Data Scientist Pocket Guide Over 600 Concepts, Terminologies, and Processes of Machine Learning and Deep Learning Assembled… (Mohamed Sabri) (Z-Library)
Author: Mohamed Sabri
艺术
Discover one of the most complete dictionaries in data science. Key Features ● Simplified understanding of complex concepts, terms, terminologies, and techniques. ● Combined glossary of machine learning, mathematics, and statistics. ● Chronologically arranged A-Z keywords with brief description. Description This pocket guide is a must for all data professionals in their day-to-day work processes. This book brings a comprehensive pack of glossaries of machine learning, deep learning, mathematics, and statistics. The extensive list of glossaries comprises concepts, processes, algorithms, data structures, techniques, and many more. Each of these terms is explained in the simplest words possible. This pocket guide will help you to stay up to date of the most essential terms and references used in the process of data analysis and machine learning. What you will learn ● Get absolute clarity on every concept, process, and algorithm used in the process of data science operations. ● Keep yourself technically strong and sound-minded during data science meetings. ● Strengthen your knowledge in the field of Big data and business intelligence. Who this book is for This book is for data professionals, data scientists, students, or those who are new to the field who wish to stay on top of industry jargon and terminologies used in the field of data science. Table of Contents 1. Chapter one: A 2. Chapter two: B 3. Chapter three: C 4. Chapter four: D 5. Chapter five: E 6. Chapter six: F 7. Chapter seven: G 8. Chapter eight: H 9. Chapter nine: I 10. Chapter ten: J 11. Chapter 11: K 12. Chapter 12: L 13. Chapter 13: M 14. Chapter 14: N 15. Chapter 15: O 16. Chapter 16: P 17. Chapter 17: Q 18. Chapter 18: R 19. Chapter 19 : S 20. Chapter 20 : T 21. Chapter 21 : U 22. Chapter 22 : V 23. Chapter 23: W 24. Chapter 24: X 25. Chapter 25: Y 26. Chapter 26 : Z About the Authors Mohamed Sabri, the author of this book, completed his graduation in Mathematics and Economics from the University o
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Data Scientist Pocket Guide Over 600 Concepts, Terminologies, and Processes of Machine Learning and Deep Learning Assembled Together Mohamed Sabri www.bpbonline.com
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FIRST EDITION 2021 Copyright © BPB Publications, India ISBN: 978-93-90684-97-7 All Rights Reserved. No part of this publication may be reproduced, distributed or transmitted in any form or by any means or stored in a database or retrieval system, without the prior written permission of the publisher with the exception to the program listings which may be entered, stored and executed in a computer system, but they can not be reproduced by the means of publication, photocopy, recording, or by any electronic and mechanical means. LIMITS OF LIABILITY AND DISCLAIMER OF WARRANTY The information contained in this book is true to correct and the best of author’s and publisher’s knowledge. The author has made every effort to ensure the accuracy of these publications, but publisher cannot be held responsible for any loss or damage arising from any information in this book. All trademarks referred to in the book are acknowledged as properties of their respective owners but BPB Publications cannot guarantee the accuracy of this information. Distributors:
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BPB PUBLICATIONS 20, Ansari Road, Darya Ganj New Delhi-110002 Ph: 23254990/23254991 MICRO MEDIA Shop No. 5, Mahendra Chambers, 150 DN Rd. Next to Capital Cinema, V.T. (C.S.T.) Station, MUMBAI-400 001 Ph: 22078296/22078297 DECCAN AGENCIES 4-3-329, Bank Street, Hyderabad-500195 Ph: 24756967/24756400
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BPB BOOK CENTRE 376 Old Lajpat Rai Market, Delhi-110006 Ph: 23861747 Published by Manish Jain for BPB Publications, 20 Ansari Road, Darya Ganj, New Delhi-110002 and Printed by him at Repro India Ltd, Mumbai www.bpbonline.com
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Dedicated to My father and our Sundays…
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About the Author Mohamed the author of this book, completed his graduation in Mathematics and Economics from the University of Ottawa. He is a Managing Partner and Consultant in the field of Data Science and MLOps, and is working with the North American organizations in the Banking, Retail, and Gaming sector. With an irrefutable passion for Data Science, he is driven to do more for the domain by being involved in a range of innovative AI projects that help him deliver end-to-end solutions in the field of AI. He drives his professional journey with his excellent communication skills and his expertise in Tech popularisation for complex projects. Building upon his commitment towards ensuring work and team cohesiveness, he has successfully executed several AI projects. In his book, “Data Scientist Pocket Guide”, he has interestingly poured his secrets of becoming a benevolent data scientist. His secret passion for connecting and networking with people and professionals is channelled through this book, that attempts to connect and reach several data scientists and make their everyday job enriching and easier.
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About the Reviewer Prateek Gupta is a Data Enthusiast and loves data-driven technologies. Prateek has done his B.Tech in Computer Science & Engineering and currently working as a Data Scientist in an IT company. Prateek has a total of 10 years of experience in the software industry, and currently, he is working in the Computer Vision area. Prateek is also author of the book “Practical Data Science with Jupyter” 2 nd Edition published by the BPB Publications.
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Acknowledgements The completion of this book could not have been possible without the support of BPB Publications. I would like to thank all the team members of BPB Publications; despite the COVID crisis, they extended their full support with access to all the resources that were critical in completing this book. I would like to thank my family for their support and encouragement while writing this book, with a special mention to my parents for being an incredible source of inspiration in my life. A huge thanks to my father for teaching me how to be patient and resilient in life. Lastly, I would like to underline the importance of patience in writing a book or facing any challenge in life.
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Preface At the beginning of my career as a data scientist, I use to go on search engines and use various sources to find explanations about a concept in data science. This was time consuming and the answers to my questions where not always reliable. It is hard for any data scientist to find quickly all the answers to his questions and sometimes answers vary from a source to another. Also, some concepts are hard to understand so you have to find a source that explains clearly what a concept means. This book is a first of a kind dictionary or glossary that regroups the most popular terms in data science. It helps data scientist from beginners to senior to look for definitions very quickly and have reliable answers to their questions. Usually books in data science focuses on coding and on practical use cases, whereas this book goal is to explain concepts and give a better idea to data scientist about what the words means. It’s good to be able to code in data science and build machine learning models but if the data scientist doesn’t understand the logic and the mechanism behind each concept it is hard for him to provide good results and explain its work. I hope you will keep this book as your Bible for data science and use it each time you have doubt about a concept’s meaning. Have fun! This book is separated into two sections. The first section is composed of 26 chapters, each chapter correspond to a letter in the alphabet and a set of definitions in each chapter. The second section is an FAQ or frequently asked questions and it contains all the questions that a data scientist might have when it comes
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to data science, the questions covers some theorical parts and others are more practical such as “should I learn R or Python?”. This book objective is not be read all at once but to become your data science Bible, so each time you might have a question about a concept and wondering how it works or what does it mean you might look at the book for answers. Also, this book is a good support for beginners that are always confused around all the concepts that they might find in data science. So, the lecture of this book is not linear you might start to read wherever you want and jump to any chapter based on the answers you are looking for. This book is a first of a kind in data science as no other book regroup as much terms in the field as this book does.
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Downloading the coloured images: Please follow the link to download the Coloured Images of the book: https://rebrand.ly/i9waffm Errata We take immense pride in our work at BPB Publications and follow best practices to ensure the accuracy of our content to provide with an indulging reading experience to our subscribers. Our readers are our mirrors, and we use their inputs to reflect and improve upon human errors, if any, that may have occurred during the publishing processes involved. To let us maintain the quality and help us reach out to any readers who might be having difficulties due to any unforeseen errors, please write to us at : errata@bpbonline.com Your support, suggestions and feedbacks are highly appreciated by the BPB Publications’ Family.
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Table of Contents 1. FAQ How to fine tune a machine learning algorithm? How to build deep neural network architecture? How to train a machine learning algorithm faster? Why do we normalize the input data in deep neural network? When can we consider that we did a good job in a machine learning project? When should we use deep learning instead of the traditional machine learning models? How much time does it take to become a good data scientist? How to evaluate the performance of a model? In case of a large dataset, should I sample my data or use distributed computing? How much time should I spend in data transformation? How to select the right machine learning algorithm? Should I learn R or Python? What’s the trade-off between bias and variance? What is the difference between supervised and unsupervised machine learning? What is the difference between L1 and L2 regularizations? What’s the difference between type I and type II error? What’s the difference between probability and likelihood? What’s the difference between a generative and discriminative model? Which is more important model accuracy, or model performance? How would you handle an imbalanced dataset? How do you ensure that you’re not overfitting with a model?
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What’s the “kernel trick” and how is it useful? How do you handle missing data in a dataset? What are the origins of machine learning? What is the difference between a classifier and a model? What is the difference between a parametric learning algorithm and a non-parametric learning algorithm? What is the difference between a cost function and a loss function in machine learning? What is the difference between covariance and correlation? Why did it take so long for deep networks to be invented? What are some good books/papers for learning machine learning? What are the advantages of semi-supervised learning over supervised and unsupervised learning? When should I apply data normalization/standardization? How do you deal with a machine learning problem with a large number of features? When should one use median as opposed to the mean or average? Why is “Naive” Bayes called naive? 2. A A/B testing Accuracy Action Activation function Active learning AdaBoost AdaDelta AdaGrad Adam
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Adaptive learning rate Affine layer Agent Agglomerative clustering AlexNet Algorithm Anaconda Anchor box Annotator ANOVA Apache Spark ARIMA Artificial general intelligence (AGI) Artificial intelligence Artificial narrow intelligence (ANI) Artificial super intelligence (ASI) Association learning Association rules Attention mechanism Attribute Area under the ROC Curve (AUC) Autocorrelation Autoencoder Automatic summarization Automation bias Autoregression Average pooling Average precision 3. B Backpropagation
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Backpropagation through time (BPTT) Bag of words Bagging Bar chart Base learner Baseline Batch Batch gradient descent Batch normalization Bayes’ theorem Bayesian inference Bayesian statistics Bellman equation Bernoulli distribution Bias Bias-variance trade-off Bidirectional Recurrent Neural Network Big Data Big O notation Binarization Binary classification Binary variables Binning Binomial distribution Black box model BLEU score Boosting Bootstrapping Bottleneck layer Bounding box Box plot
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Bucketing Business analytics Business intelligence 4. C Caffe Calibration Candidate generation Candidate sampling Categorical cross-entropy Categorical variable Centroid Centroid-based algorithm Chain rule Chainer Channel Checkpoints Chi-square test Chi-squared distribution CIFAR: Classification Classification threshold Classifier Clipping Cloud Clustering CNN CNTK Co-adaptation COCO Coefficient of determination
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