Intro to Statistics (Machine Learning Foundations)
Published by Pearson
Evaluate ML Inputs and Outputs with the Essential Statistical Approaches
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)
- Intro to Linear Algebra
- Linear Algebra II: Matrix Tensors
- Linear Algebra III: Eigenvectors
Calculus (four classes)
- Intro to Calculus
- Calculus II: Automatic Differentiation
- Calculus III: Partial Derivatives
- Calculus IV: Gradients and Integrals
Probability and Statistics (four classes)
- Intro to Probability
- 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
You’re welcome to pick and choose between any of the 14 individual classes based on your particular interests or your existing familiarity with the material. Note that 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.
(Note that at any given time, only a subset of the ML Foundations classes will be scheduled and open for registration. To be pushed notifications of upcoming classes in the series, sign up for the instructor’s email newsletter at jonkrohn.com.)
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 which 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 II: Regression and Bayesian.
What you’ll learn and how you can apply it
- Understand the fundamentals of both frequentist and Bayesian statistics, as well as appreciate when one of these approaches is appropriate for the problem you’re solving.
- Prepare data for machine learning models by removing outliers and adjusting input distributions toward standard normal.
- Hypothesize about and critically evaluate the inputs and outputs of ML algorithms using essential statistical tools for comparing means such as the t-test and ANOVA.
This live event is for you because...
- You use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms, and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities
- You’re a software developer who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems
- You’re a data scientist who would like to reinforce your understanding of the subjects at the core of your professional discipline
- You’re a data analyst or A.I. enthusiast who would like to become a data scientist or data/ML engineer, and so you’re keen to deeply understand the field you’re entering from the ground up (very wise of you!)
Prerequisites
- Programming: All code demos will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the code examples.
- Mathematics: You should either have attended the Linear Algebra II: Matrix Tensors live training or be familiar with the content in Lessons 1-6 of Jon Krohn’s Linear Algebra for ML LiveLessons
Materials, downloads, or Supplemental Content needed in advance
- During class, we’ll work on Jupyter notebooks interactively in the cloud via Google Colab. This requires zero setup and instructions will be provided in class.
Resources
- If you’re feeling extremely ambitious, you can get a headstart on the content we’ll be covering in class by viewing Lessons 7-9 of Jon Krohn’s Linear Algebra for ML LiveLessons
The remainder of Jon’s ML Foundations curriculum is split across the following videos:
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Segment 1: Intro to Stats (30 min)
- Frequentist vs Bayesian Statistics
- Applications of Statistics to ML
- Quick Review of Essential Probability Theory
- Q&A and Break
Segment 2: Quantifying Uncertainty (90 min)
- z-scores
- Outliers
- p-values
- Q&A and Break
Segment 3: Comparing Means (90 min)
- Single-Sample t-tests
- Degrees of Freedom
- Independent t-tests
- Paired t-tests
- Confidence Intervals
- ANOVA: Analysis of Variance
- Final Exercises and Q&A
Your Instructor
Jon Krohn
Jon Krohn is Co-Founder of the AI software firm Y Carrot and a Fellow at Lightning AI. He authored the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He is also the host of SuperDataScience, the data science industry’s most listened-to podcast. Jon is renowned for his compelling lectures, which he offers at leading universities and conferences, as well as via his award-winning YouTube channel. He holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010.
Skills covered
- Machine Learning
- Statistics