Intro to Statistics (ML Foundations Series)

• Quantify differences with confidence: Builds on probability theory to enable quantification of confidence in how data distributions relate to each other.
• Essential statistical approaches: Develop a practical understanding of key statistical tests for assessing data correlation and sampling differences, which are valuable for critically evaluating ML models.
• Foundational for advanced statistics: Serves as a prerequisite for the "Statistics II” class, which covers regression and Bayesian techniques.

The Machine Learning Foundations series of online trainings provides a comprehensive overview of all of the subjects — mathematics, statistics, and computer science — that underlie contemporary machine learning techniques, including deep learning and other artificial intelligence approaches. Extensive curriculum detail can be found at the course’s GitHub repo.

All of the classes in the ML Foundations series bring theory to life through the combination of vivid full-color illustrations, straightforward Python examples within hands-on Jupyter notebook demos, and comprehension exercises with fully-worked solutions. The focus is on providing you with a practical, functional understanding of the content covered. Context will be given for each topic, highlighting its relevance to machine learning. You will be better positioned to understand cutting-edge machine learning papers and you will be provided with resources for digging even deeper into topics that pique your curiosity.

There are 14 classes in the series, organized into four subject areas:

Linear Algebra (three classes)

• Linear Algebra for Machine Learning: Intro
• Linear Algebra for Machine Learning, Level II: Matrix Tensors
• Linear Algebra for Machine Learning, Level III: Eigenvectors

Calculus (four classes)

• Calculus for Machine Learning: Intro
• Calculus for Machine Learning, Level II: Automatic Differentiation
• Calculus for Machine Learning, Level III: Partial Derivatives
• Calculus for Machine Learning, Level IV: Gradients & Integrals

Probability and Statistics (four classes)

• Intro to Probability Theory
• Probability II and Information Theory
• Intro to Statistics
• Statistics II: Regression and Bayesian

Computer Science (three classes)

• Intro to Data Structures and Algorithms
• DSA II: Hashing, Trees, and Graphs
• Optimization

Each of the four subject areas are fairly independent, however theory within a given subject area generally builds over the 3-4 classes — topics in later classes of a given subject area often assume an understanding of topics from earlier classes. Work through the individual classes based on your particular interests or your existing familiarity with the material. (Note that at any given time, only a subset of the ML Foundations classes will be scheduled and open for registration.)

This class, Intro to Statistics, builds on probability theory to enable us to quantify our confidence about how distributions of data are related to one another. Through the measured exposition of theory paired with interactive examples, you’ll develop a working understanding of all of the essential statistical tests for assessing whether data are correlated with each other or sampled from different populations — tests that frequently come in handy for critically evaluating the inputs and outputs of machine learning algorithms. The content covered in this class is foundational for Statistics Level II: Regression and Bayesian.