Machine Learning With Python For Beginners: A Step-By-Step Guide with Hands-On Projects (Learn Coding Fast with Hands-On Project, Band 7)

Chan, Jamie

ISBN 13: 9798549178373

Verlag: Independently published, 2021

Beschreibung

Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

Bestandsnummer des Verkäufers M0B09BT7YCCM-V

Bibliografische Details

• Titel: Machine Learning With Python For Beginners: A Step-By-Step Guide with Hands-On Projects (Learn Coding Fast with Hands-On Project, Band 7)
• Verlag: Independently published
• Veröffentlichungsdatum: 2021
• Sprache: Englisch
• ISBN-13: 9798549178373
• Bindung: Softcover
• Zustand: very good

Über diesen Titel

Inhaltsangabe

A Step-By-Step Beginner's Guide to Machine Learning

Have you always been curious about machine learning but do not know where to start. Or perhaps your new job requires you to learn machine learning but you are overwhelmed with all the information available online.

What is machine learning? What is Scikit-Learn? What does the fit() method that you see on so many online tutorials do?

Does machine learning seem like a black-box to you?

This book is for you.

You no longer have to spend hours watching videos online or reading through tutorials that assume you are familiar with essential scientific and machine learning libraries in Python. This book answers all the questions above and more.

Pre-Requisites

The book assumes that you are familiar with basic Python, especially with the concept of object-oriented programming in Python. If you are not familiar, you can check out my introductory book "Learn Python in one day and Learn It Well (2nd Edition)" available at https://www.amazon.com/dp/1546488332.

What this book offers?

Machine Learning for Beginners

Complex concepts are broken down into simple steps and examples are carefully chosen to illustrate each concept. Mathematical concepts are explained without complicated notations and formulas.

Hands-On Approach

Countless examples are provided for you to try out in each chapter, so that you can understand exactly what different machine learning methods do.

Systematic Approach

A systematic approach is taken to provide you with the background knowledge needed before covering advanced concepts.

How is this book different?

The best way to learn anything is by doing.

This book includes three hands-on projects at the end of the book for you to apply and practice all the concepts taught previously.

Working through the projects will not only give you an immense sense of achievement, it'll also help you retain the knowledge and solidify your understanding.

Whether you are an aspiring data scientist or just curious about machine learning, the book is designed to help you grasp the fundamental concepts of machine learning in a systematic and step-by-step fashion.

Are you ready to dip your toes into the exciting world of Machine Learning? This book is for you. Click the "Add to Cart" button to buy it now.

What you'll learn:

• What is Machine Learning
• What is supervised, unsupervised, and reinforcement learning
• How to use the NumPy and pandas library
• How to use matplotlib to plot charts
• What is the Scikit-Learn library?
• What do the fit() and transform() methods do
• How to pre-process our data
• How to use pipelines and column transformers to streamline our code
• How to evaluate our models
• What is a confusion matrix and how to interpret it
• What is regression, classification, and clustering
• What is the theory behind the linear regression, poly regression, decision tree, random forest, SVM, and k-means clustering algorithms
• How to do a grid search to find the best hyperparameters
• What is regularization
• How to reduce the dimensions of our dataset

and more...

Finally, you'll be guided through three hands-on projects that require the application of all the topics covered.

Click the "Add to Cart" button now to start learning machine learning and build your own models.