Probability & Statistics for Machine Learning & Data Science from Coursera

Mathematics for Machine Learning and Data science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning.

After completing this course, learners will be able to:

• Describe and quantify the uncertainty inherent in predictions made by machine learning models, using the concepts of probability, random variables, and probability distributions.

• Visually and intuitively understand the properties of commonly used probability distributions in machine learning and data science like Bernoulli, Binomial, and Gaussian distributions

• Apply common statistical methods like maximum likelihood estimation (MLE) and maximum a priori estimation (MAP) to machine learning problems

• Assess the performance of machine learning models using interval estimates and margin of errors

• Apply concepts of statistical hypothesis testing to commonly used tests in data science like AB testing

• Perform Exploratory Data Analysis on a dataset to find, validate, and quantify patterns.

Many machine learning engineers and data scientists struggle with mathematics. Challenging interview questions often hold people back from leveling up in their careers, and even experienced practitioners can feel held by a lack of math skills.

This specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works. Upon completion, you’ll understand the mathematics behind all the most common algorithms and data analysis techniques — plus the know-how to incorporate them into your machine learning career.

What's inside

Syllabus

Week 1 - Introduction to Probability and Probability Distributions

In this week, you will learn about probability of events and various rules of probability to correctly do arithmetic with probabilities. You will learn the concept of conditional probability and the key idea behind Bayes theorem. In lesson 2, we generalize the concept of probability of events to probability distribution over random variables. You will learn about some common probability distributions like the Binomial distribution and the Normal distribution.

Week 2 - Describing probability distributions and probability distributions with multiple variables

This week you will learn about different measures to describe probability distributions as well as any dataset. These include measures of central tendency (mean, median, and mode), variance, skewness, and kurtosis. The concept of the expected value of a random variable is introduced to help you understand each of these measures. You will also learn about some visual tools to describe data and distributions. In lesson 2, you will learn about the probability distribution of two or more random variables using concepts like joint distribution, marginal distribution, and conditional distribution. You will end the week by learning about covariance: a generalization of variance to two or more random variables.

Week 3 - Sampling and Point estimation

This week shifts its focus from probability to statistics. You will start by learning the concept of a sample and a population and two fundamental results from statistics that concern samples and population: the law of large numbers and the central limit theorem. In lesson 2, you will learn the first and the simplest method of estimation in statistics: point estimation. You will see how maximum likelihood estimation, the most common point estimation method, works and how regularization helps prevent overfitting. You'll then learn how Bayesian Statistics incorporates the concept of prior beliefs into the way data is evaluated and conclusions are reached.

Week 4 - Confidence Intervals and Hypothesis testing

This week you will learn another estimation method called interval estimation. The most common interval estimates are confidence intervals and you will see how they are calculated and how to correctly interpret them. In lesson 2, you will learn about hypothesis testing where estimates are formulated as a hypothesis and then tested in the presence of available evidence or a sample of data. You will learn the concept of p-value that helps in making a decision about a hypothesis test and also learn some common tests like the t-test, two-sample t-test, and the paired t-test. You will end the week with an interesting application of hypothesis testing in data science: A/B testing.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Introduces fundamental concepts in probability and probability distributions, making it suitable for entry-level learners

Taught by Luis Serrano, an experienced instructor in the field of machine learning

Provides a strong foundation for learners who wish to pursue careers in machine learning or data science

Emphasizes the practical application of mathematical concepts to real-world data analysis and machine learning problems

Suitable for individuals with a foundational understanding of mathematics and an interest in data science or machine learning

Completion of this course does not guarantee proficiency in machine learning or data science, as it primarily focuses on mathematical foundations

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Probability & Statistics for Machine Learning & Data Science with these activities:

Review basic algebra and calculus

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Refresh your understanding of algebra and calculus to ensure you have a solid foundation for the mathematical concepts covered in the course.

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Review the concepts of algebra, including variables, equations, and functions.
Review the concepts of calculus, including derivatives and integrals.
Solve practice problems to test your understanding.
Identify any areas where you need additional support and seek help from a tutor or online resources.

Join a study group or online forum

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Engage with other students to discuss course material, share insights, and support each other's learning.

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Find a study group or online forum related to probability and statistics.
Introduce yourself and share your goals.
Participate in discussions, ask questions, and share your knowledge.
Collaborate on projects or assignments.

Solve practice probability problems

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Practice solving probability problems to improve your understanding of the fundamental concepts of probability and probability distributions.

Browse courses on Probability

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Review the concepts of probability and probability distributions.
Find a collection of practice problems on probability.
Solve the practice problems and check your answers.
Repeat steps 1-3 until you feel confident in your understanding of probability.

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Compile a glossary of probability and statistics terms

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Create a comprehensive glossary of probability and statistics terms to reinforce your understanding of the key concepts covered in the course.

Browse courses on Probability

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Review the course materials and identify key terms.
Look up the definitions of these terms in textbooks, online resources, or glossaries.
Write down the definitions in your own words.
Organize the terms alphabetically or by topic.
Review your glossary regularly to reinforce your understanding.

Build a data visualization dashboard

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Create a data visualization dashboard that represents the probability distribution or statistical analysis of a dataset to enhance your understanding and communication of data insights.

Browse courses on Probability

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Choose a dataset and identify the probability distribution or statistical analysis to be visualized.
Select appropriate data visualization techniques, such as histograms, scatterplots, or probability plots.
Use a data visualization tool to create the dashboard.
Design the dashboard for clarity, readability, and impact.
Share your dashboard with others and get feedback.

Review 'Introduction to Probability' by Joseph K. Blitzstein and Jessica Hwang

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Read and review this comprehensive introduction to probability theory to deepen your understanding of the concepts covered in the course.

View Introduction to Probability, Second Edition on Amazon

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Read the book thoroughly, taking notes on key concepts.
Work through the practice problems and exercises in the book.
Summarize the main ideas of each chapter in your own words.
Discuss the book with classmates or a study group.

Build a probability calculator

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Create a simple probability calculator using a programming language of your choice to apply your understanding of probability concepts and practice your programming skills.

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Choose a programming language and development environment.
Design the user interface for your calculator.
Implement the probability calculations using the appropriate algorithms.
Test your calculator thoroughly and fix any bugs.
Share your calculator with others and get feedback.

Participate in a data science competition

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Apply your skills in a practical setting by participating in a data science competition that involves probability and statistics.

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Find a data science competition that aligns with your interests and skill level.
Form a team or work independently.
Analyze the data, develop models, and make predictions.
Submit your results and compare them to others.
Reflect on your performance and identify areas for improvement.

Career center

Learners who complete Probability & Statistics for Machine Learning & Data Science will develop knowledge and skills that may be useful to these careers:

Statistician

Statisticians use probability and statistics to analyze data and draw conclusions. They are employed in various industries, including healthcare, finance, and market research. This course can help Statisticians build a strong foundation for success by developing their understanding of statistical methods, data analysis techniques, and probability theory.

See salaries and explore the career path for Statistician

Machine Learning Engineer

Machine Learning Engineers use probability and statistics to design and evaluate machine learning models. They must have a deep understanding of these concepts to ensure the accuracy and reliability of their models. This course can help Machine Learning Engineers develop the mathematical toolkit they need to succeed in this demanding field.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts use probability and statistics to analyze data and extract meaningful insights. They play a crucial role in organizations that rely on data-driven decision-making, such as marketing, sales, and operations. This course can help Data Analysts enhance their analytical skills, develop their understanding of statistical methods, and become more proficient in data analysis techniques.

See salaries and explore the career path for Data Analyst

Biostatistician

Biostatisticians use probability and statistics to analyze data in the field of healthcare. This course can help Biostatisticians develop the specialized statistical skills they need to succeed in their roles, including designing clinical trials, analyzing medical data, and making evidence-based recommendations.

See salaries and explore the career path for Biostatistician

Risk Manager

Risk Managers use probability and statistics to assess and mitigate risks for organizations. They work in various industries, including finance, insurance, and healthcare. This course can help Risk Managers gain a comprehensive understanding of probability and statistical concepts, which is essential for their success in this field.

See salaries and explore the career path for Risk Manager

Data Scientist

Data Scientists leverage probability and statistics to assess risk, make predictions, and optimize decision-making within their domains. Candidates with specialized training in these quantitative areas are highly sought after for their analytical abilities and the insights they can derive from large, complex datasets. This course can help Data Scientists build a strong foundation for success by developing their understanding of probability distributions, estimation techniques, and hypothesis testing.

See salaries and explore the career path for Data Scientist

Financial Analyst

Financial Analysts use probability and statistics to analyze financial data and make investment recommendations. This course can help Financial Analysts develop the mathematical foundation they need to succeed in their roles, particularly in understanding financial data, conducting risk analysis, and making sound investment decisions.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries use probability and statistics to assess risk and determine insurance premiums. They are highly specialized professionals who work in the insurance industry and play a vital role in managing financial risks. This course can help Actuaries develop a deep understanding of probability and statistical concepts, which is essential for their success in this field.

See salaries and explore the career path for Actuary

Market Researcher

Market Researchers use probability and statistics to design and conduct surveys, analyze data, and make recommendations for marketing campaigns. This course can help Market Researchers develop the statistical skills they need to succeed in their roles, particularly in understanding data, designing research studies, and drawing meaningful conclusions.

See salaries and explore the career path for Market Researcher

Quantitative Analyst

Quantitative Analysts use probability and statistics to assess risk and make investment decisions. They play a vital role in the financial industry and are highly sought after for their expertise in mathematical modeling and data analysis. This course can help Quantitative Analysts develop the foundational knowledge they need to succeed in their complex and demanding roles.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

Operations Research Analysts use probability and statistics to optimize business processes and solve complex operational problems. They work in various industries, including manufacturing, logistics, and healthcare. This course can help Operations Research Analysts develop the analytical skills and understanding of probability and statistical techniques they need to succeed in their roles.

See salaries and explore the career path for Operations Research Analyst

Data Architect

Data Architects use probability and statistics to design and manage data systems. This course can help Data Architects gain a deeper understanding of probability and statistical concepts, which can be applied to optimize data storage, retrieval, and analysis.

See salaries and explore the career path for Data Architect

Data Engineer

Data Engineers use probability and statistics to develop and maintain data pipelines and databases. Probability and statistical concepts are used to ensure the accuracy and reliability of data, and to optimize data storage and retrieval. By taking this course, Data Engineers can develop the mathematical foundation they need to excel in their roles.

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Business Analyst

Business Analysts use probability and statistics to analyze data and make recommendations for business decisions. They work in various industries, including consulting, technology, and finance. This course can help Business Analysts develop the analytical skills they need to succeed in their roles, particularly in understanding data and drawing meaningful conclusions.

See salaries and explore the career path for Business Analyst

Software Engineer

Software Engineers may use probability and statistics to develop algorithms and models for machine learning, data analysis, and other applications. While this course may not be directly related to the core responsibilities of Software Engineers, it can provide a solid mathematical foundation for those interested in exploring these areas.

See salaries and explore the career path for Software Engineer