Artificial Intelligence with Python

Dr. Teoh has been pursuing research in big data, deep learning, cybersecurity, artificial intelligence, machine learning, and software development for more than 25 years. His works have been published in more than 50 journals, conference proceedings, books, and book chapters. His qualifications include a PhD in computer engineering from the NTU, Doctor of Business Administration from the University of Newcastle, Master of Law from the NUS, LLB and LLM from the UoL, CFA, ACCA, and CIMA. He has more than 15 years’ experience in data mining, quantitative analysis, data statistics, finance, accounting, and law and is passionate about the synergy between business and technology.Zheng Rong is a software engineer with 4 years of experience. He embraces the ambiguity of data and enjoys the challenges presented by business problems.  He has 3 years of teaching experience in data mining and data science, and has coauthored three journal publications on machine learning and deeplearning. He is interested in making learning programming and technology easy for all, including those from a non-technical background.

“The book under review follows the structure of a textbook and is decorated with examples, exercises and sample code … . The book is addressed to a wide range of audiences, from students to established researchers. Some computer science background facilitates the understanding of concepts; however, the numerous examples, and ample references recommend this book for researchers with backgrounds other than computer science.” (Irina Ioana Mohorianu, zbMATH 1541.68001, 2024)

• 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.