Название: Mastering Machine Learning: From Basics to Advanced
Автор: Govindakumar Madhavan
Издательство: Springer
Год: 2025
Страниц: 261
Язык: английский
Формат: pdf (true), epub
Размер: 50.9 MB

This book covers all aspects of Machine Learning (ML) from concepts and math to ML programming. ML concepts and the math associated with ML are written from an application perspective, rather than from a theoretical perspective. The book presents concepts and algorithms precisely as they are used in real-world applications, ensuring a seamless and practical understanding with no gap between theory and practice.

In a distinctive approach, the book's content is complemented by video lectures whose details can be found inside the book. This innovative approach offers readers a multimedia learning experience, accommodating different learning preferences, and reinforcing the material through visual and auditory means. If you are new to Artificial Intelligence and Machine Learning, this could be the first book you read and the first video course you take.

In the previous chapter, readers discovered how recognizing patterns underpins the foundation of Machine Learning. Building upon this, the current chapter provides an introduction to Machine Learning (ML), clearly differentiating between supervised and unsupervised learning—the primary focus areas of this book. Readers will explore industry specific ML applications spanning diverse sectors such as agriculture, education, energy, entertainment, finance, manufacturing, and healthcare, highlighting ML’s wide-ranging impact and utility. Additionally, the chapter briefly introduces reinforcement learning and semi-supervised learning, offering learners a broader perspective of the ML landscape. To simplify complex concepts, everyday analogies and relatable examples illustrate how ML models and algorithms function, making the topic approachable even to beginners. This chapter thus sets the stage for deeper exploration of supervised and unsupervised methods, preparing readers effectively for subsequent chapters dedicated to these key ML approaches. Readers will also understand why choosing the right ML technique is crucial for solving real-world problems. Finally, the chapter encourages readers to appreciate the power and potential of Machine Learning in driving innovation and improving decision-making across various domains.

In this chapter, readers are introduced to Deep Learning, an advanced subset of Machine Learningg that utilizes neural networks inspired by human brain architecture. Building upon foundational concepts from earlier chapters, readers learn how neural networks function by leveraging interconnected layers to identify complex patterns within large datasets. Key terms, such as activation functions—including sigmoid, ReLU, and softmax—are discussed clearly to illustrate their roles in neural network behavior. Readers gain insights into the distinct evolution and growing significance of Artificial Neural Networks (ANN), understanding their real-world applications. Specialized neural network structures, namely Convolutional Neural Networks (CNNs) for image analysis and Recurrent Neural Networks (RNN) for sequential data, are also explored. Advantages, limitations, and scenarios where each network type performs optimally are presented clearly. Ultimately, this chapter prepares readers to confidently approach complex real-world applications using Deep Learning algorithms. It is worth noting that RNNs are the foundational building blocks behind transformer-based architectures like transformers and GPT models, including ChatGPT.

- Comprehensive book covering all aspects of Machine Learning
- Covers all aspects of programming in a cloud based development environment
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