## LIVE TRAINING :

## Course Dates

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

12 PM EST, 9 AM PST

Â

# 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

#### Subscribe to a premium annual subscription and get all bootcamp courses freeÂ

#### 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.Â

# 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 doesnâ€™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

Uniforms

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

#### Â

#### 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
- Bayesâ€™ Theorem
- PyMC3 Notebook
- Resources for Further Study of Probability and Statistics

## Key Details

DATE

TIME:

DURATION:

LEVEL:

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

TIME: 12 PM EST, 9 AM PST

3 HOURS : PER EACH COURSE

BEGINNER

#### Prerequisites

**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. Youâ€™ll develop a working understanding of variables, probability distributions, metrics for assessing distributions, and graphical models.Â