Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn and Tensorflow - Step-by-Step Tutorial for Beginners
Language: English
Published by Independently published, 2019
- Softcover
- Used
Condition: Used - As new
£ 19.00
Quantity: 1 available
Add to basketItem description from seller
Are you eager to become a machine learning expert but unsure where to begin? You don't need a tedious and costly textbook. This guide is perfect for you! It covers machine learning and deep learning from the ground up, using simple language and numerous examples, both theoretical and programmatic, along with helpful screenshots of program outputs. 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 learn how to implement algorithms programmatically in Python. You will also explore the use of Scikit-Learn and TensorFlow libraries, as well as techniques to analyze data for patterns and trends. Target readers include complete beginners in machine learning with Python, those looking to enhance their programming skills, data science professionals, educators seeking effective teaching methods, and students focused on neural networks and deep learning. To get started, ensure you have Python 3.X, along with Numpy, Pandas, and Matplotlib installed. The author provides guidance on installing additional necessary libraries. Inside, you'll find topics such as environment setup, linear regression, k-nearest neighbors, K-means clustering, support vector machines, neural networks, and more. This book equips you to build classifiers using Scikit-Learn and TensorFlow, offering valuable insights for bo.…
Seller Inventory # c0604203-bb67-45a7-96a1-674a38c4ca28
- 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
- Independently published
- Publication year
- 2019
- Condition
- As New
- Binding
- Softcover
- Language
- English
- ISBN 10
- 1090434162
- ISBN 13
- 9781090434166
Why this guide 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.
Book Objectives:
The Aims and Objectives of the Book:- To help you understand the basics of machine learning and deep learning.
- Understand the various categories of 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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