Pro Machine Learning Algorithms: 1st Edition free pdf download

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A wide range of applications using pro machine learning algorithms techniques. As methodologies for machine learning become more widely used, it is crucial for the creators of machine learning applications to understand what the underlying algorithms are learning and, more importantly, how the different algorithms are deriving patterns from the original information in order to maximize their efficiency.

Author: V Kishore Ayyadevara
Edition: 1^st Edition
Publisher: Apress
Published: July 1, 2018
Language: English
Pages: 379 Pages
File Size: 22 MB
ISBN: 978-1484235645

The target audience for this book is data scientists and analysts who are curious about the inner workings of different machine learning algorithms. The knowledge and abilities you get from this book will help you construct the most important predictive models for machine learning and evaluate models that are given to you. This book considers an AI & ML book which is one of the General books.

We first develop the algorithms in Excel so that we may take a peep inside the procedures’ mysterious black box in order to understand what the machine learning algorithms are learning and how they are learning it.

This teaches the reader how different algorithmic levers affect the end outcome. After understanding how the algorithms operate, we put them into practice using Python and also R. But given that this isn’t a book on Python or R, I’m assuming that the reader is at least somewhat conversant with programming. Having said that, the appendix explains the fundamentals of Excel, Python, and also Rare.

About V Kishore Ayyadevara:

A group led by V Kishore Ayyadevara focuses on applying AI to address issues in the healthcare industry. He has worked for well-known technological businesses for more than 10 years in the data science industry. In his present position, he is in charge of creating powerful technical teams and also a range of cutting-edge analytical solutions that have an impact at scale. In the nexus of machine learning, healthcare, and also operations, Kishore has eight patent applications. He has written four books on machine learning and deep learning prior to this one. Kishore received his engineering degree from Osmania University and also his MBA from IIM Kolkata.

Senior data scientist Yeshwanth Reddy has a significant interest in the investigation and application of cutting-edge technologies to address issues in the fields of medicine and also computer vision. In the area of OCR, he has four patent applications. In his two years of teaching, he also instructed hundreds of students in the subjects of statistics, machine learning, artificial intelligence, and natural language processing. At IIT Madras, he earned both his MTech and BTech degrees.

Free download:

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The core language of data science is introduced in Chapter 1 along with the normal project methodology. Some of the most important supervised machine learning and deep learning algorithms used in business are covered in Chapters 2 through 10. The principal unsupervised learning methods are covered in Chapters 11 and 12. We put into practice the many methods recommender systems employ to foretell whether a user would appreciate a certain item in Chapter 13. Lastly, Chapter 14 examines how to use Google Cloud Platform, Microsoft Azure, and Amazon Web Services, the three main cloud service providers.

About Book:

To better adjust your models, fill in the knowledge gap between a high-level grasp of an algorithm's operation and the technical details. You will gain the knowledge and abilities needed to create all the key machine-learning models after reading this guide. Before applying the models in Python/R in Pro Machine Learning Algorithms, you will first create the algorithm in Excel to gain a real grasp of all the variables that can be adjusted in a model.

You will study both supervised and unstructured learning methods, including decision trees, random forests, GBMs, linear/logistic regression, k-means clustering, PCA, recommender systems, and neural networks. Through the use of CNNs, RNNs, and word2vec for text extraction, you will also be introduced to the newest developments in deep learning.

In order to improve a model's efficiency, you will learn feature engineering principles in addition to the methods themselves. For the vast majority of machine learning methods used in industry, you will see both theory and case studies, including sentiment classification, fraud detection, recommender systems, and picture identification. This will give you the best of both worlds.

You will be introduced to operating machine-learning models on all the main cloud service providers in addition to learning algorithms.

You are supposed to have a basic understanding of statistics and computer programming, and by the conclusion of this book, you ought to be confident enough to work on a machine learning assignment. If you want to read about the Introduction of Machine Learning 2nd Edition which gives you the basic concept of machine learning.

What Will You Learn?

Learn everything there is to know about the most important pro machine learning and deep learning techniques.

Recognize the dangers to watch out for when creating models.
Utilize cloud-based machine-learning techniques
Learn ensemble learning techniques to create models that are more precise.
Learn the fundamentals of R/Python computing and the Keras deep learning system.