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Data Analysis from Scratch with Python: the Complete Beginner's Data Analysis from Scratch with Python The Complete Beginner's Guide for Machine Learning Techniques and A Step By Step NLP using Python Guide To Expert (Including Programming Interview Questions) STEPHEN RICHARD © Copyright 2019 by Stephen Richard All rights reserved. No a part of this book may be reproduced or transmitted in any kind or by any suggests that electronic or mechanical, as well as photocopying, Recording or by any data storage and TABLE OF CONTENTS INTRODUCTION CHAPTER 1 Method and Technique When to use each method and technique Machine learning methods Machine Learning Techniques Intel NLP Architect: Open Source Natural Language Processing Model Library CHAPTER 2 How To Solve NLP Tasks: A Walkthrough On Natural Language Processing A look at the importance of Natural Language Processing NLP: How to Become a Natural Language Processing Specialist CHAPTER 3 Programming Interview Questions CHAPTER 4 What is data analysis and why is it important? Software For The Application Of Automatic Learning Techniques For Medical Diagnosis CHAPTER 5 What Is Machine Learning And What Is It Contributing To Cognitive Neuroscience? Machine learning techniques at the service of Energy Efficiency in the digital home CHAPTER 6 5 natural language processing methods that are rapidly changing the world around us CHAPTER 8 How to develop chatbot from scratch in Python: a detailed instruction SUMMARY AND CONCLUSION INTRODUCTION The data analysis techniques have substantial support in machine learning for the generation of knowledge in the organization. Machine learning exploits statistics and many other areas of mathematics. Its advantage is speed. Although it would be unfair to consider that the strength of this type of techniques is only speed, it must be taken into a report that the speed that characterizes this artificial intelligence modality comes from the processing power, both sequential and parallel, and in-memory storage capacity. However, the potential of machine learning to complement other, more traditional data analysis techniques has to do with the ability to relearn the representation of the data. Machine learning allows machines to use a different and proper language for problem-solving. It is a language in continuous evolution and with roots in the analysis, although, for many, it cannot be considered analytics as such. When the possibilities of machine learning complement the scope of the data analysis techniques, it is possible to see much more clearly what matters in terms of knowledge generation, not only at the quantitative level but also ensuring a significant qualitative improvement. Why machine learning is the best complement to data analysis techniques Machine learning methods are far superior in the analysis of data from multiple sources. Transactional information that which comes from social media or that originates from systems such as CRM can overwhelm the ability of traditional data analysis techniques. On the contrary, high-performance machine learning can analyze a whole Big Data set, instead of forcing business users to settle for a representative sample that, after all, remains a sample. This scalability not only allows predictive solutions based on sophisticated algorithms to be more accurate but also drives the importance of software speed. In this way, it is already possible to interpret in real-time the billions of rows and columns that must be investigated, while the analysis of the data flow that is arriving does not stop. In order to take full advantage of machine learning, organizations must move towards a model that allows: Win in intelligence and usability in regards to the use of machine learning. Allow not only data scientists and professional profiles with a higher degree of specialization to access this technology. Put the means so that business users can also take advantage of the latest generation analytical capabilities that machine learning offers them. When this technology is democratized and integrated with the data analysis techniques that are already in use in the organization, the business not only gains insights but significantly accelerates the time needed to generate quality knowledge. In this way, any organization, of any size, can exploit the unprecedented competitive potential. When data analysis techniques find no substitute It seems obvious to think that the ideal complement to data analysis techniques can also be the determining factor in their end. Moreover, yet it is not. Machine learning is a winning combination when it is known to integrate into an analytics strategy, but it cannot replace the necessary predictive capabilities that every organization needs. Sometimes it is not efficient, nor profitable, and much less logical, to resort to this form of artificial intelligence to solve specific questions. Typically, these are topics where the best answer comes from analytics, such as: The marketing strategy that gives the best results. The calculation of routes. The behavior of a consumer segment. Why invest in machine learning when it is not the best solution? Supplement yes, substitute no. Because although the data analysis techniques that are usually used in organizations can be limited, related to the volume of data to be processed or the speed at which it is necessary to obtain insights; Machine learning is also not the most straightforward alternative. Machine learning requires the organization to be able to provide data in certain conditions (a continuous flow in real-time), and, above them, it needs to create an algorithm, which allows reaching a set of rules that work regardless of whether it exists or no information available. And, while it is true that the only way to exploit the actual value of IoT or the most efficient and lowest-cost way to achieve innovative solutions in line with wearables, for example, in the health industry, is the machine learning; It should also be borne in mind that many business decisions only require the support given by a couple of processing rules, a solution that facilitates the discovery of data and the identification of trends, or software that allows them to forecast future scenarios with a minimum margin of error. Can you do without predictive analytics? Today, it would be unthinkable to define strategies or carry out any action without the support provided by data analysis techniques. Is it possible to continue moving forward without machine learning? Except for particular industries and in particular cases, there is still room until it becomes imperative to turn to machines to identify potential patterns and correlations in data, despite the impressive results they deliver. CHAPTER 1 Method and Technique When to use each method and technique You probably heard more and more about machine learning, a subset of artificial intelligence. But what exactly can be done with it? Technology encompasses methods and techniques, and each has a set of potential use cases. Companies would do well to examine them before moving forward with plans to invest in the machine learning tools and the infrastructure. Machine learning methods Supervised learning: This Supervised learning is ideal if you know what you want a machine to learn. You can expose it to a vast set of training data, examine the result, and modify the parameters until you get the results you expect. After that, you can see what the machine has learned by predicting the results of a validation data set that you have not seen before. The supervised learning tasks include classification and prediction or regression. The Supervised learning methods can be used for applications such as determining the financial risk of individuals and organizations, based on past information on financial performance. They can also provide a good idea of how customers will act or what their preferences are based on previous behavior patterns. For example, the Lending Tree online loan market is using an automated DataRobot machine learning platform to customize experiences for its customers and predict their intention based on what they have done in the past, says Akshay Tandon, vice president and chief of strategy and analytics By predicting the intention of the client - primarily through the qualification of potential clients - Lending Tree can classify people who are looking for a rate, compared to those who are really looking for a loan and are ready to apply for one. Using supervised learning techniques, he constructed a classification model to define the probability of a lead closing. Unsupervised learning: Unsupervised learning allows a machine to explore a set of data and identify hidden patterns that link different variables. This method can be used to group data into groups only based on their statistical properties. A useful application of the unsupervised learning is the clustering algorithm used to make links to probabilistic records, a technique that extracts connections between data elements and builds them to identify people and organizations and their connections in the physical or virtual world. This is especially useful for companies that need, for example, to integrate data from disparate sources and/or in different business units to build a consistent and complete view of their customers, says Flavio Villanustre, vice president of technology at LexisNexis Risk Solutions, a A company that uses analysis to help customers predict and manage risks. Unsupervised learning could be used for sentiment analysis, which identifies people's emotional state based on their social media posts, emails or other written comments, notes Sally Epstein, a machine learning engineering specialist at Cambridge consultancy. Consultants the firm has gotten an increasing number of companies in financial services that use unsupervised learning to obtain information on customer satisfaction. Semi-Supervised Learning: Semi-Superviseded Learning is a hybrid of supervised and unsupervised learning. By labeling a little portion of the data, a trainer can give the machine clues about how to group the rest of the data set. Semi-supervised learning can be used to detect identity fraud, among other uses.
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