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Computer Science - Introduction to Machine Learning with Python: A Guide for Data Scientists

Description

Book Synopsis: Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

With this book, you’ll learn:
-Fundamental concepts and applications of machine learning
-Advantages and shortcomings of widely used machine learning algorithms
-How to represent data processed by machine learning, including which data aspects to focus on
-Advanced methods for model evaluation and parameter tuning
-The concept of pipelines for chaining models and encapsulating your workflow
-Methods for working with text data, including text-specific processing techniques
-Suggestions for improving your machine learning and data science skills.

Details

Are you ready to take your data science skills to the next level? Introducing "Introduction to Machine Learning with Python: A Guide for Data Scientists" - the ultimate book for anyone interested in machine learning. Written for Python users of all levels, this comprehensive guide will teach you everything you need to know to build your own machine learning solutions.

With the power of Python and the scikit-learn library, you'll be able to create successful machine learning applications that were once only possible for large companies with extensive research teams. Whether you're a beginner or an experienced data scientist, this book will provide you with practical ways to leverage machine learning in your projects.

No matter what type of data you're working with, this book has you covered. Learn how to represent and process data with machine learning algorithms, and discover advanced methods for model evaluation and parameter tuning. The concept of pipelines will help you streamline your workflow, while text-specific processing techniques will allow you to work with textual data effectively.

Not just limited to theory, this book focuses on the practical aspects of using machine learning algorithms. Authors Andreas Müller and Sarah Guido break down complex concepts into easy-to-understand explanations, making it accessible for anyone who wants to dive into machine learning.

Don't miss out on the opportunity to advance your machine learning and data science skills. Order your copy of "Introduction to Machine Learning with Python: A Guide for Data Scientists" today and unlock the limitless possibilities of machine learning!

Order now and embark on your journey towards becoming a machine learning expert!

Disclosure: I get commissions for purchases made through links in this website