Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow - Step-by-Step Tutorial for Beginners
Language: English
Published by END OF LINE CLEARANCE BOOK, 2019
- Softcover
- Used
Condition: Used - As new
£ 17.04
Quantity: 1 available
Add to basketItem description from seller
Leichte Kratzer / Abnutzungen / Druckstellen. Want to learn Machine Learning and Deep Learning with Python, Scikit-Learn, and TensorFlow but don't know where to start? This book is perfect for you--no need for boring, expensive textbooks. The author covers everything from the basics in simple language, providing numerous examples and program outputs through screenshots. The content is organized chronologically and step-by-step. The book aims to help you grasp the fundamentals of machine learning and deep learning, understand various algorithm categories, and implement them programmatically in Python. You'll also learn to use Scikit-Learn and TensorFlow libraries, as well as analyze data to uncover patterns and relationships. Target readers include complete beginners, those looking to enhance their Python programming skills for machine learning, data science professionals, educators seeking effective teaching methods, and students focused on neural networks and deep learning. To get started, you need to have Python 3.X, along with Numpy, Pandas, and Matplotlib installed. The author guides you through installing other necessary libraries. Inside, you'll find topics like environment setup, linear regression, k-nearest neighbors, K-means clustering, support vector machines, neural networks, random forests, and recurrent neural networks. This book provides valuable insights into machine learning classifiers, helping you set up a learning pipeline that processes data and ap.…
Seller Inventory # 11cada27-e9f8-4adf-b8af-8618e090208c
- Title
- Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow - Step-by-Step Tutorial for Beginners
- Author
- Samuel Burns
- Publisher
- END OF LINE CLEARANCE BOOK
- Publication year
- 2019
- Condition
- As New
- Binding
- Softcover
- Language
- English
- ISBN 10
- 1793175853
- ISBN 13
- 9781793175854
Why this book is the best one for data scientist?
Here are the reasons:The author has explored everything about machine learning and deep learning right from the basics.- A simple language has been used.
- Many examples have been given, both theoretically and programmatically.
- Screenshots showing program outputs have been added.
- The book is written chronologically, in a step-by-step manner
Book Objectives:
The Aims and Objectives of the Book:- To help you understand the basics of machine learning and deep learning.
- Understand the various categoriesof machine learning algorithms.
- To help you understand how different machine learning algorithms work.
- You will learn how to implement various machine learning algorithms programmatically in Python.
- To help you learn how to use Scikit-Learn and TensorFlow Libraries in Python.
- To help you know how to analyze data programmatically to extract patterns, trends, and relationships between variables.
Who this Book is for?
Here are the target readers for this book:- Anybody who is a complete beginner to machine learning in Python.
- Anybody who needs to advance their programming skills in Python for machine learning programming and deep learning.
- Professionals in data science.
- Professors, lecturers or tutors who are looking to find better ways to explain machine learning to their students in the simplest and easiest way.
- Students and academicians, especially those focusing on neural networks, machine learning, and deep learning.
What do you need for this Book?
You are required to have installed the following on your computer:- Python 3.X
- Numpy
- Pandas
- Matplotlib
The Author guides you on how to install the rest of the Python libraries that are required for machine learning and deep learning.
What is inside the book:
- Getting Started
- Environment Setup
- Using Scikit-Learn
- Linear Regression with Scikit-Learn
- k-Nearest Neighbors Algorithm
- K-Means Clustering
- Support Vector Machines
- Neural Networks with Scikit-learn
- Random Forest Algorithm
- Using TensorFlow
- Recurrent Neural Networks with TensorFlow
- Linear Classifier
"Synopsis" may belong to another edition of this title.
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