A Review on Business Intelligence and Big Data <s> Machine Learning <s> A practical, step-by-step approach to making sense out of data Making Sense of Data educates readers on the steps and issues that need to be considered in order to successfully complete a data analysis or data mining project. The author provides clear explanations that guide the reader to make timely and accurate decisions from data in almost every field of study. A step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. With a comprehensive collection of methods from both data analysis and data mining disciplines, this book successfully describes the issues that need to be considered, the steps that need to be taken, and appropriately treats technical topics to accomplish effective decision making from data. Readers are given a solid foundation in the procedures associated with complex data analysis or data mining projects and are provided with concrete discussions of the most universal tasks and technical solutions related to the analysis of data, including: Problem definitions Data preparation Data visualization Data mining Statistics Grouping methods Predictive modeling Deployment issues and applications Throughout the book, the author examines why these multiple approaches are needed and how these methods will solve different problems. Processes, along with methods, are carefully and meticulously outlined for use in any data analysis or data mining project. From summarizing and interpreting data, to identifying non-trivial facts, patterns, and relationships in the data, to making predictions from the data, Making Sense of Data addresses the many issues that need to be considered as well as the steps that need to be taken to master data analysis and mining. <s> BIB001 </s> A Review on Business Intelligence and Big Data <s> Machine Learning <s> SummaryMachine Learning in Action is 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 the BookA 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.Purchase includes free PDF, ePub, and Kindle eBooks downloadable at manning.com. What's InsideA no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos=================================== Table of ContentsPART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: nave Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce <s> BIB002 </s> A Review on Business Intelligence and Big Data <s> Machine Learning <s> Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. Youll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your companys data science projects. Youll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organizationand how you can use it for competitive advantage Treat data as a business asset that requires careful investment if youre to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates <s> BIB003 </s> A Review on Business Intelligence and Big Data <s> Machine Learning <s> This chapter introduces the foundations of machine learning that are necessary to motivate its combination to evolutionary algorithms and image processing. In the chapter, we will describe several machine learning tools, suitable for a wide variety of data and tasks. The goal is not to describe the total number of techniques, but instead to present a general view of the field through the description of the mail problems considered in machine learning. <s> BIB004 </s> A Review on Business Intelligence and Big Data <s> Machine Learning <s> “Big Data” can mean different things to different people. The scale and challenges of Big Data are often described using three attributes, namely volume, velocity, and variety (3Vs), which only reflect some of the aspects of data. In this chapter, we review historical aspects of the term “big data” and the associated analytics. We augment the 3Vs with additional attributes of big data to make it more comprehensive and relevant. We show that Big Data is not just the 3Vs, but actually 3 2 Vs; that is, 9Vs covering the fundamental motivation behind Big Data, which is to incorporate business intelligence based on different hypothesis or statistical models so that Big Data analytics (BDA) can enable decision makers to make useful predictions for making some crucial decisions or researching results. History of Big Data has demonstrated that the most cost-effective way of performing BDA is to employ machine learning (ML) on the cloud computing (CC)-based infrastructure or simply, ML + CC → BDA. This chapter is devoted to help decision makers by defining BDA as a solution and opportunity to address their business needs. <s> BIB005
Machine learning (ML) is situated the intersection of computer science, engineering, and statistics and as well as other disciplines BIB002 . ML learns from past and present data and uses it further future prediction and decisions. ML is used to solve complex problems that cannot be solved with standard algorithms. Standard algorithms are not able to solve every kind of problems like distinguishing spam e-mail from legitimate one BIB004 . To some degree, ML imitates human learning abilities and sometimes outperforms human being for handling complex tasks or problems. In order to adapt itself new situations without human interference and improve results, ML uses algorithms and iteratively learns from new data and old results. If an algorithm doesn't solve the problem and data are available, ML can solve. As data is increasing enormously, how to derive value from and discover hidden patterns in this huge data is a big problem. ML aims to automatically solve this problem using huge dataset, hence props up BDA. So, ML positions itself as a core component of BDA. All other components of big data platform serve for ML in some way BIB005 . ML algorithms can be broadly classified as supervised and unsupervised learning. In supervised learning, machine learns from training data. Supervised methods try to discover the relationship between inputs (independent variable) and target (dependent variable) in the context of a model  . If features and target variables of records are available supervised learning can predict accurately target variable given a new record. If there is no target but features then problem fits into unsupervised learning. When an enterprise wants to group his customers but doesn't sure how many groups should come out, it is better to use unsupervised learning. If this enterprise wants to know which customers will likely to purchase particular products and put these customers in a group, then supervised learning is in use. Supervised learning uses different techniques than unsupervised one and produces better results BIB003 . Even so, both techniques are useful for solving different kinds of problems. Supervised learning has two main sub-branch: classification and regression. Classification is the task to predict a class label for a given unlabeled point BIB004 . A Bank can group their customers according to credit risk as high, medium and low risk, by using classification. Regression tries to predict dependent variable or target value using available input (independent variables) data. For a simple example; using patient's age and body mass index, cholesterol level can be predicted. Another example: Using some car attributes such as engine capacity, age, and fuel consumption we can evaluate the value of the car with regression method. The main difference between regression and classification is; while classification methods use categorical target, regression uses numeric one BIB001 and regression also requires numeric independent variables. Unsupervised learning doesn't use training data for finding patterns from given data. Prominent methods for unsupervised learnings are clustering and associative rule mining. Clustering finds a natural grouping of instances given un-labeled data. Simply, it groups similar items. The cluster is a subset of objects such that the distance between any two objects in the cluster is less than the distance between any object in the cluster and any object not located inside it. K-means clustering and apriori algorithm for association analysis, namely shopping basket analysis are widely used clustering algorithms. 