Machine Learning Cookbook with Python Create ML and Data Analytics Projects Using Some Amazing Open Datasets

building real-world projects
Key Features
● Learn how to handle an entire Machine Learning Pipeline supported with adequate mathematics.
● Create Predictive Models and choose the right model for various types of Datasets.
● Learn the art of tuning a model to improve accuracy as per Business requirements.
● Get familiar with concepts related to Data Analytics with Visualization, Data Science and Machine Learning.
Description
Machine Learning does not have to be intimidating at all. This book focuses on the concepts of Machine Learning and Data Analytics with mathematical explanations and programming examples. All the codes are written in Python as it is one of the most popular programming languages used for Data Science and Machine Learning. Here I have leveraged multiple libraries like NumPy, Pandas, scikit-learn, etc. to ease our task and not reinvent the wheel. There are five projects in total, each addressing a unique problem. With the recipes in this cookbook, one will learn how to solve Machine Learning problems for real-time data and perform Data Analysis and Analytics, Classification, and beyond. The datasets used are also unique and will help one to think, understand the problem and proceed towards the goal. The book is not saturated with Mathematics, but mostly all the Mathematical concepts are covered for the important topics. Every chapter typically starts with some theory and prerequisites, and then it gradually dives into the implementation of the same concept using Python, keeping a project in the background.
What will you learn
● Understand the working of the O.S.E.M.N. framework in Data Science.
● Get familiar with the end-to-end implementation of Machine Learning Pipeline.
● Learn how to implement Machine Learning algorithms and concepts using Python.
● Learn how to build a Predictive Model for a Business case.
Who this book is for
This cookbook is meant for anybody who is passionate enough to get into the World of Machine Learning and has a preliminary understanding of the Basics of Linear Algebra, Calculus, Probability, and Statistics. This book also serves as a reference guidebook for intermediate Machine Learning practitioners.
Table of Contents
1. Boston Crime
2. World Happiness Report
3. Iris Species
4. Credit Card Fraud Detection
5. Heart Disease UCI