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Machine Learning - Getting Started Launch yourself into machine learning!

      Author: Baturi   |   11 August 2022   |   comments: 0

Machine Learning - Getting Started Launch yourself into machine learning!
Machine Learning - Getting Started: Launch yourself into machine learning! by Ananda Soundhararajan
English | 2022 | ISBN: N/A | ASIN: B0B6NJZ26M | 182 pages | PDF | 4.54 Mb
If yes, then machine learning is the skill you need to develop!


Maybe you have tried learning machine learning before but found it too difficult, or perhaps you are not sure if machine learning is the correct next step for you. Well, this book covers all the fundamentals of machine learning to give you an intuitive understanding of the subject, so by the end of this book, you will find that machine learning is easier than you thought, and you will know if you want to get into machine learning or not.
This book is for software engineers who want to get into machine learning field. Machine learning engineers get paid more compared to traditional software engineers, they also never have a slow day given the unseen challenges and problems to solve.
What's so special about this book?
This book outlines the subject of machine learning in clear and simple English. There are easy-to-understand examples so that you can practice along at home.
Includes the next steps you should take to further your goal of becoming a machine learning engineer. Addresses the popular myths about the subject and pitfalls that you should avoid.
Simplifies the complex math and algorithm parts of machine learning and creates fascinating, fun, and intuitive ways to understand the theories required to become a machine learning engineer.
By the time you finish reading this book, you will be sure of which path to take and what subjects you should learn more about, and you will be brimming with excitement and interest in machine learning.
You will learn,The foundations of machine learningWhat the landscape of machine learning looks likeSimple explanations of statistics, probability, linear algebra, calculus, and gradient descent required for machine learningHow to explore the dataset and choose relevant featuresHow to build your own supervised models, unsupervised models, and neural networksFundamentals of natural language processingHow traditional software engineering differs from machine learningAdditional materials to develop hands-on experienceNext steps you can take to become a machine learning engineer
If you want to launch yourself into a machine learning career, this book is your first step!



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