DeepLearning.AI
Probability & Statistics for Machine Learning & Data Science
DeepLearning.AI

Probability & Statistics for Machine Learning & Data Science

This course is part of Mathematics for Machine Learning and Data Science Specialization

Taught in English

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Luis Serrano

Instructor: Luis Serrano

46,788 already enrolled

Course

Gain insight into a topic and learn the fundamentals

4.6

(314 reviews)

|

93%

Beginner level

Recommended experience

33 hours (approximately)
Flexible schedule
Learn at your own pace

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

Details to know

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Assessments

8 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.6

(314 reviews)

|

93%

Beginner level

Recommended experience

33 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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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 quizzes1 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 quizzes1 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 quizzes2 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 quizzes1 programming assignment1 ungraded lab

Instructor

Instructor ratings
4.5 (77 ratings)
Luis Serrano
DeepLearning.AI
3 Courses113,479 learners

Offered by

DeepLearning.AI

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