11 results on 'LN cat08778a'
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2. Clean Ruby: A Guide to Crafting Better Code for Rubyists#
- Author
-
Carleton DiLeo
- Subjects
Internet of things ,Machine learning ,Electronic books - Published
- 2020
3. Python machine learning cookbook : over 100 recipes to progress from smart data analytics to deep learning using real-world datasets.
- Author
-
Ciaburro, Giuseppe and Joshi, Prateek
- Subjects
Python (Computer program language) ,Machine learning ,Electronic books - Published
- 2019
4. Business analytics.
- Author
-
Sahay, Amar
- Subjects
Management -- Statistical methods ,Decision making -- Statistical methods ,Business planning ,Strategic planning ,Business intelligence ,BUSINESS & ECONOMICS -- Industrial Management ,BUSINESS & ECONOMICS -- Management ,BUSINESS & ECONOMICS -- Management Science ,BUSINESS & ECONOMICS -- Organizational Behavior ,Electronic books ,analytics ,business analytics ,business intelligence ,data analysis ,data mining ,decision making ,descriptive analytics ,machine learning ,modeling ,neural networks ,optimization ,predictive analytics ,predictive modeling ,prescriptive analytics ,quantitative techniques ,regression analysis ,simulation ,statistical analysis ,time-series forecasting - Abstract
Abstract: This book is about Business Analytics (BA)--an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI) that looks into historical data to better understand business performance thereby improving performance, and creating new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics--the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe a number of terms related to business analytics. The second part of the book is about descriptive analytics and its applications. The topics discussed are--Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically--Concepts and Computer Applications. Finally, an overview and a case on descriptive statistics with applications and notes on implementation are presented. The concluding remarks provide information on becoming a certified analytics professional (CAP) and an overview of the second volume of this book which is a continuation of this first volume. It is about predictive analytics which is the application of predictive models to predict future trends. The second volume discusses Prerequisites for Predictive Modeling; Most Widely used Predictive Analytics Models, Linear and Non-linear regression, Forecasting Techniques, Data mining, Simulation, and Data Mining.
- Published
- 2018
5. Business analytics.
- Author
-
Sahay, Amar
- Subjects
Management -- Statistical methods ,Decision making -- Statistical methods ,Business planning ,Strategic planning ,Business intelligence ,BUSINESS & ECONOMICS -- Industrial Management ,BUSINESS & ECONOMICS -- Management ,BUSINESS & ECONOMICS -- Management Science ,BUSINESS & ECONOMICS -- Organizational Behavior ,Electronic books ,analytics ,business analytics ,business intelligence ,data analysis ,data mining ,decision making ,descriptive analytics ,machine learning ,modeling ,neural networks ,optimization ,predictive analytics ,predictive modeling ,prescriptive analytics ,quantitative techniques ,regression analysis ,simulation ,statistical analysis ,time-series forecasting - Abstract
Abstract: This book is about Business Analytics (BA)--an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI) that looks into historical data to better understand business performance thereby improving performance, and creating new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics--the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe a number of terms related to business analytics. The second part of the book is about descriptive analytics and its applications. The topics discussed are--Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically--Concepts and Computer Applications. Finally, an overview and a case on descriptive statistics with applications and notes on implementation are presented. The concluding remarks provide information on becoming a certified analytics professional (CAP) and an overview of the second volume of this book which is a continuation of this first volume. It is about predictive analytics which is the application of predictive models to predict future trends. The second volume discusses Prerequisites for Predictive Modeling; Most Widely used Predictive Analytics Models, Linear and Non-linear regression, Forecasting Techniques, Data mining, Simulation, and Data Mining.
- Published
- 2018
6. Fundamentals of deep learning : designing next-generation machine intelligence algorithms.
- Author
-
Buduma, Nikhil and Locascio, Nicholas
- Subjects
Artificial intelligence ,Machine learning ,Neural networks (Computer science) ,Deep learning ,Künstliche Intelligenz ,Maschinelles Lernen ,Electronic books - Published
- 2017
7. A First Course in Machine Learning.
- Author
-
Rogers, Simon and Girolami, Mark
- Subjects
Machine learning ,COMPUTERS -- General ,Data Mining ,Maschinelles Lernen ,Machine Learning ,Electronic books - Published
- 2016
8. Computational trust models and machine learning.
- Author
-
Liu, Xin, Datta, Anwitaman, and Lim, Ee-Peng
- Subjects
Computational intelligence ,Machine learning ,Truthfulness and falsehood -- Mathematical models ,COMPUTERS -- General ,Electronic books - Abstract
Summary: "This book provides an introduction to computational trust models from a machine learning perspective. After reviewing traditional computational trust models, it discusses a new trend of applying formerly unused machine learning methodologies, such as supervised learning. The application of various learning algorithms, such as linear regression, matrix decomposition, and decision trees, illustrates how to translate the trust modeling problem into a (supervised) learning problem. The book also shows how novel machine learning techniques can improve the accuracy of trust assessment compared to traditional approaches"-- Provided by publisher.
- Published
- 2015
9. Learning Spark : lightening fast data analysis.
- Author
-
Karau, Holden
- Subjects
ApacheSpark ,Big data ,Machine learning ,Electronic books - Abstract
Summary: This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. You'll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.
- Published
- 2015
10. Machine learning in action.
- Author
-
Harrington, Peter
- Subjects
Machine learning ,Machine learning -- Handbooks, manuals, etc ,Engineering & Applied Sciences ,Computer Science ,Electronic book ,Electronic books ,Handbooks and manuals - Abstract
Summary: Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About this Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos About the Author Peter Harrington is a professional developer and data scientist. He holds five US patents and his work has been published in numerous academic journals.
- Published
- 2012
11. Machine learning in action.
- Author
-
Harrington, Peter
- Subjects
Machine learning ,Machine learning -- Handbooks, manuals, etc ,Engineering & Applied Sciences ,Computer Science ,Electronic book ,Electronic books ,Handbooks and manuals - Abstract
Summary: Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About this Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos About the Author Peter Harrington is a professional developer and data scientist. He holds five US patents and his work has been published in numerous academic journals.
- Published
- 2012
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