Foundations of Machine Learning: A Beginner’s Journey

Learn basics concepts of ML, deep learning, and AI tools with practical examples and beginner-friendly explanitions.

What you'll learn
- Understand the core concept of machine learning and how it powers modern AI systems.
- Get introduced to neural networks — the building blocks of modern AI, and learn how backpropagation works.
- Dive into deep learning and how it powers things like image recognition and voice assistants.
- Learn the difference between supervised, unsupervised, and reinforcement learning.
- Build models for classification and regression to make predictions from data.
- Explore clustering and density estimation for discovering patterns in unlabeled data.
- Master data preprocessing techniques like handling missing values, normalization, and feature engineering.
- Evaluate models using ROC curves, AUC, and other performance metrics.
- Understand how probability helps machines make smarter guesses, including Bayes classifiers and logistic regression.
- Apply linear models and optimize them using gradient descent.
- Learn about generative models such as GANs and variational autoencoders (VAEs).
- Understand how machines deal with sequences, like predicting the next word in a sentence.
- Implement ensemble methods like bagging, boosting, and random forests.
- Work with sequence models including RNNs and LSTMs for time-series and text.
- Build embedding models for recommender systems and graph-based learning.
- Understand reinforcement learning concepts like policy gradients and Q-learning.
- Reflect on the social and ethical impact of machine learning in real-world applications.

Requirements
- Familiarity with high school-level mathematics (algebra, probability, and statistics).
- Curiosity and motivation to learn machine learning concepts deeply.

Description
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 ofMachine 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 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.

Course homeworks are available for download under each relevant lecture's downloadable materials.

Course worksheets and Python files are available as a combined Zip package under the first lecture.

Each section comes with a small amount of supplementary reading material. They are available as downloadable resources with each section's first lecture.

A PDF of preliminary concepts is attached under the first lecture. They are the thingsyou should know already, either from prior courses you've followed, or from your high school education. However, since this course caters to many programs, we cannot fully ensure that all the preliminaries have been perfectly covered in videos. Therefore, this PDF describes and explains the basics that can be helpful for you.

Why take this course?

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

Covers everything from the basics to advanced topics like deep learning and AI ethics.

Perfect for beginners, career switchers, and curious minds.

Who this course is for:
- Beginners who want a comprehensive introduction to machine learning.
- Students and professionals looking to strengthen their understanding of ML theory and practice.
- Data scientists and analysts seeking to expand their toolkit with modern ML techniques.
- Software engineers interested in integrating machine learning into applications.
- Researchers and academics exploring the social and ethical implications of AI.
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