Machine learning has become a major and integral part of human’s life, having many commercial applications, industrial automation, robotics and research projects are being in continuation of development but this field is not exclusive to large companies with extensive research teams and professional developers. If you know how use Python, even as a beginner, this book will teach you practical ways to build your own interesting machine learning projects and advance solutions of modern problems. With all the data available till date, machine learning and AI applications are limited only by your imagination.
You’ll learn the step by step concept that is necessary to create a successful machine-learning application with Python language and the scikit-learn library. Authors Sir Andreas Müller and Sir Sarah Guido focus mainly focus on the practical aspects of using machine learning algorithms, rather than the math behind each and every algorithm. With deeper knowledge about NumPy and matplotlib libraries will help you get even more from the book “A Guide for Data Scientists”.
With this book, you’ll learn:
Nearly 200 self-contained recipes are available in this practical guide “Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning” to help you solve machine learning challenges and problems you may encounter in your daily work. If you’re comfortable with Python and its libraries such as scikit-learn, including pandas you’ll be able to address specific problems such as handling text, loading data, numerical data, model selection, and dimensionality reduction and many other topics that are bothering you as a data scientist.
Each recipe includes code that you can easily copy and paste into a toy dataset to ensure that it actually works. From there, you can combine, insert, or adapt the code to help construct your own application. Recipes also include an interesting discussion area that explains the solution and provides meaningful context to code written. This cookbook takes you beyond theory and concepts by providing the bolts and nutsyou need to construct working machine learning projects and applications.
You’ll find recipes for:
This book would explain all the common terms and algorithms related to machine learning in an intuitive and easy way. The author used a progressive approach, start out slowly and improve on the complexity of the problem in every stage with solutions to the entire problems.
Python Machine Learning from Scratch is the best book for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence, Machine Learning and Data Science from scratch. It will help you in building a solid foundation and make you prepare for any other high-level courses.
Book include Step By Step Guide, Visual Illustrations and Examples
This book contains accompanying examples, that would be well suited to tackle most of the problems that came across building application.
Instead of tough math formulas, this book contains images and graphs which is very useful in getting the basics of machine learning in detail, and all other important Machine Learning concepts and their applications.
Variety of audiences targeted from this book. The most suitable users would include:
Anyone who is interested and fascinated about by how algorithms arrive at predictions but has no previous knowledge of the field. For software engineers and developers with a strong programming background seeking to break into the field of machine learning and artificial intelligence. Seasoned professionals in the field of artificial intelligence and machine learning who desire a bird’s eye view of current approaches and techniques.
What’s Inside This Book?
• Supervised Learning Algorithms Unsupervised Learning Algorithms
• Semi-supervised Learning Algorithms
• Reinforcement Learning Algorithms
• Overfitting and underfitting correctness
• The Bias-Variance Trade-off Feature
• Extraction and Selection