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Über dieses Buch

Learn about the emergence and evolution of IT in the enterprise, see how machine learning is transforming business intelligence, and discover various cognitive artificial intelligence solutions that complement and extend machine learning. In this book, author Rohit Kumar explores the challenges when these concepts intersect in IT systems by presenting detailed descriptions and business scenarios. He starts with the basics of how artificial intelligence started and how cognitive computing developed out of it. He'll explain every aspect of machine learning in detail, the reasons for changing business models to adopt it, and why your business needs it.

Along the way you'll become comfortable with the intricacies of natural language processing, predictive analytics, and cognitive computing. Each technique is covered in detail so you can confidently integrate it into your enterprise as it is needed. This practical guide gives you a roadmap for transformin

g your business with cognitive computing, giving you the ability to work confidently in an ever-changing enterprise environment.

What You'll Learn

See the history of AI and how machine learning and cognitive computing evolvedDiscover why cognitive computing is so important and why your business needs it

Master the details of modern AI as it applies to enterprisesMap the path ahead in terms of your IT-business integrationAvoid common road blocks in the process of adopting cognitive computing in your business

Who This Book Is For

Business managers and leadership teams.



Chapter 1. Journey of Business Intelligence

The scope of this chapter is to build up an initial understanding of business intelligence (BI) with definitions. This chapter is also aimed at building a basic understanding of how it started and what the initial use cases for it were. I will also cover the paradigm shift into the industry business usability use case for business intelligence.
Rohit Kumar

Chapter 2. Why Cognitive and Machine Learning?

This chapter continues further in discussing why the cognitive option was a natural choice for the evolution of business intelligence. In Chapter 1, we discussed the various levels of evolution for business intelligence and saw that the more human-like thinking machines doing predictive and prescriptive analytics had become available by the 2000s. These were the cognitive and machine learning applications or machines. Though we intend to discuss these terms in coming chapters, let us first dig a little to see why this evolution was required.
Rohit Kumar

Chapter 3. Artificial Intelligence—Basics

The scope of this book is to build essential knowledge around cognitive computing and its role in influencing business and IT. But cognitive computing knowledge cannot be built up without having basic knowledge of artificial intelligence (AI), machine learning (ML), and deep learning.
Rohit Kumar

Chapter 4. Machine Learning—Basics

Taking our AI discussion further from Chapter 3, we will discuss here further a special type of artificial intelligence called machine learning.
Rohit Kumar

Chapter 5. Natural Language Processing

Taking our discussion further along from Chapter 4, on machine learning basics, we will discuss here the next topic: natural language processing, or the way machines and computers process and understand natural (human) languages.
Rohit Kumar

Chapter 6. Predictive Analytics

The subject matter of this chapter continues on from artificial intelligence, machine learning, and natural language processing. Now we will discuss predictive analytics, which uses all of these to predict valuable information from both present and historical data sets.
Rohit Kumar

Chapter 7. Cognitive Computing

This chapter talks about cognition and cognitive computing concepts. These machines simulate human thought process through computational capabilities.
Rohit Kumar

Chapter 8. Principles for Cognitive Systems

This chapter talks about the principles of designing cognitive systems. The systems to be used as cognitive assistants should be trained and designed in a way that works with maximum output in those areas.
Rohit Kumar

Chapter 9. Parallel Evolving IT-BI Systems

This is a new concept, and I believe it’s the way IT landscapes and business intelligence systems should evolve. I call it “parallel evolving IT-BI systems” (PEIBs).
Rohit Kumar

Chapter 10. Transformation Roadmap

This chapter talks about “transformation roadmaps” using the PEIB-based framework.
Rohit Kumar

Chapter 11. Transformation Road Blockers

This chapter talks about the road blockers in transformation.
Rohit Kumar

Chapter 12. Self-Evolving Cognitive Business

This chapter talks about businesses which are naturally cognitive driven. A business which is cognitively driven to the core to be responsive not only to needs for change almost in real time but also to business planning and monitoring driven by real-time data. Decisions in the cognitive computing environment are almost 100% data driven and always validated for biases.
Rohit Kumar

Chapter 13. Path Ahead

In this concluding chapter, we will discuss the path ahead for organizations, IT service providers, and IT consultants.
Rohit Kumar


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