In 2019, MIT’s Katie Bouman processed five petabytes of data to develop the first-ever image of a black hole. Data science, machine learning, and artificial intelligence played a central role in this extraordinary discovery.

Data is the new electricity, and as per HBR, data scientist is the “sexiest” job of the 21st century. Data is fueling business decisions and making its impact felt across all sectors and walks of life. It is allowing us to create intelligent products, improvise marketing strategies, innovate business strategies, enhance safety mechanisms, arrest fraud, reduce environmental pollution, and create path-breaking medicines. Our everyday life is enriched and our social media interactions are more organized. It is allowing us to reduce costs, increase profits, and optimize operations. It offers a fantastic growth and career path ahead, but there is a dearth of talent in the field.

This book attempts to educate the reader in a branch of machine learning called supervised learning. This book covers a spectrum of supervised learning algorithms and respective Python implementations. Throughout the book, we are discussing building blocks of algorithms, their nuts and bolts, mathematical foundations, and background process. The learning is complemented by developing actual Python code from scratch with step-by-step explanation of the code.

The book starts with an introduction to machine learning where machine learning concepts, the difference between supervised, semi-supervised, and unsupervised learning approaches, and practical use cases are discussed. In the next chapter, we examine regression algorithms like linear regression, multinomial regression, decision tree, random forest, and so on. It is then followed by a chapter on classification algorithms using logistic regression, naïve Bayes, knn, decision tree, and random forest. In the next chapter, advanced concepts of GBM, SVM, and neural network are studied. We are working on structured data as well as text and image data in the book. Pragmatic Python implementation complements the understanding. It is then followed by the final chapter on end-to-end model development. The reader gets Python code, datasets, best practices, resolution of common issues and pitfalls, and pragmatic first-hand knowledge on implementing algorithms. The reader will be able to run the codes and extend them in an innovative manner, as well as will understand how to approach a supervised learning problem. Your prowess as a data science enthusiast is going to get a big boost, so get ready for these fruitful lessons!

The book is suitable for researchers and students who want to explore supervised learning concepts with Python implementation. It is recommended for working professionals who yearn to stay on the edge of technology, clarify advanced concepts, and get best practices and solutions to common challenges. It is intended for business leaders who wish to gain first-hand knowledge and develop confidence while they communicate with their teams and clientele. Above all, it is meant for a curious person who is trying to explore how supervised learning algorithms work and who would like to try Python.

Stay blessed, stay healthy!