decisions: Model Evaluation: Here, we dive into how to test and compare models. You’ll learn about experiments, evaluation metrics, and how to know if your model is actually working well.

4. Preparing Your Data: Data Pre-processing: Before machines can learn, they need clean data. You’ll learn how to handle missing values, outliers, imbalanced classes, and how to transform data for better results.

5. Thinking in Probabilities: Probabilistic Models: You’ll explore how probability helps machines make decisions advanced risk management decision making under uncertainty. Topics include Bayes classifiers, logistic regression, and information theory.

6. Going Deeper: Neural Networks and Deep Learning: Now we enter the world of deep learning. You’ll understand how neural networks work, how they learn through backpropagation, and how they power modern AI systems.

7. Advanced Techniques: Generative Models and Ensembles: You’ll learn how machines can generate new data using GANs and autoencoders, and how combining models (ensembles) can improve accuracy and robustness.

8. Learning Over Time: Sequences and Recurrent Models: Explore how machines handle data that changes over time — like text, speech, or video — using RNNs, LSTMs, and Markov models.

9. Smart Recommendations: Embedding Models: Discover how platforms like Netflix and Amazon recommend content using embedding models, PCA, and graph-based techniques.

10. Learning by Doing: Reinforcement Learning: Finally, you’ll learn how machines can learn by trial and error — like playing games or navigating environments — using reinforcement learning and policy optimization.

11. Thinking Critically: The Social Impact of AI: Throughout the course, we’ll pause to reflect on the ethical and social implications of machine learning — from bias to fairness to responsible AI.

Before you begin the course:

• Lecture annotations and slides are downloadable under the first lecture of each section.

Why take this course?

• Learn by doing: practical examples, hands-on exercises, and real-world projects.