Are you looking for the B**est Math Courses for Machine Learning and Data Science**? But confused because of so many courses available online. So,** don’t worry**. Your search will **end** after reading this article. In this article, you will find the 12 Best Math Courses for Machine Learning and Data Science. So, give your few minutes to this article and find the **Best Math Courses for Machine Learning** **and Data Science**.

- 1. Mathematics for Machine Learning Specialization- Coursera
- 2. Intro to Statistics– Udacity
- 3. Linear Algebra Refresher Course– Udacity
- 4. Data Science Math Skills- Coursera
- 5. Introduction to Calculus- Coursera
- 6. The Math of Data Science: Linear Algebra– edX
- 7. Probabilistic Graphical Models Specialization- Coursera
- 8. Statistics with R Specialization- Coursera
- 9. Probability and Statistics- Coursera
- 10. Intro to Descriptive Statistics– Udacity
- 11. Mathematical Foundation For Machine Learning and AI- Udemy
- 12. Calculus 3 (Multivariable calculus)- Udemy

Knowledge of Mathematics is **essential** for understanding how machine learning and its algorithms work. In math, the most important topics are-

- Probability and Statistics
- Linear Algebra
- Calculus
- Matrix

This image from **Built In** shows the importance of each mathematic topic for Machine Learning.

So, to learn or brush up on these topics, I have chosen the **12** **Best Math Courses for Machine Learning** **and Data Science**. First, let me tell you, by what criteria these courses are “**Best Math Courses**”-

**Criteria-**

- Rating of these Courses.
- Coverage of Topics.
- Engaging trainer and Interesting lectures.
- Number of Students Benefitted.
- Good Reviews from various aggregators and forums.

So, without wasting your time, let’s start finding the **Best Math Courses for Machine Learning****and Data Science** for you.

**Best Math Courses for Machine Learning** **and Data Science**

**Best Math Courses for Machine Learning**

**1.** **Mathematics for Machine Learning Specialization**– Coursera

**Mathematics for Machine Learning Specialization**– Coursera

Rating- | 4.4/5 |

Provider- | Imperial College London |

Time to Complete- | 4 Months (4 hours/week) |

FREE/Paid– | Paid |

This ** Mathematics for Machine Learning Specialization** is one of the best specialization programs that covers

**all mathematical topics**required for Machine Learning. This specialization program is a

**3-course**series.

To learn Linear Algebra concepts such as **vectors, matrices**,** etc, **and Calculus concepts such as **Regression**, **optimization**, **Taylor series, and linearisation**, this specialization program is good.

The last course of this program is on **Principal Component Analysis (PCA)**. **Principal Component Analysis(**PCA) is an unsupervised algorithm. Also, it is the most popular **dimensionality Reduction Algorithm.**

This specialization program also has various exercises and hands-on practices to strengthen your understanding.

These are the 3 courses-

**Courses Include-**

**Mathematics for Machine Learning: Linear Algebra****Mathematics for Machine Learning: Multivariate Calculus****Mathematics for Machine Learning: PCA**

**Pros-**

- You will get a
**Shareable Certificate and Course Certificates**upon completion. - Provides enough knowledge of
**Linear Algebra.** - Their
**quizzes and assignments**are engaging and helpful.

**Cons-**

- The last course of this program lacks in quality compared to the first two courses.

Now, let’s see whether you should enroll in this specialization program or not.

**You Should Enroll if-**

- The program claims that this is suitable for beginners but I observed that previous Linear Algebra Knowledge is required. And basic knowledge of Python and NumPy is required for
**Course 3**.

**Interested to Enroll?**

If yes, then check out all details here- **Mathematics for Machine Learning Specialization**

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

**Intro to Statistics**–

**Udacity**

Rating- | NA |

Provider- | Udacity |

Time to Complete- | 2 Months |

FREE/Paid– | FREE |

This ** Intro to Statistics** is a

**completely FREE course**for

**beginners**. The founder of Udacity “

**Sebastian Thrun**” will teach in this course. If you are a beginner and want to

**learn statistics from scratch,**I would suggest this course. Because this course is completely free and you will learn from the basics.

I found various free courses that lack content quality but this course has a lot of content. There are 34 lessons in this course where you will learn **Scatter Plots, Bar Charts, Pie Charts, Probability, Bayes Rule, Central Limit Theorem, Normal Distribution, and many more.**

This course not only covers theoretical concepts but also has **exercises and case studies.** There are various problem sets in this course for eg. problem set on **Regression and Correlation**, problem set on **Probability, etc.**

**Pros-**

- For this course, you don’t need to pay any amount.
- You will learn from an experienced instructor,
**Sebastian Thrun**.

**Cons-**

- After completing this course, you will not receive a certificate.

**You Should Enroll if-**

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

**Interested to Enroll?**

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

**3. ****Linear Algebra Refresher Course– Udacity**

**Linear Algebra Refresher Course– Udacity**

Rating- | NA |

Provider- | Udacity |

Time to Complete- | 4 Months |

FREE/Paid– | FREE |

This ** Linear Algebra Refresher Course** is a

**Free course**to learn

**linear algebra basics.**This is not a very long course. It has only

**4 lessons.**

In this course, you will learn **Points and Vectors** and how to identify **Points & Vectors**. For learning **vectors**, there is a separate lesson in this course, where you will learn **The Vector Module**, **Plus, Minus, Scalar Multiply**, **Magnitude and Direction**, **Inner Products**, **Coding Dot Product & Angle**, **Projecting Vectors**, and **Cross Products**.

The last two lessons are on **Intersections** and **Matrices and Transformation of State**.

In **Matrices and Transformation of State**, you will learn about **Kalman Prediction**, **Vectors in Python**, **Coding Matrices**, etc. The instructor uses **Python Programming Language** to teach these concepts.

**Pros-**

- This course is completely free.
- Work on hands-on practices.
- Cover in-depth Linear Algebra concepts.

**Cons-**

- This course doesn’t provide a certificate of completion.

**You Should Enroll if-**

- You have experience with Python programming languages.

**Interested to Enroll?**

If yes, then start learning- **Linear Algebra Refresher Course**

**4. Data Science Math Skills- Coursera**

Rating- | 4.5/5 |

Provider- | Duke University |

Time to Complete- | 13 hours |

FREE/Paid– | Paid |

This **Data Science Math Skills** course is offered by **Duke University**. As the name sound, this course is designed for math required for data science.

In this course, first, you will learn **Sets**, **Numbers, and Sigma Notation**. The best thing I found in this course is that this course doesn’t cover only the theoretical part. There are **various quizzes** in this course. You will find a graded quiz on **Sets, Numbers**, **Sigma Notation**, etc.

You will also learn **Cartesian Plane**,** Functions, and Tangent Lines**.

This course will also teach **Probability** and covers **Permutations and Combinations**, **Bayes’ Theorem, etc.** Overall, this is a great course to learn **Linear Algebra. **

**Pros-**

- You will get a
**Shareable Certificate**upon completion. - This course also has quizzes and practical assignments.

**Cons-**

- The instructor doesn’t explain probability concepts in detail.

Now, let’s see whether you should enroll in this course or not.

**You Should Enroll if-**

- You have
**no prior knowledge of Math**and want to learn the basics of Math required for Machine Learning and Data Science.

**Interested to Enroll?**

If yes, then check out all details here- **Data Science Math Skills**

**5. Introduction to Calculus- Coursera**

Rating- | 4.8/5 |

Provider- | The University of Sydney |

Time to Complete- | 51 hours |

FREE/Paid– | Paid |

This **Introduction to Calculus** is dedicated to **Calculus**. In this course, you will get a complete understanding of Calculus. There are 5 modules in this course.

The course is combined with **videos, quizzes, and notes.** In the first two modules, you will learn **precalculus and functions.** You will find a lot of exercises throughout this course.

**Limits and Derivatives** are explained in modules 3 and 4. And the last and 5th module covers **integral calculus**. The quizzes are not easy to complete and you might need **extra resources **to finish the quizzes.

But, Overall this is a great course to **dive deeper into calculus** required for machine learning and data science. The instructor of this course **explains the concepts very easily **and his pace of teaching is just perfect.

**Pros-**

- You will get a
**Shareable Certificate**upon completion. - Covers calculus in detail.
- Uses quizzes and exercises to understand the concepts.

**Cons-**

- The quizzes are hard to finish.

Now, let’s see whether you should enroll in this course or not.

**You Should Enroll if-**

- You want to learn Calculus but have some previous knowledge.

**Interested to Enroll?**

If yes, then check out all details here- **Introduction to Calculus**

**6. ****The Math of Data Science: Linear Algebra– edX**

**The Math of Data Science: Linear Algebra– edX**

Rating- | NA |

Provider- | RICE University |

Time to Complete- | 8 Weeks( If you spend 6-8 hours per week) |

FREE/Paid– | Paid |

This **“ The Math of Data Science: Linear Algebra“** is another best course to learn Linear algebra for data science. The best thing about this course is that it tries to make a

**balance between theory and practice.**

The instructor explains the theory as well as covers **hands-on practices. **edX is one of the most popular data science and machine learning platforms. Their courses are from **Top Universities across the globe.**

Throughout this course, you will learn the essential concepts of Linear Algebra such as **Linear equations, vectors, matrices, etc.** The instructor not only covers these topics but also teaches** how to extract useful information from data using Linear Algebra.**

**Pros-**

- You will get a
**Shareable Certificate**upon completion. - Work on real-world problems.
- Accredited certificate.

**Cons-**

- edX enrollment process is confusing.

**Who Should Enroll?**

- Those who studied
**High school algebra**.

**Interested to Enroll?**

If yes, then check out all details here- **The Math of Data Science: Linear Algebra**

**7. Probabilistic Graphical Models Specialization- Coursera**

Rating- | 4.6/5 |

Provider- | Stanford University |

Time to Complete- | 4 Months ( 11 hours/week) |

FREE/Paid– | Paid |

This **Probabilistic Graphical Models Specialization** is an advanced-level program. This is not for beginners. In this program, you will learn about **probabilistic graphical models**. This program required **previous knowledge of Probability, previous programming experience, and a basic understanding of Data Structure and Algorithms.**

This program has 3 courses. And in this program, you will learn fundamental methods in **probabilistic graphical models** and the real-world applications related to **probabilistic graphical models**.

This program is **more practical** in nature. There are** different quizzes and exercises **in this program. To complete this program, you must give proper attention to the material because this program covers** complex topics.**

**Pros-**

- You will get a
**Shareable Certificate and Course Certificates**upon completion. - You will work on Real-World Projects.
- And you will learn advanced topics.

**Cons-**

- The support is not good. It’s hard to get a reply from the instructor if you have any doubts.

**You Should Enroll if-**

- You have
**some mathematical skills****and Programming skills.** - And some previous knowledge of
**basic concepts in discrete probability theory**(independence, conditional independence, and Bayes’ rule) will also help.

**Interested to Enroll?**

If yes, then check out all details here- **Probabilistic Graphical Models Specialization**

**8. ****Statistics with R Specialization**– Coursera

**– Coursera**

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

Provider- | Duke University |

Time to Complete- | 7 Months (3 hours/week) |

FREE/Paid– | Paid |

This **Statistics with R Specialization** program is good for learning

**Statistical concepts using R Programming.**This program has 3 courses. This program is designed for those who are interested in

**Data Analytics and Statistics.**

Throughout this program, you will learn the** concepts of statistics and data analytics** and how to perform data analysis using R. The first course of this program will teach the** Probability** basics and the second course is all about** inferential statistics.** In this second course, you will get to know about **Central Limit Theorem and Confidence Interval**.

The last course will teach **Regression** and cover **Linear Regression and Multiple Regression. **Along with the theoretical concepts, you will work on **quizzes and exercises** in all three courses.

These are the 3 courses-

**Courses Include-**

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

**Pros-**

- You will get a
**Shareable Certificate and Course Certificates**upon completion. - Provides
**Practice Quizzes and Graded Assignments with Peer Feedback**.

**Cons-**

- The program doesn’t provide enough knowledge about R Programming.

**You Should Enroll if-**

- You have
**basic math knowledge**. It is good to have previous R Programming knowledge.

**Interested to Enroll?**

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

**9. Probability and Statistics- Coursera**

Rating- | 4.6/5 |

Provider- | University of London |

Time to Complete- | 18 Hours |

FREE/Paid– | Paid |

This **Probability and Statistics** course is especially dedicated to Probability and Statistics. This is a **basic course** that covers Probability and Statistics. This course is **more theoretical in nature.**

This course will clear your **doubts regarding Probability and Statistics. **The explanation method was unique and helpful. This course has **video tutorials and reading materials.** That means you have to read some extra materials after each module.

This course uses **various helpful examples** to explain the topic which is helpful for students.

**Pros-**

- You will get a
**Shareable Certificate**upon completion. - For beginners, this course is good to understand Probability and Statistics using examples.

**Cons-**

- Only covers theory about Statistics and Probability.

**You Should Enroll if-**

- You are a beginner and want to learn Probability and Statistics.

**Interested to Enroll?**

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

**10.** **Intro to Descriptive Statistics– Udacity**

Rating- | NA |

Provider- | Udacity |

Time to Complete- | 2 Months |

FREE/Paid– | FREE |

This **Intro to Descriptive Statistics** is another **Free **statistics course at Udacity. This course will teach how to **compute a value from data by using Google Spreadsheets.**

You will also learn how to** gather, organize, compute, and visualize **information from a dataset. This course also has one final project, where you will use the tools you learned in the course to compute statistics hidden within a deck of cards.

There is a separate tutorial on **Google Spreadsheet and Central Tendency.** This course has **quizzes and problem sets** to get hands-on practice.

Overall, this course is good for learning** descriptive statistics fundamentals.**

**Pros-**

- This is a FREE course and you don’t need to pay.
- You will learn advanced topics and work on hands-on practices.

**Cons-**

- You will not receive a certificate after completing the course.

**You Should Enroll if-**

- You have an understanding of basic algebra and arithmetic.

**Interested to Enroll?**

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

**11. Mathematical Foundation For Machine Learning and AI- Udemy**

Rating- | 4.0/5 |

Provider- | Eduonix Learning Solutions |

Time to Complete- | 4.5 hours |

FREE/Paid– | Paid |

This **Mathematical Foundation For Machine Learning and AI** course covers three main mathematical theories: **Linear Algebra, Multivariate Calculus, and Probability Theory.**

But this course is** not a very detailed course** to learn these topics. This course is good for revising your concepts. The instructor will teach **Scalars, Vectors, Derivatives, Gradients, and Probability.**

I found that this is a **short course that only covers basics.** If you want a detailed course, then this course is not for you.

**Pros-**

- You will get a
**Certificate of completion**and**6 downloadable resources**. - And you will get lifetime access to the course material.

**Cons-**

- The course doesn’t use the latest version of Python.

**You Should Enroll if-**

- You want to
**refresh or learn the mathematical tools required for AI and machine learning.**

**Interested to Enroll?**

If yes, then check out all details here- **Mathematical Foundation For Machine Learning and AI**

**12. Calculus 3 (Multivariable calculus)- Udemy**

Rating- | 4.8/5 |

Provider- | Hania Uscka-Wehlou |

Time to Complete- | 47.5 hours |

FREE/Paid– | Paid |

This ** Calculus 3 (Multivariable calculus)** is another best course available on Udemy for **Multivariable calculus. **This course covers **advanced calculus topics**. The** instructor’s explanation** makes complex topics easier.

This course is the perfect balance between** theory and practice.** And the content quality of this course is perfect. Before enrolling in this course, you must have previous knowledge of **Calculus 1, Calculus 2, and Linear Algebra.** Overall, this course is good for you if you want to gain a thorough understanding of calculus.

**Pros-**

- You will get a
**Certificate of completion.** - Well-structured content.
- Along with this, you will get
**300 downloadable resources**and**full lifetime access**to the course material.

**Cons-**

- You might feel the pace of teaching is a bit fast.

**Who Should Enroll?**

- You are an engineering student or a machine learning enthusiast.

**Interested to Enroll?**

If yes, then check out all details here- **Calculus 3 (Multivariable calculus)**

So, that’s all. These are the **12** **Best Math Courses for Machine Learning****and Data Science**. Now, it’s time to wrap up.

**Conclusion**

I hope these courses will give you a strong understanding of math concepts. My aim is to provide you with the best resources for Learning. If you have any doubt or questions, feel free to ask me in the comment section.

**Comparison of Best Math Courses** **for Machine Learning/Data Science**

S/N | Course Name | FREE/Paid | Rating | Provider | Time to Complete |

1. | Mathematics for Machine Learning Specialization | Paid | 4.4/5 | Imperial College London | 4 Months |

2. | Intro to Statistics | FREE | NA | Udacity | 2 Months |

3. | Linear Algebra Refresher Course | FREE | NA | Udacity | 4 Months |

4. | Data Science Math Skills | Paid | 4.5/5 | Duke University | 13 hours |

5. | Introduction to Calculus | Paid | 4.8/5 | The University of Sydney | 51 Hours |

6. | The Math of Data Science: Linear Algebra | Paid | NA | edX | 8 Weeks( If you spend 6-8 hours per week) |

7. | Probabilistic Graphical Models Specialization | Paid | 4.6/5 | Stanford University | 4 Months |

8. | Statistics with R Specialization | Paid | 4.6/5 | Duke University | 7 Months |

9. | Probability and Statistics | Paid | 4.6/5 | University of London | 18 Hours |

10. | Intro to Descriptive Statistics | FREE | NA | Udacity | 2 Months |

11. | Mathematical Foundation For Machine Learning and AI | Paid | 4.0/5 | Udemy | 4.5 hours |

12. | Calculus 3 (Multivariable calculus) | Paid | 4.9/5 | Udemy | 47.5 hours |

**My Recommendation**

Best Math Course for Beginners | Mathematics for Machine Learning Specialization |

Best Math Course for Advanced Learners | Probabilistic Graphical Models Specialization |

Best Linear Algebra Course | Linear Algebra Refresher Course (FREE) |

Best Statistics Course(For Beginners) | Intro to Statistics (FREE) |

Best Calculus Course | Calculus 3 (Multivariable calculus) |

Tell me in the comment section, which is the ** Best Math Course for Machine Learning** and Data Science?

All the Best!

Happy 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.