Python for Programmers: with Big Data and Artificial Intelligence Case Studies by Paul Deitel , Harvey Deitel is a PDF book for free download.
The professional programmer’s Deitel guide to Pythonwith introductory artificial intelligence case studies
Written for programmers with a background in another high-level language, this book uses hands-on instruction to teach today’s most compelling, leading-edge computing technologies and programming in Python–one of the world’s most popular and fastest-growing languages. Please read the Table of Contents diagram inside the front cover and the Preface for more details.
In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you’ll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms.
After covering Python Chapters 1—5 and a few key parts of Chapters 6—7, you’ll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11—16, which are loaded with cool, powerful, contemporary examples.
These include natural language processing, data mining Twitter for sentiment analysis, cognitive computing with IBM Watson™, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop, Spark™ and NoSQL databases, the Internet of Things and more. You’ll also work directly or indirectly with cloud-based services, including Twitter, Google Translate™, IBM Watson, Microsoft Azure, OpenMapQuest, PubNub and more.
Publisher : Pearson (March 22, 2019)
Language : English
Pages : 640
File : PDF, 14.59 MB
Free Download the Book: Python for Programmers: with Big Data and Artificial Intelligence Case Studies
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SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.
Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:
1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.
2. Every database seller needs an approach to separate its item from others.
Right now, contrasts are noted where fitting.
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The business impact companies are making with big data analytics is driving investment in digital transformation across the board.
Faced with multiple waves of disruption in a COVID-19 world, almost 92% of companies are reporting plans to spend the same or more on data/AI projects, according to a recent survey from NewVantage Partners.
Data mature companies are citing business-critical benefits from using big data, including:
Offensive drivers such as new competitive advantages, innovation, and transformation override defensive ones as change is becoming the only market constant nowadays.
Let’s explore what business benefits exactly companies are achieving with big data to edge out the competition.
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If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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Learn how Big Data and Business Intelligence, both technologies helps the decision-makers to make proper decisions that can help the organization to get advantages over their peers.
Businesses are becoming more data-driven. The implementation of big data analytics helps businesses in achieving full competitive advantage thereby reducing operational costs. Big data deals with data mining, storage, and analysis, while business intelligence is all about reporting and analysis of data, for making prudent business decisions, technological advancements, the various approaches, and for which the data is made easily accessible.
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We live in a world where billions of data points are generated every single day from different sources, such as banks, telecommunication companies, industries, tourism, the agriculture sector, educational institutions (primary, secondary, colleges, and universities), and mobile devices. Any organization can start using their data to make data-driven decision-making that is effective and supportive of their mission and vision.
Regardless of the size of the business you’re running, you need valuable data to provide you with business insights. The insights help you to know your target audience and their preferences, and as a result, your business will be able to anticipate their needs. You can use insights from big data to outperform your competition by capturing and innovating through big data.
Companies like Google and Alibaba are using it to discover flaws in their services and products, suppliers and buyers, and consumer intent and preferences so they can create newer, better ones.
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