This cheat sheet is a condensed version of machine learning manual, which contains many classical equations and
diagrams on machine learning, and aims to help you quickly recall knowledge and ideas in machine learning.

 An introduction to machine learning book will get you started with various data science techniques such as decision trees, performance evaluation, among others. It also covers sub-categories such as unsupervised learning, reinforcement learning, and neural networks. Learners can obtain a detailed understanding of various classifiers and algorithms from 17 chapters, thereby making it a good read during the lockdown.

 A proper grasp of statistics is essential for any machine learning enthusiast to succeed in the competitive domain. Consequently, one should focus more on statistics than on the latest fancy techniques. The book — All of Statistics — consists of 24 chapters and covers every topic right from probability to statistical inference and statistical models and methods.

 Time series analysis has gain popularity due to its use cases in financial data, especially, in companies’ stock prediction, fraud detection in transactions, and more. Besides, it is now becoming an essential technique in data science due to the rise of streaming analytics in numerous business operations. Obtaining real-time insights while also comparing with the historical trends allow companies to quickly make informed decisions. 

The book includes 11 chapters on a wide range of time series techniques such as nonstationary and seasonal time series models, multivariate time series, state-space models, and forecasting methods.

 With more than 2.3 million downloads, this is one of the most popular data science books. The process of data mining has increasingly become essential for businesses to achieve rapidly grow by making decisions based on insights. Therefore, this book includes all principles for data mining to discover patterns in a colossal amount of data. Designed to help even the beginners, it covers an introduction to classification like Naive Bayes, Nearest Neighbour, Decision Tree, among others, along with detailed explanations.

 It is a comprehensive book on deep learning for every aspirant as well as working professionals. The book covers both classical as well as the latest models in deep learning, thereby explaining the difference between several techniques and their effectiveness. The book also focuses on the right practices for AI agents to generalise. Besides, it has lessons on various neural networks like RNN, CNN, DRL, other advanced topics in deep learning.

 Today, companies are delivering personalised experiences to their users for enhancing customer experience. Such practices are widely common on social media platforms and e-commerce websites, however, now it is being democratised across all business to increase conversion rate. The book has categorised the learning in three sub-section: algorithms and evaluation, recommendations in specific domains and contexts, advanced topics and applications. 

 Computer vision technology is playing a crucial role in data science by expediting various processes such as analysing medical imaging, assisting in the development of self-driving cars, and determining defects in manufacturing processes. It can be applied in endless processes to simplify the life of humans. However, there are various challenges in computer vision technology while interpreting 3D images as well as delivers biased results. This book explains the ins and outs of the computer vision along with lessons on various techniques.