How to Prepare

Part 1: Python Basics
Learning Resource →

Part 2: Topic Classifications
The topics are categorized into three distinct sections, indicating the level and nature of knowledge contestants need:

1. Theory (How it works)

Contestants should thoroughly understand core concepts and theoretical underpinnings—essentially, the “why” behind AI. This may involve studying textbooks courses, and other resources to delve into the mechanics that power AI algorithms.

2. Practice (What it does, when to use it, and how to implement it)

Contestants should develop the practical skills necessary to implement AI methods in code. This includes knowing how to use library functions effectively, call the method on a particular data, and interpret outputs. Example: While a contestant need not fully dissect the internal workings of the Adam optimizer, they should be able to decide when and how to employ it.

3. Both

Certain topics require knowledge of both theoretical principles and practical application.

Section 1: Foundational Skills & Classical Machine Learning →

Section 2: Neural Networks & Deep Learning →

Section 3: Computer Vision →

Section 4: Natural Language Processing →

Part 3: Educational Resources

Type Title Author/Institution Contents Level URL
Online Course AI Python for Beginners Andrew Ng, Stanford University Python basics, API programming Beginner Link
Online Course Elements of AI University of Helsinki Introduction to AI, methods, applications, ethics Beginner Link
Online Course Python for Everybody University of Michigan Python basics, data structures, web scraping, databases Beginner Link (Audit)
Online Course Data Analysis with Python IBM Data analysis with Python, NumPy, Pandas Beginner Link (Audit)
Tutorial Google's Python Class Google Python basics, strings, lists, sorting, dicts, files, regex Beginner Link
Tutorial NumPy Tutorial W3Schools Introduction to NumPy, arrays, operations Beginner Link
Tutorial Pandas Tutorial W3Schools Introduction to Pandas, dataframes, series, operations Beginner Link
Open Course Introduction to Computer Science and Programming in Python MIT OpenCourseWare Python programming, computational thinking Beginner Link
Online Course Computer Vision Basics University at Buffalo, Coursera Image processing, feature detection, image matching Beginner Link (Audit)
eBook Python Crash Course Eric Matthes Python basics, data structures, file I/O Beginner Link (Sample chapters free)
eBook Think Python: How to Think Like a Computer Scientist Allen B. Downey Python programming, algorithms, debugging Beginner Link
eBook Automate the Boring Stuff with Python Al Sweigart Practical Python: automation, web scraping, working with files Beginner Link
Online Course Introduction to Artificial Intelligence with Python Harvard University Search algorithms, knowledge representation, ML, neural networks Beginner to Intermediate Link (Audit)
Book Hands-On Large Language Models: Language Understanding and Generation Jay et al. LLM basics with most intuitive explanations Beginner to Intermediate Link
Course Generative AI for Beginners Microsoft Generative AI intro Beginner to Intermediate Link
Course Hugging Face Agents Course Hugging Face Agents intro Beginner to Intermediate Link
Online Course Machine Learning with Python IBM ML with Scikit-learn: classification, regression, clustering Beginner to Intermediate Link (Audit)
Tutorial Natural Language Processing with Python NLTK Text processing, classification, tagging, parsing Beginner to Intermediate Link
eBook Introduction to Machine Learning with Python Andreas C. Müller, Sarah Guido ML basics with Scikit-learn: classification, regression, clustering Beginner to Intermediate Link (Free via some libraries or promotions)
eBook Natural Language Processing with Python Steven Bird, et. al. NLP basics, text processing, linguistic analysis Beginner to Intermediate Link
Online Course Machine Learning Andrew Ng, Stanford University Supervised and unsupervised learning, best practices Intermediate Link (Audit)
Online Course Deep Learning Andrew Ng, Stanford University Deep Learning Intermediate Link (Audit)
eBook Deep Learning with Python François Chollet Deep learning concepts, neural networks, Keras Intermediate Link (Free chapter previews)
Online Course Intro to Deep Learning with PyTorch Udacity, Facebook AI Deep learning fundamentals with PyTorch Intermediate Link
Jupyter Notebooks, Slides Introduction to Machine Learning Ali Sharifi, Sharif University Classical ML, Deep Learning, CV and NLP Intermediate Link
eBook Neural Networks and Deep Learning Michael Nielsen Neural network fundamentals, backpropagation, deep learning Intermediate Link
Open Course Introduction to Machine Learning MIT OpenCourseWare Lecture notes, assignments on ML topics Intermediate to Advanced Link
Open Course Artificial Intelligence MIT OpenCourseWare Lecture notes, assignments on AI topics Intermediate to Advanced Link
eBook Computer Vision: Algorithms and Applications Richard Szeliski Image processing, feature detection, recognition Intermediate to Advanced Link (Draft version free)