Essentials Of Python For Artificial Intelligence And Machine Learning
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Essentials of Python for Artificial Intelligence and Machine Learning
This book introduces the essentials of Python for the emerging fields of Machine Learning (ML) and Artificial Intelligence (AI). The authors explore the use of Python’s advanced module features and apply them in probability, statistical testing, signal processing, financial forecasting, and various other applications. This includes mathematical operations with array data structures, Data Manipulation, Data Cleaning, machine learning, Data pipeline, probability density functions, interpolation, visualization, and other high-performance benefits using the core scientific packages NumPy, Pandas, SciPy, Sklearn/Scikit learn and Matplotlib. Readers will gain a deep understanding with problem-solving experience on these powerful platforms when dealing with engineering and scientific problems related to Machine Learning and Artificial Intelligence. Several examples of real problems using these techniques are provided along with examples. The authors also focus on the best practices in the industry on using Python for AI and ML. Deployment on a cloud infrastructure is described in detail (with code) to emphasize real scenarios.
Artificial Intelligence and Machine Learning Essentials
Author: Kiran Kumar Pappula
language: en
Publisher: Academic Guru Publishing House
Release Date: 2025-02-06
Artificial Intelligence and Machine Learning Essentials is a comprehensive guide tailored for beginners and early-stage learners eager to explore the fascinating world of Al and ML. The book covers key concepts, techniques, and tools across eight well-structured chapters, offering readers a clear pathway from fundamental understanding to practical knowledge. Beginning with the basics of Artificial Intelligence, the book introduces readers to its history, types, and applications across different industries. It then delves into the core principles of Machine Learning, detailing the various types, algorithms, and workflows essential for building intelligent systems. Readers will gain insights into critical data preprocessing techniques that ensure high-quality input for ML models. The book further explores popular supervised and unsupervised learning algorithms, including linear regression, decision trees, K-means, and PCA, making it easier to grasp both the theoretical and practical aspects. Reinforcement Learning, Deep Learning models like CNNs and RNNs, and Natural Language Processing techniques are also thoroughly explained with real-life relevance. Written in simple and accessible language, the book makes complex topics easy to understand, making it suitable for university students, tech enthusiasts, and professionals from non-technical backgrounds. With a strong emphasison clarity and practical understanding, this book serves as a stepping stone into one of the most promising areas of modern technology.
Essentials of Excel VBA, Python, and R
This advanced textbook for business statistics teaches, statistical analyses and research methods utilizing business case studies and financial data with the applications of Excel VBA, Python and R. Each chapter engages the reader with sample data drawn from individual stocks, stock indices, options, and futures. Now in its second edition, it has been expanded into two volumes, each of which is devoted to specific parts of the business analytics curriculum. To reflect the current age of data science and machine learning, the used applications have been updated from Minitab and SAS to Python and R, so that readers will be better prepared for the current industry. This second volume is designed for advanced courses in financial derivatives, risk management, and machine learning and financial management. In this volume we extensively use Excel, Python, and R to analyze the above-mentioned topics. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits. Readers can look to the first volume for dedicated content on financial statistics, and portfolio analysis.