A Practical Approach for Machine Learning and Deep Learning Algorithms

A Practical Approach for Machine Learning and Deep Learning Algorithms is a hands-on guide to machine learning that keeps the focus on doing, not just reading about theory. With MATLAB at its core and selected Python and TensorFlow material included, it is built for readers who want to understand how algorithms are used in real situations and how data moves through a working machine learning workflow.

Practical machine learning from the ground up 💻

The opening chapters introduce the essentials: accessing data, preprocessing it, working with missing values, and organizing data for analysis. That foundation matters, because so much of machine learning lives in the details of preparing data well before any model is trained.

From classification and regression to neural networks

As the book progresses, it covers the core ideas behind machine learning types, clustering, classification methods, regression models, and neural networks. The structure suggests a guided path for readers who want to move steadily from basic concepts into more applied model-building.

Deep learning topics with an applied emphasis

The later material introduces deep learning, TensorFlow, feed-forward networks, activation functions, backpropagation, and related ideas. Rather than treating these as abstract topics, the book presents them in the context of implementation and comparison, which makes it especially useful for readers who learn best by seeing techniques used in practice.

Who this ebook suits

• Graduate and research students
• Engineering readers working with MATLAB
• Beginners who want a practical entry into machine learning
• Readers looking for a code-oriented overview of classical ML and introductory deep learning

If you want an ebook that bridges the gap between machine learning concepts and hands-on implementation, this title is a strong fit for a technical bookshelf.