Probability & Statistics for Machine Learning & Data Science

Go to Course: https://www.coursera.org/learn/machine-learning-probability-and-statistics

Introduction

**Course Review: Probability & Statistics for Machine Learning & Data Science** As the field of data science continues to expand, the foundational knowledge of probability and statistics has never been more critical. If you’re looking to delve into this realm, Coursera’s "Probability & Statistics for Machine Learning & Data Science," created by DeepLearning.AI and presented by Luis Serrano, is an excellent entry point. This course not only equips you with the essential mathematical toolkit necessary for machine learning but also makes the learning process accessible for beginners. **Overview and Course Structure** The course is structured over four weeks, each focusing on critical concepts that will build your understanding progressively: 1. **Week 1 - Introduction to Probability and Probability Distributions**: You begin your journey by understanding the core principles of probability, exploring essential concepts like conditional probability and Bayes’ theorem. You’ll also dive into familiar distributions such as the Binomial and Normal distributions, setting a strong foundation for the weeks ahead. 2. **Week 2 - Describing Probability Distributions with Multiple Variables**: The second week transitions into a descriptive analysis of probability distributions. Here, you’ll learn about measures of central tendency and variance, which are vital for interpreting data. The introduction of joint and marginal distributions further expands your understanding of probability with multiple variables – a crucial skill in machine learning. 3. **Week 3 - Sampling and Point Estimation**: As you shift focus to statistics this week, you will explore essential statistical concepts such as sample versus population, the law of large numbers, and the central limit theorem. The principles of point estimation, including maximum likelihood estimation, will enhance your analytical capabilities, and you’ll also touch upon Bayesian statistics. This segment provides a practical framework for evaluating and inferring conclusions from data. 4. **Week 4 - Confidence Intervals and Hypothesis Testing**: The final week dives into interval estimation with a thorough look at confidence intervals and hypothesis testing. Understanding the p-value and various hypothesis tests, including t-tests, arms you with the knowledge to make data-driven decisions effectively. The application of hypothesis testing in A/B testing is also particularly relevant for aspiring data scientists in real-world scenarios. **Recommendation and Conclusion** This course stands out due to its structured approach and the clarity with which Luis Serrano presents the material. Each week builds logically on the previous one, allowing you to absorb complex concepts without feeling overwhelmed. The practical exercises complement the theoretical components, reinforcing your understanding through real-world application. Furthermore, the course is designed with beginners in mind. No advanced mathematical background is necessary; as long as you have a basic understanding of algebra, you'll find the material approachable and engaging. For anyone aiming to work in machine learning or data science, I highly recommend "Probability & Statistics for Machine Learning & Data Science" on Coursera. Not only will this course arm you with the essential statistical knowledge necessary for data analysis but it will also instill confidence in tackling real-world data-driven challenges. Completing this course is a significant step toward mastering the mathematics that underpins successful machine learning models and data science applications. So, if you are eager to elevate your skills and venture into the world of data science with a solid mathematical foundation, this course should be at the top of your list!

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.

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.

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.

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.