Machine Learning For Decision Sciences With Case Studies In Python
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Machine Learning for Decision Sciences with Case Studies in Python
This book provides a detailed description of machine learning algorithms in data analytics, data science life cycle, Python for machine learning, linear regression, logistic regression, and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical, oil and gas, e-commerce, and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learning including unsupervised, supervised, and reinforcement learning. This book is aimed at researchers, professionals, and graduate students in data science, machine learning, computer science, and electrical and computer engineering.
Python Machine Learning Case Studies
Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on minimal time and resources. Python Machine Learning Case Studies takes you through the steps to improve business processes and determine the pivotal points that frame strategies. You’ll see machine learning techniques that you can use to support your products and services. Moreover you’ll learn the pros and cons of each of the machine learning concepts to help you decide which one best suits your needs. By taking a step-by-step approach to coding in Python you’ll be able to understand the rationale behind model selection and decisions within the machine learning process. The bookis equipped with practical examples along with code snippets to ensure that you understand the data science approach to solving real-world problems. What You Will Learn Gain insights into machine learning concepts Work on real-world applications of machine learning Learn concepts of model selection and optimization Get a hands-on overview of Python from a machine learning point of view Who This Book Is For Data scientists, data analysts, artificial intelligence engineers, big data enthusiasts, computer scientists, computer sciences students, and capital market analysts.
Case Studies on Holistic Medical Interventions
The First International Medical Case Reports Conference, 2024(IMED-C) was a pioneering event set to redefine the landscape of medical research and case reporting. This conference was designed to foster collaboration and knowledge exchange among healthcare professionals, researchers, and scholars worldwide. What made this edition exceptional was its virtual online format, breaking down geographical barriers and transforming the way medical knowledge is shared. It was a platform where the latest breakthroughs in medical case reports were unveiled, innovative diagnostic strategies and treatment approaches showcased, and visionary ideas were given a voice. It became a central meeting point for professionals and scholars seeking to share experiences and expertise across borders.