The right statistics and probability course for machine learning depends on your starting point: basic maths, statistical reasoning, or hands-on coding. Compare the options below by learning goal and programming language, then choose one foundation course and apply it to a small project.
Statistics and Probability Courses: Quick Comparison
These recommendations compare curriculum, prerequisites and practice format. They are not a claim that every course has been personally completed. Choose for the skill you need, rather than collecting overlapping certificates.
| Course | Best fit | Practice |
|---|---|---|
| DeepLearning.AI: Probability & Statistics | Applying probability to ML | Python labs |
| Khan Academy: Statistics and Probability | Rebuilding statistical foundations | Concepts and exercises |
| Duke: Introduction to Probability and Data with R | Exploring data and sampling | R |
| Harvard: Data Science: Probability | Probability through simulation | R |
| Imperial: Mathematics for Machine Learning | Complementary algebra, calculus and PCA | Python / NumPy for PCA |
| Udacity: Intro to Statistics | Introductory statistics and problem sets | Optional programming |
| 365 Careers: Statistics for Data Science and Business Analysis | Business statistics and regression | Conceptual focus |
| Codecademy: Learn Statistics with NumPy | Short practical statistics refresher | Python / NumPy |
My suggested starting point: choose Khan Academy or Udacity if the basic concepts are unfamiliar. If you already write Python and want an ML-focused course, consider DeepLearning.AI. Choose Duke or Harvard if you want to learn through R; choose Codecademy for a shorter NumPy refresher.
Access: free learning, free previews, trials and paid certificates are different offers. Check the linked provider before enrolling; prices and access conditions vary. Selected course details checked on November 2, 2026.
For the wider sequence, see the Foundations phase of the free 90-day ML roadmap. Already comfortable with probability? Continue to Core ML.
Which Statistics Course Should You Choose for Machine Learning?
Starting from scratch? My suggestion is to begin with Khan Academy’s statistics and probability lessons. Work through averages, variation, probability and sampling before worrying about advanced ML mathematics. Use the exercises to decide which topics need another pass.
Already comfortable with Python? Consider DeepLearning.AI’s Probability & Statistics for Machine Learning & Data Science. Its Python labs connect distributions, estimation and hypothesis testing to ML. The provider lists intermediate level and basic-to-intermediate Python requirements, so it is not my first choice for someone learning both coding and statistics from zero.
Studying around work? Choose one course and schedule two short study sessions plus one exercise session each week. That is a suggested routine, not a promise about completion time. If you mainly want a refresher, use the comparison above to select specific topics rather than starting several full courses.
Need a certificate? Check the enrollment terms separately from access to lessons. A free preview is not a free full course, and a certificate is not evidence that you can apply the material. Try a small project too: our Python bootstrap confidence interval example gives sampling and uncertainty a practical purpose.
Statistics and probability are two of the main tools of any Data Scientist or Machine Learning practitioner. Without understanding them well, it is almost impossible to make sense of how our algorithms and models work, and what they tell us.
It is also very important to master them in order to be able to translate business problems into Machine Learning solutions properly.
As we know it is not always easy to find resources for these topics, we have put together a list of on-line courses for you. Have fun, and learn a lot!
If you are already familiar with statistics and probability, go take a look at our list of Machine Learning Courses.
Not sure where statistics fits in your learning path? The free 90-Day Machine Learning Roadmap shows exactly when to study probability and statistics, and what to learn before and after. If you would rather learn by doing, Chapter 8 of the ML Starter Kit covers probability and Bayes with real Python code.
Probability & Statistics for Machine Learning & Data Science DeepLearning.AI on Coursera
Cost: Free preview of the first module; full access and certificate require payment, Coursera Plus or Financial Aid
Best fit: learners who know some Python and want to connect probability with machine learning. The course covers distributions, estimation and hypothesis testing, with Python labs. Start with an introductory statistics course if both the maths and the programming are new to you.
📘 What You’ll Learn
- Quantifying Uncertainty: Understand and describe the uncertainty inherent in predictions made by machine learning models using probability concepts, random variables, and probability distributions.
- Probability Distributions: Gain intuitive insights into commonly used probability distributions in machine learning and data science, such as Bernoulli, Binomial, and Gaussian distributions.
- Statistical Methods: Apply statistical methods like Maximum Likelihood Estimation (MLE) and Maximum A Priori Estimation (MAP) to machine learning problems.
- Model Performance Assessment: Assess the performance of machine learning models using interval estimates and margins of error.
- Hypothesis Testing: Apply concepts of statistical hypothesis testing to commonly used tests in data science, like A/B testing.
Workload: Coursera currently estimates 3 weeks at 10 hours per week. This is an overall study estimate, not a video-runtime figure; allow extra time to repeat the labs.
Prerequisites: high-school algebra and functions, plus basic-to-intermediate Python. Be comfortable with loops, functions and debugging before tackling the labs.
Stay on track. Get the Roadmap.
Download the 90-Day Roadmap PDF + get weekly tips and resources to help you succeed
Statistics and Probability by Khanacademy
Free course: This course is absolutely free. No tricks or certificates.
Description: As with most of Khan Academy’s courses, Statistics and Probability is offered through an extensive series of fun, short videos with quizzes in between where you can get points and check the level of your statistical knowledge.
They give the courses a game-like structure which makes them a lot of fun to take and also very educational. However, despite this game-like structure it is a course that covers a lot of material: from basic probability and distributions to more advanced concepts like inference or ANOVA models.
This course might be a great step after going through an Introductory Statistics book like Bayesian Statistics: The Fun way that is very theoretical, and has very little code.
Workload: self-paced. Choose the units you need and use the exercises to check understanding; there is no single completion time that fits both a quick refresher and a first introduction.
Prerequisites: It requires no formal prerequisites, other than an appropriate level of maths.
Introduction to probability and data on Coursera
Access: check the course enrollment screen for current payment, preview and financial-aid options. “Enroll for free” does not by itself confirm free access to all graded work or a certificate. Compare this R-based course with our broader machine learning courses if you are ready to move beyond foundations.
Description: This course is offered by Duke University. It is more oriented towards Data Science than other courses that can be found out there. It will introduce you to sampling and exploring data, basic probability theory and Bayes Theorem.
You will also learn how to explore and visualise data using the R software, which you will use too for the exercises and final project. It is an extremely complete course, and for many it has been the first step in their Data Science careers. It is a great alternative to books like Statistical Rethinking or Practical Statistics for Data Scientists.
Workload: Coursera currently estimates 2 weeks at 10 hours per week. The course has eight modules; budget time for the R exercises and final project, not just the videos.
Prerequisites: the provider lists beginner level with no prior experience required. R and RStudio are introduced in the course.
Data Science: Probability on edx
Free course: This course is free if you don’t want the shiny certificate at the end.
Best fit: learners who want a focused probability course using R. Topics include random variables, independence, expected values, Monte Carlo simulation and the central limit theorem. Treat it as probability preparation rather than a complete statistics or machine learning curriculum.
It also covers some practical methodologies like Monte Carlo simulations along with theoretical insights like the Central Limit Theorem. Exercises and lessons are covered in R, so you will also get a nice introduction into this programming Language.
For Python practice afterwards, use the Python Data Science Handbook to recreate a simulation or visualisation. Translating one example from R to Python is a useful way to check whether you understand the concept.
Workload: Harvard lists eight weeks at 1–2 hours per week, with self-paced study.
Starting level: Harvard labels the course introductory. It uses R, so familiarity with basic R syntax will make the practical work easier.
Mathematics for Machine Learning Specialisation by Imperial College London on Coursera
Cost: Coursera replaced its free “Audit” option in 2025 with a free preview of each course’s first module (see our Coursera guide). Full access, including the assignments we consider essential, requires paying or applying for Financial Aid.
Best fit: learners who need the mathematical tools behind ML: linear algebra, multivariate calculus and PCA. This is complementary preparation, not a substitute for a probability and statistics course.
You will also be provided with examples regarding the use of the mathematical tools that are used to design and that serve as the foundation of Machine learning Techniques like Principal Component Analysis (PCA), Bayesian Linear Regression and Support Vector Machines (SVMs)
The specialisation is divided into three courses:
In the first course on Linear Algebra you will learn what linear algebra is and how it relates to data. Then you will get comfortable with vectors and matrices, and learn how to work with them. The second course, Multivariate Calculus, builds on this to look at how to optimise fitting functions to get good fits to data.
It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting. Lastly, the third course covers Dimensionality Reduction with Principal Component Analysis and uses the mathematics from the first two courses to compress high-dimensional data.
At the end of this specialisation you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.
We think that if you don’t have a strong mathematical background, and want to get into Machine Learning this specialisation is a very good point to take you from beginner to medium/high level. The courses can be take individually instead of in the specialisation format, so we advise at least to take the first one in order to be able to grasp the maths of Machine Learning. The individual courses can be found on the following links:
- Mathematics for Machine Learning: Linear Algebra
- Mathematics for Machine Learning: Multivariate Calculus
- Mathematics for Machine Learning: PCA
Workload: Coursera currently estimates four weeks at 10 hours per week for the three-course specialisation. Allow more time if you are learning Python or revisiting calculus alongside it.
Prerequisites: the specialisation is listed as beginner level, but the PCA course is intermediate and requires Python and NumPy knowledge. Check the requirements of each course individually.
Intro to Statistics on Udacity
Access: Udacity currently lists Intro to Statistics as a free course. Check its enrollment page for current access and any completion-document terms.
Description: The intro to statistics course on Udacity (also known as Statistics 101) is, as its name says, a beginner statistics course that covers data visualisation, probability and many elementary statistics concepts like regression, hypothesis testing and more.
It is a self paced course with a final exam to check your knowledge on the probability and statistics learned throughout the different chapters.
Specifically it covers: Visualisation and relationships in data, Probability with Bayes Rule and Correlation vs Causation, estimation with Maximum Likelihood, and an introduction to concepts that are central to statistics like the mean, median and mode, a whole chapter on outliers and distributions, and two final blocks covering statistical inference and regression analysis.
Sebastian Thrun, the professor is engaging, bright and fun, making the lectures pass by with ease, while increasing the retention of the learned concepts. Each chapter contains assignments that are a must do, and will guide you to making the most out of the course.
Workload: the current syllabus contains 44 lessons, including problem sets and a final exam. Study at your own pace and allow time to solve the exercises.
Prerequisites: Udacity lists no prior technical experience required. Several programming exercises are optional.
Statistics for Data Science and Business Analysis on Udemy
Cost: paid course. Udemy prices and promotions vary by country and date, so check the current checkout price rather than assuming a fixed discount.
Description: This Udemy course covers the fundamental of statistics as well with how to plot different types of data, calculating correlation and covariance, estimating confidence intervals, and overall the basics of how to use data to make better business decisions.
Fit for ML: use this as a conceptual introduction to descriptive statistics, inference and regression. If your priority is coding statistical calculations in Python, pair it with practical NumPy exercises.
Duration: It has 5 hours of under demand video, 28 articles, and a lot of further resources. We recommend taking it slow, absorbing the content little by little. Even doing so, the course should not take more than 2 weeks if you dedicate a little time to it.
Prerequisites: Absolutely no experience is required. This is a basic statistics course that starts very easy and gradually builds up your knowledge. Everything you need is in the course. It is a great resource for consuming before books like Practical Statistics for Data Science. It will get you just to the level you need to fully enjoy these kind of texts, and take your knowledge to the next level.
Learn Statistics with Numpy
Access: This course is listed on Codecademy. Check which lessons and projects your plan includes; the completion certificate requires Plus or Pro.
Description: For those looking for a very practical course, where you will be coding hand to hand the theoretical explanations of the main statistical concepts, then this is the course for you.
Learn Statistics with Numpy, hosted on the Codeacademy, is as the name of the platform says, a coding course where you will go through practical example of the main concepts of probability and statistics while you code in one of the most famous Python Libraries.
You will learn how to use Numpy for statistical management and calculations, the basic data types of the library and how to operate with them, the different metrics such as mean, medians, and standard deviations and get tested at the end of each section with quizzes and exercises that will consolidate the knowledge of what you have learned.
Lastly, you will learn how to create visualisations to summarise large quantities of numerical data.
Workload: Codecademy currently estimates four hours, with three projects. Allow additional time to repeat calculations on your own data.
Prerequisites: Codecademy labels this course intermediate and recommends completing Learn Python 3 first. You can find awesome Python books here, and free tutorials on our tutorials category.
What Statistics Should You Learn Before Machine Learning?
Focus first on interpreting distributions, conditional probability, sampling, uncertainty and evaluation. You can begin simple ML projects while strengthening these foundations; you do not need to finish every course on this page.
A Practical Learning Order
1. Describe data. Calculate the mean, median, standard deviation and quantiles. Plot a histogram and explain why the mean and median can differ.
2. Reason about probability. Work through conditional probability and Bayes’ rule. Simulate an event repeatedly and compare the observed frequency with your theoretical calculation.
3. Understand samples and uncertainty. Learn how sampling affects estimates, what confidence intervals mean, and why correlation alone does not establish causation.
4. Evaluate predictions. Use a held-out dataset and a simple baseline. Our Brier score tutorial explains probability prediction error; the lift curve tutorial examines ranking performance. They answer different questions.
Should You Learn Statistics in Python or R?
Use Python if it is the language you plan to use for ML projects. Choose an R-based course if its teaching style or statistical focus suits you better: the probability concepts transfer, although you will need separate practice with Python libraries.
Can You Start with a Free Statistics Course?
Yes. Khan Academy and Udacity offer introductory study options, and Harvard lists a free audit option for Data Science: Probability. Check assessment and certificate access separately. A free course plus completed exercises is a useful starting point; choose paid access when the structure or feedback meets a specific need.
Continue Your Machine Learning Studies
We hope that you found our section on Statistics and Probability Courses to prepare you for tackling Machine Learning Material, useful. Once you’re done here, check out our list of resources to guide on that task:
Want to practise the probability, not just read about it? Chapter 8 of the ML Starter Kit, Probability & Bayes, works through the probability you actually use in machine learning with Python code that actually ran, alongside 25 other chapters that take you from setup to a deployed model. Prefer a plan first? Start with the free 90-Day Machine Learning Roadmap.
Thank you very much for browsing through our Statistics and Probability for Machine learning section.
We hope you found the resources you were looking for. If not, we have reviewed some intro to Statistics books which you might find interesting like The Elements of Statistical Learning , An Introduction to Statistical Learning, or Statistical Rethinking, so feel free to check them out to learn all the necessary statistics for Machine Learning!
Have a great day!
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