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Master the world of machine learning and data science with this comprehensive beginner's bundle. Data Science and Machine Learning are the biggest buzzwords in the business world nowadays. If you want to learn more about Machine Learning and Data Science or how to master them with Python quickly and easily - we have the answer! Machine Learning is the key to learning Python for machine learning, artificial intelligence, and data science. This is your guide to the future of how we do business! In this book, you will discover: What is a data scientist? What languages should be learned? The three musketeers of Data Science Python introduction Languages do you need to learn for data science These are some of the topics covered in this book: Machine Learning Algorithms K NN - Nearest Neighbor Method SVC - support vector machine Mathematics for Data Analysis Working with Threads in Python The following are the objectives of this book: To help you understand deep learning in detail To help you know how to get started with deep learning in Python by setting up the coding environment. To help you transition from a deep learning Beginner to a Professional. Model in Python on your own. And more Get this book now to learn more about -- Deep Learning in Python by setting up the coding environment and learn the Secrets of Machine Learning, Data Science Analysis, and Artificial Intelligence)
This book aims to demystify machine learning and data science for beginners using Python. It addresses the growing importance of these fields in the business world and provides a roadmap for those seeking to master them. The text guides readers through understanding the role of a data scientist, essential programming languages, and the core concepts of machine learning, including algorithms like K-NN and SVC, and the mathematics underpinning data analysis.
The book is presented as a comprehensive guide for beginners entering the fields of machine learning and data science with Python. It aims to provide a foundational understanding of key concepts, algorithms, and the necessary tools for practical application. The structure suggests a progression from defining roles and necessary skills to delving into specific algorithms and coding environments, with the objective of enabling readers to build their own models.
Page Count:
317
Publication Date:
2021-02-12
Publisher:
Independently Published
ISBN-13:
9798708480156
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