Home > Store

Probability and Statistics for Machine Learning LiveLessons (Video Training)

Probability and Statistics for Machine Learning LiveLessons (Video Training)

Your browser doesn't support playback of this video. Please download the file to view it.

Online Video

Register your product to gain access to bonus material or receive a coupon.

Description

  • Copyright 2021
  • Edition: 1st
  • Online Video
  • ISBN-10: 0-13-756623-9
  • ISBN-13: 978-0-13-756623-5

9 Hours of Video Instruction

Hands-on approach to learning the probability and statistics underlying machine learning

Overview

Probability and Statistics for Machine Learning (Machine Learning Foundations) LiveLessons provides you with a functional, hands-on understanding of probability theory and statistical modeling, with a focus on machine learning applications.

About the Instructor

Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated, an instant #1 bestseller that has been translated into six languages. Jon is renowned for his compelling lectures, which he offers in person at Columbia University and New York University, as well as online via O'Reilly, YouTube, and the SuperDataScience podcast. Jon holds a PhD from Oxford and has been publishing on machine learning in leading academic journals since 2010; his papers have been cited over a thousand times.

Skill Level

  • Intermediate

Learn How To
  • Understand the appropriate variable type and probability distribution for representing a given class of data
  • Calculate all of the standard summary metrics for describing probability distributions, as well as the standard techniques for assessing the relationships between distributions
  • Apply information theory to quantify the proportion of valuable signal that's present among the noise of a given probability distribution
  • Hypothesize about and critically evaluate the inputs and outputs of machine learning algorithms using essential statistical tools such as the t-test, ANOVA, and R-squared
  • 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
  • Use historical data to predict the future using regression models that take advantage of frequentist statistical theory (for smaller data sets) and modern machine learning theory (for larger data sets), including why we may want to consider applying deep learning to a given problem
  • Develop a deep understanding of what's going on beneath the hood of predictive statistical models and machine learning algorithms

Who Should Take This Course
  • 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 AI 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!)

Course Requirements
  • Mathematics: Familiarity with secondary school-level mathematics will make it easier for you to follow along with the class. 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 of the mathematics.
  • 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 hands-on examples.

Lesson Descriptions

Lesson 1: Introduction to Probability
In Lesson 1, Jon starts by orienting you to the machine learning foundations series and covering what probability theory is. He then begins coverage of the most essential probability concepts, which is reinforced by comprehension exercises. The lesson ends with a comparison of Bayesian and frequentist statistics, as well as a discussion of applications of probability to machine learning.

Lesson 2: Random Variables
Lesson 2 focuses on random variables, a fundamental probability concept that is a prerequisite for understanding the later lessons. Jon starts off with an exploration of discrete and continuous variables as well as the probability distributions to which they correspond. The lesson wraps up with calculation of the expected value of random variables.

Lesson 3: Describing Distributions
Lesson 3 is all about metrics for describing probability distributions. Jon covers measures of central tendency, quantiles, box-and-whisker plots, measures of dispersion, and measures of relatedness.

Lesson 4: Relationships Between Probabilities
In Lesson 4, Jon explores the core relationships between probabilities, including joint distributions, marginal and conditional probabilities, the chain rule, and independence.

Lesson 5: Distributions in Machine Learning
Having now led you through mastering probability theory in general, in Lesson 5 Jon details the most important probability distributions in machine learning, including the uniform and normal distributions, as well as the critical concept of the central limit theorem. He also covers the log-normal, exponential, discrete, and Poisson distributions, as well as mixtures of distributions and how to prepare distributions for input into a machine learning model.

Lesson 6: Information Theory
In Lesson 6, Jon provides you with an introduction to information theory, a field of study related to probability theory that includes some key concepts that are ubiquitous in machine learning. Specifically, he defines self-information, Shannon entropy, KL divergence, and cross-entropy.

Lesson 7: Introduction to Statistics
From Lesson 7 onward, Jon shifts gears from general probability theory to the statistical models that probability theory facilitates. He starts by explaining how statistics are applied to machine learning and reviewing the most essential probability theory you absolutely must know to move forward. He then introduces new statistics concepts, specifically z-scores and p-values.

Lesson 8: Comparing Means
In Lesson 8, Jon teaches you to use probability and statistics to compare distributions with t-tests. He covers all the critical types, including the single-sample, independent, and paired varieties. Jon provides specific applications of t-tests to machine learning, and then wraps the lesson up with a discussion of related concepts, namely, confidence intervals and analysis of variance.

Lesson 9: Correlation
Lesson 9 builds on the introduction to correlation in Lesson 3. You are now armed with enough statistical knowledge to calculate p-values for correlations and calculate the coefficient of determination. Jon finishes off the lesson with important discussions about inferring causation and correcting for multiple comparisons.

Lesson 10: Regression
You're in for a treat with Lesson 10, which brings together the preceding lessons with practical, real-world demonstrations of regression--a powerful, highly extensible approach to making predictions. Jon distinguishes independent from dependent variables and uses linear regression to predict continuous variables--first with a single model feature and then with many, including discrete features. The lesson concludes with logistic regression for predicting discrete outcomes.

Lesson 11: Bayesian Statistics
Lesson 11 is on Bayesian statistics. Jon provides a guide as to when frequentist statistics or Bayesian statistics might be the appropriate option for the problem you're solving. Jon then introduces the most essential Bayesian concepts. Finally, Jon leaves you with resources for studying probability and statistics beyond what we had time for in these LiveLessons themselves.

Notebooks are available at github.com/jonkrohn/ML-foundations

About Pearson Video Training

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.

Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.

Sample Content

Table of Contents

Introduction to Probability and Statistics for Machine Learning (Machine Learning Foundations) LiveLessons

Lesson 1: Introduction to Probability
Topics
1.1 Orientation to the Machine Learning Foundations Series
1.2 What Probability Theory Is
1.3 Events and Sample Spaces
1.4 Multiple Observations
1.5 Factorials and Combinatorics
1.6 Exercises
1.7 The Law of Large Numbers and the Gambler's Fallacy
1.8 Probability Distributions in Statistics
1.9 Bayesian versus Frequentist Statistics
1.10 Applications of Probability to Machine Learning

Lesson 2: Random Variables
Topics
2.1 Discrete and Continuous Variables
2.2 Probability Mass Functions
2.3 Probability Density Functions
2.4 Exercises on Probability Functions
2.5 Expected Value
2.6 Exercises on Expected Value

Lesson 3: Describing Distributions
Topics
3.1 The Mean, a Measure of Central Tendency
3.2 Medians
3.3 Modes
3.4 Quantiles: Percentiles, Quartiles, and Deciles
3.5 Box-and-Whisker Plots
3.6 Variance, a Measure of Dispersion
3.7 Standard Deviation
3.8 Standard Error
3.9 Covariance, a Measure of Relatedness
3.10 Correlation

Lesson 4: Relationships Between Probabilities
Topics
4.1 Joint Probability Distribution
4.2 Marginal Probability
4.3 Conditional Probability
4.4 Exercises
4.5 Chain Rule of Probabilities
4.6 Independent Random Variables
4.7 Conditional Independence

Lesson 5: Distributions in Machine Learning
Topics
5.1 Uniform
5.2 Gaussian: Normal and Standard Normal
5.3 The Central Limit Theorem
5.4 Log-Normal 
5.5 Exponential and Laplace
5.6 Binomial and Multinomial
5.7 Poisson
5.8 Mixture Distributions
5.9 Preprocessing Data for Model Input
5.10 Exercises

Lesson 6: Information Theory
Topics
6.1 What Information Theory Is
6.2 Self-Information, Nats, and Bits
6.3 Shannon and Differential Entropy
6.4 Kullback-Leibler Divergence and Cross-Entropy

Lesson 7: Introduction to Statistics
Topics
7.1 Applications of Statistics to Machine Learning
7.2 Review of Essential Probability Theory
7.3 z-scores and Outliers
7.4 Exercises on z-scores
7.5 p-values
7.6 Exercises on p-values

Lesson 8: Comparing Means
Topics
8.1 Single-Sample t-Tests and Degrees of Freedom
8.2 Independent t-Tests
8.3 Paired t-Tests
8.4 Applications to Machine Learning
8.5 Exercises
8.6 Confidence Intervals
8.7 ANOVA: Analysis of Variance

Lesson 9: Correlation
Topics
9.1 The Pearson Correlation Coefficient
9.2 R-Squared Coefficient of Determination
9.3 Correlation versus Causation
9.4 Correcting for Multiple Comparisons

Lesson 10: Regression
Topics
10.1 Independent versus Dependent Variables
10.2 Linear Regression to Predict Continuous Values
10.3 Fitting a Line to Points on a Cartesian Plane
10.4 Linear Least Squares Exercise
10.5 Ordinary Least Squares
10.6 Categorical "Dummy" Features
10.7 Logistic Regression to Predict Categories
10.8 Open-Ended Exercises

Lesson 11: Bayesian Statistics
Topics
11.1 Machine Learning versus Frequentist Statistics
11.2 When to use Bayesian Statistics
11.3 Prior Probabilities
11.4 Bayes' Theorem
11.5 Resources for Further Study of Probability and Statistics

Summary of Probability and Statistics for Machine Learning (Machine Learning Foundations) LiveLessons

Updates

Submit Errata

More Information

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.

Overview


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information


To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.

Surveys

Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.

Newsletters

If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information


Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.

Security


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.

Children


This site is not directed to children under the age of 13.

Marketing


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information


If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.

Choice/Opt-out


Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information


Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents


California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure


Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.

Links


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact


Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice


We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020