In machine learning, knowledge of **probability and statistics** is mandatory. But when it comes to learning, we might feel overwhelmed. Because there are lots of resources available for learning probability and statistics. That’s why I am gonna share some of the Best Resources to Learn Probability and Statistics For Machine Learning.

So, without further ado, let’s get started-

**Best Resources to Learn Probability and Statistics For Machine Learning**

- Online Courses
- 1. Probability Theory, Statistics, and Exploratory Data Analysis- National Research University Higher School of Economics
- 2. Intro to Statistics- Udacity
- 3. Probability and Statistics- University of London
- 4. Statistics and probability- Khan Academy
- 5. Intro to Inferential Statistics– Udacity
- 6. Probability and Statistics in Data Science using Python- UCSanDiego
- 7. Statistics with R Specialization- Duke University
- 8. Basic Statistics- University of Amsterdam
- 9. Probability – The Science of Uncertainty and Data- MITx
- 10. Statistics with Python Specialization- University of Michigan
- 11. Fundamentals of Statistics- MITx
- TextBooks
- 1. An Introduction to Statistical Learning
- 2. Practical Statistics for Data Scientists
- 3. Probability and Statistics for Data Science
- 4. Introduction to Probability
- Other Resources
- Conclusion

**Online Courses**

**1. ****Probability Theory, Statistics, and Exploratory Data Analysis**– **National Research University Higher School of Economics**

**Probability Theory, Statistics, and Exploratory Data Analysis**

**Rating- **4.7/5

**Provider- **Coursera

**Time to Complete- **19 Hours

This course starts from the **very basics** all way up to the level required for** jump-starting your ascent in Data Science.** This course is part of **Mathematics for Data Science Specialization**. This course not only teaches theory, but also focuses on **practical aspects** for working with **probabilities, sampling, data analysis, and data visualization in Python. **

**Prof. Ilya V. Schurov** covers many aspects of probability theory clearly and more understandably**.** In short, this is one of the best courses to understand the **fundamentals of probability & statistics.**

**You Should Enroll if-**

- You have basic knowledge in
**Discrete mathematics and calculus (derivatives, integrals).**

**Interested to Enroll?**

If yes, then check out all details here- **Probability Theory, Statistics, and Exploratory Data Analysis**

**2. ****Intro to Statistics**– **Udacity**

**Time to Complete- **2 Months

This is a **beginner-level statistics course** that covers **data visualization, probability, and many elementary statistics concepts like regression, hypothesis testing, and more.**

In this course, you will learn **visualization and relationships in data, Probability with Bayes Rule and Correlation vs Causation, estimation with Maximum Likelihood,** **mean, median and mode,** **statistical inference, and regression analysis.**

**You Should Enroll if-**

- You are beginner, but it’s good if you have already heard of some
**easy statistical concepts.**

**Interested to Enroll?**

If yes, then check out all details here- **Intro to Statistics**

**3. ****Probability and Statistics**– University of London

**Probability and Statistics**– University of London

**Rating- **4.6/5

**Provider- **Coursera

**Time to Complete- **18 Hours

This course is especially dedicated to **Probability and Statistics**. In this course, you will learn many useful tools to deal with uncertainty.

The main topics of the course are **quantifying uncertainty with probability, descriptive statistics, point and interval estimation of means and proportions, the basics of hypothesis testing, and a selection of multivariate applications**.

**You Should Enroll if-**

- You are beginner and wants to learn Probability and Statistics.

**Interested to Enroll?**

If yes, then check out all details here- **Probability and Statistics**

**4. Statistics and probability– Khan Academy**

**Time to Complete- **Less than a month

This course covers basic **probability and distributions** to more advanced concepts like** inference or ANOVA models. **This course is the best step after going through an Introductory Statistics book like **Bayesian Statistics the Fun Way**, which is more theoretical and has less code.

Most of the Khan Academy courses are combined with** fun and short videos with quizzes**. In quizzes you get points. These quizzes will help you to check your statistical knowledge level.

**You Should Enroll if-**

- You have some basic understanding of maths.

**Interested to Enroll?**

If yes, then check out all details here-** **Statistics and probability

**5. ****Intro to Inferential Statistics– Udacity**

**Intro to Inferential Statistics– Udacity**

**Time to Complete- **2 Months

This is a complete **Free course** for statistics. In this course, you will learn how to **estimate parameters of a population using sample statistics, hypothesis testing and confidence intervals, t-tests and ANOVA, correlation and regression, and chi-squared test.**

This course is taught by industry professionals and you will learn by doing various exercises.

**You Should Enroll if-**

- You have basic understanding of Descriptive Statistics.

**Interested to Enroll?**

If yes, then start learning- **Intro to Inferential Statistics**

**6. Probability and Statistics in Data Science using Python– UCSanDiego**

**Provider- **edX

**Time to Complete- **10 Weeks

This course covers both the** mathematical theory**, and get the **hands-on experience** of applying this theory to actual data using **Jupyter notebooks.** In this course, you will learn about **random variables, dependence, correlation, regression, PCA, entropy, and MDL.**

**You Should Enroll if-**

- You have
**good understanding of Python and has basic knowledge of maths.**

**Interested to Enroll?**

If yes, then check out all details here- **Probability and Statistics in Data Science using Python**

**7. ****Statistics with R Specialization**– **Duke University**

**Statistics with R Specialization****Rating-** 4.6/5

**Provider-** Coursera

**Time to Complete- **7 Month

This specialization program will give you **more in-depth Statistics with the help of R**. In this program, you will learn how to** analyze and visualize data in R** and create reproducible data analysis reports, and much more.

R is much better than Python for performing statistical operations. So, if you want to master Statistics, then I will recommend this specialization program. This specialization program contains **5 Courses**. Let’s see course details-

**Courses Includes-**

**Introduction to Probability and Data with R****Inferential Statistics****Linear Regression and Modeling****Bayesian Statistics****Statistics with R Capstone**

**You Should Enroll if-**

- You have basic math knowledge. No previous programming knowledge is required for this course.

**Interested to Enroll?**

If yes, then check out all details here- **Statistics with R Specialization**

**8. **** Basic Statistics**– **University of Amsterdam**

**Basic Statistics****Rating- **4.7/5

**Provider- **Coursera

**Time to Complete- **26 hours

In this course, you will learn the **basics of statistics **like what cases and variables are and how you can compute measures of **central tendency** **(mean, median, and mode) and dispersion (standard deviation and variance).**

Along with that, you will learn the basics of probability- **calculating probabilities, probability distributions, and sampling distributions.** You will also learn **inferential statistics.**

**You Should Enroll if-**

- You want to learn basics of statistics with beginner level.

**Interested to Enroll?**

If yes, then check out all details here- ** Basic Statistics**

**9. ****Probability – The Science of Uncertainty and Data**– **MITx**

**Provider- **edX

**Time to Complete- **16 Weeks

This is one of the most demanding courses for probability. In this course, you will learn **multiple discrete or continuous random variables, expectations, and conditional distributions, laws of large numbers, Bayesian inference methods, Poisson processes, and Markov chains.**

**You Should Enroll if-**

- You know
**college-level calculus,**comfortable with**mathematical reasoning**, and familiar with**sequences, limits, infinite series, the chain rule, and ordinary or multiple integrals.**

**Interested to Enroll?**

If yes, then check out all details here- **Probability – The Science of Uncertainty and Data**–

**10. ****Statistics with Python Specialization**– **University of Michigan**

**Statistics with Python Specialization****Rating- **4.5/5

**Provider- **Coursera

**Time to Complete- ** 3 months

This specialization program is especially dedicated to statistics. In this program, you will learn the beginning and intermediate concepts of statistical analysis using the Python programming language.

In this program, you will learn all important concepts like- **where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization.**

Along with that, you will work with a variety of assignments that will help you to check your knowledge and ability. This specialization program is a 3-course series. Let’s see the courses includes-

**Courses Includes-**

**Understanding and Visualizing Data with Python****Inferential Statistical Analysis with Python****Fitting Statistical Models to Data with Python**

**You Should Enroll if-**

- You have Knowledge of
**basic Python and High school-level algebra.**

**Interested to Enroll?**

If yes, then check out all details here- **Statistics with Python Specialization**

**11. Fundamentals of Statistics**– **MITx**

**Provider- **edX

**Time to Complete- ** 18 Weeks

This is an advanced-level course, that teaches core ideas on firm **mathematical grounds** that begin with the **construction of estimators and tests, as well as an analysis of their asymptotic performance.**

In this course, you will learn how to make predictions using **linear, nonlinear, and generalized linear models**, how to perform **dimension reduction using principal component analysis (PCA)**, how to choose **between different models using the goodness of fit test**, etc.

**You Should Enroll if-**

- You are familiar with
**vectors and matrices**, and has**college-level single and multi-variable calculus knowledge.**

**Interested to Enroll?**

If yes, then check out all details here- **Fundamentals of Statistics**

So, these are some best online courses for **Probability and Statistics For Machine Learning**. Now let’s see some Textbooks for **Probability and Statistics.**

**TextBooks**

**1. An Introduction to Statistical Learning**

**Author**– Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

**About Book-**

This book is written for those people who **don’t have programming and statistical knowledge**. Even if you are an experienced person, you can refer to this book to brush up on **your knowledge**. Because of a lot of statistical concepts, you kind of forget about them over time.

This book presents some of the most important modeling and prediction techniques along with relevant applications. Topics include **linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering**, and more.

**You Should Read this Book, if-**

- You are a beginner in Data Science Field, or Experienced, who want to brush-up your knowledge.

**Where to Get-**

You can buy this book on Amazon-**An Introduction to Statistical Learning**

You can download the pdf version of this book from **here.**

**2. ****Practical Statistics for Data Scientists**

**Author-**Peter Bruce

**About Book-**

If you are a beginner, then this book is good for you. **Practical Statistics for Data Scientists **explains how to apply various statistical methods to data science.

In this book, you will learn **randomization, sampling, distribution, sample bias**, etc.

All of these concepts are explained with examples. Along with that, this book explains how these concepts are relevant to data science.

This book will not give in-depth knowledge but it is good for getting a quick and easy reference to Data Science.

**Where to Get this Book-**

You can buy this book on Amazon-**Practical Statistics for Data Scientists**

You can download the pdf version of this book from **here.**

**3. Probability and Statistics for Data Science**

**Author-**Norman Matloff

**About Book-**

This book covers “math stat”―distributions, expected value, estimation, etc. In this book, Read datasets are used. All Data Analysis tasks are performed with the **R programming language.**

**Probability and Statistics for Data Science** Include many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks.

**You Should Read this Book, if-**

You have some knowledge of **calculus and programming** before reading this book.

**Where to Get-**

You can buy this book on Amazon-**Probability and Statistics for Data Science.**

**4. Introduction to Probability**

**Author-** Joseph K. Blitzstein, Jessica Hwang

**About Book-**

For Data Science, you should have basic knowledge of Probability. So, to learn the basics of Probability, this is the best book.

If you have studied probability in your school, then this is the **best book to brush up your knowledge**. And if you never learn Probability, then this book will give you a strong foundation in the core concepts.

The book includes many intuitive **explanations, diagrams, and practice problems**. Each chapter ends with a section showing how to perform **relevant simulations and calculations in R, a free statistical software environment.**

**Introduction to Probability** has been one of the most popular books for about 5 decades.

**You Should Read this Book, if-**

- You want to learn the basics of Probability for Data Science. This book is ideal for beginners and experienced.

**Where to Buy this Book-**

You can buy this book on Amazon- **Introduction to Probability.**

You can download the pdf version of this book from **here.**

So, these are some best books for probability and statistics. Now let’s see some other resources for learning probability and statistics.

**Other Resources**

**Probability theory****(Wikipedia)****Statistics for Data Science****(YouTube Video)****Probability****on Khan Academy****Statistics – Probability****(TutorialsPoint)****Probability Tutorial****(Stat Trek)****Probability and Statistics****(MathisFun)**

And here the list ends. So these are some of the **Best Resources to Learn Probability and Statistics For Machine Learning**. Now it’s time to wrap up.

**Conclusion**

In this article, I tried to cover **Best Resources to Learn Probability and Statistics For Machine Learning** from online courses to textbooks. I hope these resources will definitely make you master probability and statistics for machine learning.

If you have any doubts or questions, feel free to ask me in the comment section.

All the Best!

Enjoy Learning!

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### Written By Aqsa Zafar

Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.