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Artificial Intelligence with Python [electronic resource] /

By: Contributor(s): Material type: TextTextSeries: Machine Learning: Foundations, Methodologies, and ApplicationsPublisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2022Edition: 1st ed. 2022Description: XIV, 336 p. 20 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9789811686153
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 006.3 23
LOC classification:
  • Q334-342
  • TA347.A78
Online resources:
Contents:
Part I Python -- 1 About Python -- 2 What’s Python? -- 3 An Introductory Example -- 4 Basic Python -- 5 Intermediate Python -- 6 Advanced Python -- 7 Python for data analysis -- Part II Artificial Intelligence Basics -- 8 Introduction to artificial intelligence -- 9 Data wrangling -- 10 Regression -- 11 Classification -- 12 Clustering -- 13 Association Rules -- Part III Artificial Intelligence -- Implementations -- 14 Text Mining -- 15 Image Processing -- 16 Convolutional Neural Networks -- 17 Chatbot, Speech and NLP -- 18 Deep Convolutional Generative Adversarial Network -- 19 Neural style transfer -- 20 Reinforcement learning -- 21 References.
In: Springer Nature eBookSummary: Entering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon’s Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence. This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjectsin deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing.
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Part I Python -- 1 About Python -- 2 What’s Python? -- 3 An Introductory Example -- 4 Basic Python -- 5 Intermediate Python -- 6 Advanced Python -- 7 Python for data analysis -- Part II Artificial Intelligence Basics -- 8 Introduction to artificial intelligence -- 9 Data wrangling -- 10 Regression -- 11 Classification -- 12 Clustering -- 13 Association Rules -- Part III Artificial Intelligence -- Implementations -- 14 Text Mining -- 15 Image Processing -- 16 Convolutional Neural Networks -- 17 Chatbot, Speech and NLP -- 18 Deep Convolutional Generative Adversarial Network -- 19 Neural style transfer -- 20 Reinforcement learning -- 21 References.

Entering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon’s Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence. This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjectsin deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing.

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