Probability and Statistics are two intertwined topics that smoothen one’s path to becoming a Machine Learning pro. In this blog, you will find a detailed description of all you need to learn about probability and statistics for machine learning.
If you are a regular user of social media sites, you must have encountered on your timeline at least one of the memes that reflect machine learning is nothing but glamorised statistics. That is true but only to some extent. Statistical tools and the basics of probability smoothen the learning path for a beginner in machine learning, but probability and statistics cover a wide variety of topics. So, it is alright if you do not fancy exploring the depth of probability and statistics for machine learning because you don’t have to.
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The natural question is, how much probability and statistics do we need to know to get started with machine learning? If you are looking to answer that question, continue reading this article because we are about to answer in the most detailed way possible.
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Whenever we work on a project that uses a machine-learning algorithm, there are two significant steps involved. The first one is to understand the dataset, and this is where you require knowledge of statistics. The second is predicting the probability of an event, for example, estimating how likely a patient will have diabetes based on the information received from their medical tests. Thus, this suggests how significant probability and statistics are for machine learning.
So, dear ProjectPro reader, now that you understand the significance of learning probability and statistical tools in Machine Learning, let us dive into the minute details of things you need to know to understand the two domains of mathematics. The details have been divided into sections mentioned below. We have presented what each section contains so that you can proceed with reading at your convenience.
Best Statistics Book for Machine Learning: Here, you will find recommendations for books that you may go through to introduce yourself to the exciting domain of statistics.
Best Probability Book for Machine Learning: Like the previous sections, you will come across book recommendations for learning probability from scratch.
How to choose the Best Statistics Course for Machine Learning? In this section, we have prepared a list that lays out the specific topics your chosen course on statistics must-have.
How to choose the Best Probability Course for Machine Learning? This section will guide you through the learning path for understanding probability deeply. You will find a list of beginner-friendly topics that you must consider while choosing a course on probability.
How to become good at Statistics for Machine Learning?: Here, we have added a step-by-step guide to becoming a statistical analysis pro. As after learning from the books, you must try to explore exciting ways of implementing your knowledge.
How to become good at Probability for Machine Learning?: Similar to the previous one, this section contains strategies for excelling at probability. We have listed practical applications that you must attempt to master the subject.
Recommended Reading: 50 Statistic and Probability Interview Questions for Data Scientists
Let us now get started with our statistics for machine learning book recommendations list.
1) Introductory Statistics by Barbara Illowsky, Susan Dean
This statistics book is not only a hardcore statistics book, but it also introduces probability topics. It starts with explaining sampling, descriptive statistics and then moves on to probability. After that, you will come across random variables and their types, normal distribution, and the central limit theorem. The last few chapters are related to methods of hypothesis testing. Finally, it introduces one of the most popular and simplest machine learning algorithms: Linear Regression. So, you should consider this as your first book in statistics as it will assist you in becoming comfortable with complex machine learning algorithms.
Book Link: Introductory Statistics - Google Books
2) Introduction to Mathematical Statistics by Robert V. Hogg, Joseph W. McKean, Allen Thornton Craig
If you enjoy studying statistics in the standard mathematical language, then this book is for you. Although it might seem a bit terse in the beginning, as you flip through more pages, you will get used to the symbols of the author and start experiencing the beauty of mathematical statistics the book provides.
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The book starts with the basic principles of probability and then explains different distributions widely used to describe datasets. Next, it focuses on statistical inference, which plays a vital role in statistics as well as probability theory. The book also presents hypothesis tests, Bayesian statistics, non-parametric statistics, and a short chapter on linear models that hint at linear regression in machine learning.
Book Link: Introduction to Mathematical Statistics - Google Books
3) An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani
This textbook is an advanced level statistics book as it assumes that you have a basic idea of statistical methods and are now ready to enter the world of machine learning algorithms.
The book introduces basic machine learning algorithms like linear regression, logistic regression, principal component analysis, K-Nearest Neighbors, Random Forest, Decision Tree, etc., in the most beginner-friendly way. The book does not contain complicated mathematical jargon and is thus recommended to have a rough idea of machine learning algorithms. The book is downloadable for FREE; you may refer to the link below for it.
Book Link: An Introduction to Statistical Learning
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Both Statistics and Probability are closely related to each other. It is difficult to understand one without the other. Thus, in this section, you will find books that will teach you both the subjects.
1) Python for Probability, Statistics, and Machine Learning by José Unpingco
This is one book that has it all. While we have included it in this section because it nicely lays out the basics for probability, it also profoundly explains statistics and machine learning. Another exciting facet of this book is that it has an introductory section on python too. So, if you are a beginner in Data Science who wants to learn the basics of python, this book can help you there.
Take a look at the preview of this book through the link given below.
Book Link: Python for Probability, Statistics, and Machine Learning
2) Probability for Statistics and Machine Learning 2nd Edition by Anirban DasGupta
This is the book that can serve as a one-stop learning probability centre for statistics and machine learning. It has all the relevant information on the probability that a newbie needs to know. It first builds the essential elements of probability for its readers and then climbs up to complicated topics in advanced distribution theory. The exciting part of this book is that it has a section on random walks, which is an excellent way of understanding probabilistic processes. It also explains Brownian motion and Gaussian processes in detail that are pretty tempting topics in probability theory. The book’s last chapter is a friendly and brief introduction to valuable statistics and machine learning concepts that you will enjoy reading.
Book Link: Probability for Statistics and Machine Learning
3) Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy
If you are looking for a smooth transition from probability to Machine Learning, then this book is one of the best ones suited for such a purpose. It first provides you with motivation and significance for learning probability theory by introducing the basic idea of machine learning. After that, it presents the foundation’s section, which has all you need to know for learning probability to get started with machine learning. The bonus of this book is that it allows you to gradually shift machine learning algorithms and then introduce deep learning algorithms. The content in this book is well structured and will suit most readers who are new to machine learning.
Book Link: "Probabilistic Machine Learning" - a book series by Kevin Murphy
Recommended Reading:
In one of the previous sections, we mentioned books for machine learning and statistics. We will now proceed with a list of topics that you must consider if you are planning to enrol in a statistics course. These topics are important as they will comfort your ride of exploring machine learning algorithms.
Elementary Statistics: By this term, we want to reflect on the basic statistics you learn in high school. Understanding a dataset by evaluating the mean, median, mode, variance, and standard deviations is crucial for figuring out suitable machine learning algorithms for that dataset.
Bayesian Statistics: This statistics includes utilising the Bayes theorem to evaluate probabilities. It is vital to learn the Naive Bayes Classifier that is widely used in machine learning.
Statistical Inferences Methods: Hypothesis testing methods like chi-square test, R-squared value, F1-score based on precision and recall, etc are widely used by Machine learning engineers every day.
Statistical Approach to Machine Learning Algorithms: After learning general statistical techniques, you can start learning the statistics of machine learning algorithms. It will be of great help in deciding which algorithm will work for a given problem and dataset.
If you are looking for the best statistics masters for machine learning, then please make sure that the four elements mentioned above are a part of the curriculum. After you are thorough with the course, you will only have to understand how to implement them in the real world, and that is why you should read the fourth section of this blog.
Probability theory involves understanding random experiments and predicting the likelihood of a possible outcome. Thus, it will be beneficial if you grasp the theory of probability as it will better prepare you for understanding the predictions of machine learning algorithms.
Below are vital topics in probability theory that you can explore to smoothen your learning path of achieving proficiency in machine learning. If you are planning to pursue a specialised course in probability, look out for the topics mentioned below in the course structure.
Set Theory: This theory is key for learning probability theory. Most textbooks use the sets to denote the sample space of a random experiment as the symbols and notations used in set theory are quite handy in representing information concisely.
Random Variables- Discrete and Continuous: The outcomes of a real-world random experiment are represented using random variables. These outcomes can not always be quantified and may therefore be continuous and discrete. Hence, learning about evaluating probabilities for both of them will prepare you for working with practical datasets.
Different types of Distributions: There are various types of distributions that the values of a random variable can follow. The three most common distributions are Gaussian (or Normal). Poisson, and Binomial. However, there are special distributions as well that you must explore to develop a better understanding of working with complicated datasets.
Information Theory: This subject is used to represent the circumstances that affect the flow of information mathematically. It is slightly advanced, and you are not expected to learn all of it. A brief introduction to the theory will work as it will assist you in grasping the classification machine learning algorithms like Decision Tree and Random Forest quickly.
Recommended Reading: Logistic Regression vs Linear Regression Machine Learning Algorithms in Data Science
If you do a quick google search on Google for “statistical courses useful for machine learning”, you will notice plenty of options are available for it. But, most of the courses are there only to give you a theoretical perspective of the mathematical formulae used by statisticians. Often, beginners struggle with choosing the correct statistical methods even after completing long duration courses. Thus, we recommend that after you have read the books and topics we recommended, you start working on real-world problems through projects.
Here are a few project ideas that you might find useful to enhance your statistical skills.
This project should be your first project to learn the practical applications of statistics that you would have learned after going through the recommended textbooks. Market Basket Analysis involves understanding customer preferences in purchasing different products from a market (online or offline). That makes it easy for stores to recommend relevant products to their customers that they’ll likely buy after they have purchased a specific product.
Complete Solution: Market basket Analysis and Association Rule Learning
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This project aims to use the characteristics of a loan applicant to predict the probability of their successfully repaying the loan. This project is a good start for understanding the applications of univariate and multivariate distributions. By working on this project idea, you will explore the Bayesian model, Gini Index, AUC-ROC Score, Precision, Recall, F1 score, etc.
Complete Solution: Classification of Loan Applications
To understand probability in greater depth, we suggest that you work on machine learning projects and draw inferences from the predictions the algorithms make. This approach will give you a better sense of probability numbers.
Below you will find a list of project ideas that a beginner in machine learning will thoroughly enjoy.
Giant supermarket stores often utilise machine learning models for estimating their sales. These models allow them to predict the sales of each product in their stores and help them with inventory management. This project aims to analyse what the predicted probabilities are reflecting and understand which products need significantly more attention than the others.
Complete Solution: BigMart Sales Prediction Solution Python
Insurance companies need to foresee the upcoming insurance claims to keep their finances in check. And, thus they use machine learning algorithms to estimate the probabilities for insurance claims.
In this project, you will explore various statistical tools like correlation, covariance, chi-square tests, etc. and apply machine learning techniques to the given dataset to evaluate the relevant probabilities.
Complete Solution: All-State Insurance Claims Severity Prediction
We hope you enjoyed the project ideas that we discussed. Don’t be surprised if we tell you that we have more exciting projects in our library to smoothen your data science and big data journey. We have industry-relevant and solved end-to-end projects that will help you ace your career and allow you to showcase your mettle to the hiring managers. Refer to the following categories of projects to know more.
Practical Machine Learning Projects in Python for Beginners
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