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Machine Learning Quick Reference

Machine Learning Quick Reference PDF Author: Rahul Kumar
Publisher: Packt Publishing Ltd
ISBN: 1788831616
Category : Computers
Languages : en
Pages : 283

Book Description
Your hands-on reference guide to developing, training, and optimizing your machine learning models Key FeaturesYour guide to learning efficient machine learning processes from scratchExplore expert techniques and hacks for a variety of machine learning conceptsWrite effective code in R, Python, Scala, and Spark to solve all your machine learning problemsBook Description Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference. What you will learnGet a quick rundown of model selection, statistical modeling, and cross-validationChoose the best machine learning algorithm to solve your problemExplore kernel learning, neural networks, and time-series analysisTrain deep learning models and optimize them for maximum performanceBriefly cover Bayesian techniques and sentiment analysis in your NLP solutionImplement probabilistic graphical models and causal inferencesMeasure and optimize the performance of your machine learning modelsWho this book is for If you’re a machine learning practitioner, data scientist, machine learning developer, or engineer, this book will serve as a reference point in building machine learning solutions. You will also find this book useful if you’re an intermediate machine learning developer or data scientist looking for a quick, handy reference to all the concepts of machine learning. You’ll need some exposure to machine learning to get the best out of this book.

Machine Learning Quick Reference

Machine Learning Quick Reference PDF Author: Rahul Kumar
Publisher: Packt Publishing Ltd
ISBN: 1788831616
Category : Computers
Languages : en
Pages : 283

Book Description
Your hands-on reference guide to developing, training, and optimizing your machine learning models Key FeaturesYour guide to learning efficient machine learning processes from scratchExplore expert techniques and hacks for a variety of machine learning conceptsWrite effective code in R, Python, Scala, and Spark to solve all your machine learning problemsBook Description Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference. What you will learnGet a quick rundown of model selection, statistical modeling, and cross-validationChoose the best machine learning algorithm to solve your problemExplore kernel learning, neural networks, and time-series analysisTrain deep learning models and optimize them for maximum performanceBriefly cover Bayesian techniques and sentiment analysis in your NLP solutionImplement probabilistic graphical models and causal inferencesMeasure and optimize the performance of your machine learning modelsWho this book is for If you’re a machine learning practitioner, data scientist, machine learning developer, or engineer, this book will serve as a reference point in building machine learning solutions. You will also find this book useful if you’re an intermediate machine learning developer or data scientist looking for a quick, handy reference to all the concepts of machine learning. You’ll need some exposure to machine learning to get the best out of this book.

Data Science Quick Reference Manual – Deep Learning

Data Science Quick Reference Manual – Deep Learning PDF Author: Mario A. B. Capurso
Publisher: Mario Capurso
ISBN:
Category : Computers
Languages : en
Pages : 261

Book Description
This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. Part in a series of texts, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. As this text uses Orange for the application aspects, it describes its installation and widgets. The data modeling phase is considered from the perspective of machine learning by summarizing machine learning types, model types, problem types, and algorithm types. Deep Learning techniques are described considering the architectures of the Perceptron, Neocognitron, the neuron with Backpropagation and the activation functions, the Feed Forward Networks, the Autoencoders, the recurrent networks and the LSTM and GRU, the Transformer Neural Networks, the Convolutional Neural Networks and Generative Adversarial Networks and analyzed the building blocks. Regularization techniques (Dropout, Early stopping and others), visual design and simulation techniques and tools, the most used algorithms and the best known architectures (LeNet, VGGnet, ResNet, Inception and others) are considered, closing with a set of practical tips and tricks. The exercises are described with Orange and Python using the Keras/Tensorflow library. The text is accompanied by supporting material and it is possible to download the examples and the test data.

Data Science Quick Reference Manual - Modeling and Machine Learning

Data Science Quick Reference Manual - Modeling and Machine Learning PDF Author: Mario A. B. Capurso
Publisher: Mario Capurso
ISBN:
Category : Computers
Languages : en
Pages : 191

Book Description
This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. Part of a series of books, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. Since this text uses Orange for the application aspects, it describes its installation and widgets. Then it considers the concept of model, its life cycle and the relationship with measures and metrics. The data modeling phase is considered from the point of view of machine learning by deepening the types of machine learning, the types of models, the types of problems and the types of algorithms. After considering the ideal characteristics of models and algorithms, a vocabulary of the types of models and algorithms is compiled and their use in Orange is considered through two supervised and unsupervised projects respectively. The text is accompanied by supporting material and you can download the samples in Orange and the test data.

Data Science Quick Reference Manual - Advanced Machine Learning and Deployment

Data Science Quick Reference Manual - Advanced Machine Learning and Deployment PDF Author: Mario A. B. Capurso
Publisher: Mario Capurso
ISBN:
Category : Computers
Languages : en
Pages : 278

Book Description
This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. Part in a series of texts, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. As this text uses Orange for the application aspects, it describes its installation and widgets. The data modeling phase is considered from the perspective of machine learning by summarizing machine learning types, model types, problem types, and algorithm types. Advanced aspects associated with modeling are described such as loss and optimization functions such as gradient descent, techniques to analyze model performance such as Bootstrapping and Cross Validation. Deployment scenarios and the most common platforms are analyzed, with application examples. Mechanisms are proposed to automate machine learning and to support the interpretability of models and results such as Partial Dependence Plot, Permuted Feature Importance and others. The exercises are described with Orange and Python using the Keras/Tensorflow library. The text is accompanied by supporting material and it is possible to download the examples and the test data.

Data Science Quick Reference Manual – Methodological Aspects, Data Acquisition, Management and Cleaning

Data Science Quick Reference Manual – Methodological Aspects, Data Acquisition, Management and Cleaning PDF Author: Mario A. B. Capurso
Publisher: Mario Capurso
ISBN:
Category : Computers
Languages : en
Pages : 228

Book Description
This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. First of a series of books, it covers methodological aspects, data acquisition, management and cleaning. It describes the CRISP DM methodology, the working phases, the success criteria, the languages and the environments that can be used, the application libraries. Since this book uses Orange for the application aspects, its installation and widgets are described. Dealing with data acquisition, the book describes data sources, the acceleration techniques, the discretization methods, the security standards, the types and representations of the data, the techniques for managing corpus of texts such as bag-of-words, word-count , TF-IDF, n-grams, lexical analysis, syntactic analysis, semantic analysis, stop word filtering, stemming, techniques for representing and processing images, sampling, filtering, web scraping techniques. Examples are given in Orange. Data quality dimensions are analysed, and then the book considers algorithms for entity identification, truth discovery, rule-based cleaning, missing and repeated value handling, categorical value encoding, outlier cleaning, and errors, inconsistency management, scaling, integration of data from various sources and classification of open sources, application scenarios and the use of databases, datawarehouses, data lakes and mediators, data schema mapping and the role of RDF, OWL and SPARQL, transformations. Examples are given in Orange. The book is accompanied by supporting material and it is possible to download the project samples in Orange and sample data.

Data Science Quick Reference Manual Exploratory Data Analysis, Metrics, Models

Data Science Quick Reference Manual Exploratory Data Analysis, Metrics, Models PDF Author: Mario A. B. Capurso
Publisher: Mario Capurso
ISBN:
Category : Computers
Languages : en
Pages : 323

Book Description
This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. Third of a series of books, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. Since this text uses Orange for the application aspects, it describes its installation and widgets. Then it considers the concept of model, its life cycle and the relationship with measures and metrics. The measures of localization, dispersion, asymmetry, correlation, similarity, distance are then described. The test and score metrics used in machine learning, those relating to texts and documents, the association metrics between items in a shopping cart, the relationship between objects, similarity between sets and between graphs, similarity between time series are considered. As a preliminary activity to the modeling phase, the Exploration Data Analysis is deepened in terms of questions, process, techniques and types of problems. For each type of problem, the recommended graphs, the methods of interpreting the results and their implementation in Orange are considered. The text is accompanied by supporting material and you can download the samples in Orange and the test data.

Data Science Quick Reference Manual Analysis and Visualization

Data Science Quick Reference Manual Analysis and Visualization PDF Author: Mario A. B. Capurso
Publisher: Mario A.B. Capurso
ISBN:
Category : Computers
Languages : en
Pages : 221

Book Description
This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. Second of a series of books, it covers methodological aspects, analysis and visualization. It describes the CRISP DM methodology, the working phases, the success criteria, the languages and the environments that can be used, the application libraries. Since this book uses Orange for the application aspects, its installation and widgets are described. In visualization, historical notes are made, and next the book describes the characteristics of an effective visualization, the types of messages that can be conveyed, the Grammar of Graphics, the use of a graph and a dashboard, the software and libraries that can be used, the role and use of color. 55 types of graphs are then analyzed, reporting meaning, use, examples and visual dimensions also with a vocabulary of graphs and summary tables. Examples are given in Orange and the possible use of Python with Orange is explained. Visualization-based inference is discussed, exploratory and confirmatory analysis is defined and techniques are reported. The book is accompanied by supporting material and it is possible to download the project samples in Orange and sample data.

Python Machine Learning for Beginners

Python Machine Learning for Beginners PDF Author: Leonard Deep
Publisher:
ISBN: 9781097858309
Category :
Languages : en
Pages : 236

Book Description
Are you interested to get into the programming world? Do you want to learn and understand Python and Machine Learning? Python Machine Learning for Beginners is the guide for you. Python Machine Learning for Beginners is the ultimate guide for beginners looking to learn and understand how Python programming works. Python Machine Learning for Beginners is split up into easy to learn chapters that will help guide the readers through the early stages of Python programming. It's this thought out and systematic approach to learning which makes Python Machine Learning for Beginners such a sought-after resource for those that want to learn about Python programming and about Machine Learning using an object-oriented programming approach. Inside Python Machine Learning for Beginners you will discover: An introduction to Machine Learning The main concepts of Machine Learning The basics of Python for beginners Machine Learning with Python Data Processing, Analysis, and Visualizations Case studies and much more! Throughout the book, you will learn the basic concepts behind Python programming which is designed to introduce you to Python programming. You will learn about getting started, the keywords and statements, data types and type conversion. Along with different examples, there are also exercises to help ensure that the information sinks in. You will find this book an invaluable tool for starting and mastering Machine Learning using Python. Once you complete Python Machine Learning for Beginners, you will be more than prepared to take on any Python programming. Scroll back up to the top of this page and hit BUY IT NOW to get your copy of Python Machine Learning for Beginners! You won't regret it!

The Data Science Design Manual

The Data Science Design Manual PDF Author: Steven S. Skiena
Publisher: Springer
ISBN: 3319554441
Category : Computers
Languages : en
Pages : 445

Book Description
This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)

Data Scientist Pocket Guide

Data Scientist Pocket Guide PDF Author: Mohamed Sabri
Publisher: BPB Publications
ISBN: 9390684978
Category : Computers
Languages : en
Pages : 418

Book Description
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