Course Dates

March 25, APRIL 8, APRIL 22, MAY 6TH



Price: $299 or

  • 4 hour immersive session

  • Hands-on training with Q&A

  • Recording available on-demand

  • Certification of Completion

Free with premium annual subscription

  • Part of fundamentals bootcamp

  • 14 live and on-demand courses

  • Learn the four core foundational subjects in machine learning

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Meet Your Instructor

Dr. Jon Krohn

Dr Jon Krohn is Chief Data Scientist at the machine learning company, Untapt. He authored the 2019 book Deep Learning Illustrated, an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and the NYC Data Science Academy. Jon holds a Ph.D. in Neuroscience from Oxford and has been publishing on machine learning In leading academic journals since 2010; his papers have been cited over a thousand times. 

Why Enroll?

By the end of the course, participants will be able to:

  • Probability and Statistics are an important prerequisite for applied machine learning, as it helps us select, evaluate and interpret predictive models
  • Courses are part of the Fundamental Bootcamp
  • Learn fundamental machine learning theory
  • Probability and Statistics is one of the four core foundational subjects in machine learning

Course Overview

The premise of the course is that to be an outstanding data scientist or ML engineer, it doesn’t suffice to only know how to use ML algorithms via the abstract interfaces that the most popular libraries (e.g., scikit-learn, Keras) provide. To train innovative models or deploy them efficiently in production, an in-depth appreciation of machine learning theory is required. And, to cultivate such in-depth appreciation of ML, one must possess a working understanding of the four foundational subjects, including Probability an Statistics.

Probability and Statistics includes 4 courses, each 3 hours long. The course covers Introduction to Probability, Distribution in Machine Learning, Information Theory, Frequentist Statistics, Regression, Bayesian Statistics

Course Outline

1. Introduction to Probability

  • What Probability Theory Is
  • A Brief History: Frequentists vs Bayesians
  • Applications of Probability to Machine Learning
  • Random Variables
  • Discrete vs Continuous Variables
  • Probability Mass and Probability
  • Density Function
  • Expected Value
  • Measures of Central Tendency: Mean, Median, and Mode
  • Quantiles: Quartiles, Deciles, and Percentiles
  • The Box-and-Whisker Plot
  • Measures of Dispersion: Variance, Standard Deviation, and Standard Error
  • Measures of Relatedness: Covariance and Correlation
  • Marginal and Conditional Probabilities
  • Independence and Conditional Independence

3. Information Theory

  • What Information Theory Is
  • Self-Information
  • Nats, Bits and Shannons
  • Shannon and Differential Entropy
  • Kullback-Leibler Divergence
  • Cross-Entropy

5. Regression

  • Features: Independent vs Dependent Variables
  • Linear Regression to Predict
  • Continuous Values
  • Fitting a Line to Points on a Cartesian Plane
  • Ordinary Least Squares
  • Logistic Regression to Predict Categories
  • (Deep) ML vs Frequentist Statistics

2. Distribution in Machine Learning

  • Gaussian: Normal and Standard Normal
  • The Central Limit Theorem
  • Log-Normal
  • Binominal and Multinomial
  • Poisson
  • Mixture Distributions
  • Preprocessing Data for Model Input


4. Frequentist Statistics

  • Frequentist vs Bayesian Statistics
  • Review of Relevant Probability Theory
  • z-scores and Outliers
  • p-values
  • Comparing Means with t-tests
  • Confidence Intervals
  • ANOVA: Analysis of Variance
  • Pearson Correlation Coefficient
  • R-Squared Coefficient of Determination
  • Correlation vs Causation
  • Correcting for Multiple Comparisons

6. Bayesian Statistics

  • When to use Bayesian Statistics
  • Prior Probabilities
  • Bayes’ Theorem
  • PyMC3 Notebook
  • Resources for Further Study of Probability and Statistics

Key Details










Programming: All code demos will be in Python, so experience with it or another object-oriented programming language would be helpful, but not necessary, for following along with the code examples.

Mathematics: Familiarity with secondary school-level mathematics will make the class easier to follow along with. If you are comfortable dealing with quantitative information — such as understanding charts and rearranging simple equations — then you should be well prepared to follow along with all the mathematics.

Upcoming Live Training

March 10th

Part 1: Probability and Statistic Course

This class, Probability & Information Theory, introduces the mathematical fields that enable us to quantify uncertainty as well as to make predictions despite uncertainty. You’ll develop a working understanding of variables, probability distributions, metrics for assessing distributions, and graphical models. 

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