Machine Learning and AI Without Coding
Machine learning, a subset of artificial intelligence (AI), has become a buzzword in today’s digital age, transforming industries and automating tasks. However, the perception that machine learning requires extensive programming knowledge can be a barrier for many. This book “AI for Non-Programmers: Building Data Science and AI Solutions Without Coding,” demystifies machine learning and empowers non-programmers to harness its power. The book is available on Amazon, in paperback, hardcover, and kindle formats, at https://a.co/d/igy4L1N.
For Non-Programmers
This book is designed for individuals who wish to understand and apply machine learning concepts without the need for intricate coding. It is an essential read for business analysts, marketers, managers, and anyone with a passion for data-driven insights. The author’s mission is to make machine learning accessible to everyone, regardless of their programming background.
Introduction to Machine Learning
The book begins by introducing machine learning as a technology that enables computers to learn from data and make decisions or predictions without being explicitly programmed. It covers the four main types of machine learning: supervised learning, unsupervised learning, reinforcement learning, and deep learning. The book provides real-world examples and practical applications of each type, making the concepts easy to understand.
No-code Platform: Momentum
Another crucial aspect of the book is the utilization of no-code platform, Accure Momentum, https://accure.ai/momentum. This user-friendly platform empower users to build machine learning models, automate processes, and extract insights from data with intuitive drag-and-drop interfaces. The book also explores these tools in depth, ensuring readers have the skills they need to succeed.
Machine Learning Lifecycle
The book also covers the entire machine learning development lifecycle, from data engineering and model training to deployment, monitoring, and management. It emphasizes the importance of data engineering, which plays a crucial role in preparing data for machine learning models. The book covers key concepts in data engineering, such as SQL, data types, and data sources.
1000 Industrial AI Use Cases
Furthermore, the book provides a comprehensive list of 1,000 AI use cases across 20 industries, including healthcare, finance, retail, manufacturing, and more. Each use case includes suitable algorithms, making it an invaluable resource for identifying practical applications of machine learning within various organizations or areas of interest.
Practical Guide
This book is not just a theoretical guide; it is a practical one. It emphasizes hands-on, real-world scenarios and projects, allowing readers to apply machine learning concepts without the need for intricate coding. The author covers various machine learning tasks, such as predictive analytics, recommendation systems, and data engineering.
Conclusion
“From Concept to Creation: Building Data Science and AI Solutions Without Coding: A Practical Guide for Non-Programmers, Subject Matter Experts, Engineers, Students, and Analysts” is an essential read for anyone looking to understand and apply machine learning concepts without the need for extensive programming knowledge. Shamshad Ansari’s book breaks down the barriers to entry, making machine learning accessible to business leaders, managers, and professionals, enabling them to become more data-savvy, make informed decisions, and drive innovation within their organizations.
