Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you learn using Python programming in hands-on lab exercises. As a learner in this program, you'll need basic to intermediate Python programming skills to be successful.
Probability & Statistics for Machine Learning & Data Science
Probability & Statistics for Machine Learning & Data Science
This course is part of Mathematics for Machine Learning and Data Science Specialization
Instructor: Luis Serrano
68,303 already enrolled
Gain insight into a topic and learn the fundamentals.
4.6
(463 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 33 hours
Learn at your own pace
92%
Most learners liked this course
What you'll learn
Describe and quantify the uncertainty inherent in predictions made by machine learning models
Visually and intuitively understand the properties of commonly used probability distributions in machine learning and data science
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
Skills you'll gain
Details to know
Shareable certificate
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Assessments
8 assignments
Taught in English
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This course is part of the Mathematics for Machine Learning and Data Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
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.
What's included
30 videos9 readings2 assignments1 programming assignment4 ungraded labs
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.
What's included
27 videos2 readings2 assignments1 programming assignment3 ungraded labs
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.
What's included
20 videos3 readings2 assignments2 ungraded labs
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.
What's included
22 videos8 readings2 assignments1 programming assignment1 ungraded lab
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4.6 (122 ratings)Offered by
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Frequently asked questions
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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