Python Deep Learning: Develop Your First Neural Network in Python Using Tensorflow, Keras, and Pytorch, Samuel Burns
Автор: Samuel Burns Название: Python for Data Analysis: Master the Basics of Data Analysis in Python Using Numpy, Pandas and Ipython ISBN: 1091253900 ISBN-13(EAN): 9781091253902 Издательство: Неизвестно Цена: 16080.00 T Наличие на складе: Невозможна поставка. Описание: You want to learn Python for data analysis using NumPy, Pandas, and IPython, and you don't know how to start? You don't need a big boring and expensive textbook. This book is the best one for everyone.Get your copy Now Why this book? Here are the reasons: The author has explored everything about python for data analysis using pandas, NumPy, Ipython and Matplotlib libraries 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 why you should choose Python for data analysis tasks.
To help you know the various data analysis libraries supported by Python and how to use them.
To help you know how to analyze your business data and draw meaningful insights for effective decision making.
To equip you with data analysis skills using Python programming language.
To help you know where data analysis is applied today and how to use it in your everyday life.
Who is this Book is for?: Here are the target readers for this book:
Anybody who is a complete beginner to data analysis with Python or data analysis in general.
Anybody who wants to advance their data analysis skills with Python programming language.
Anybody who wants to know how to use data analysis for the benefit of their business or brand.
Professionals in data science, computer programming, computer scientist.
Professors, lecturers or tutors who are looking to find better ways to explain python for data analysis to their students in the simplest and easiest way.
Students and academicians, especially those focusing on python programming, computer science, 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 and configure the rest of the Python libraries that are required for data analysis.What is inside the book?:
Why Python for Data Analysis?
Exploring the Libraries
Installation and Setup
Using IPython
Numpy Arrays and Vectorized Computation
Pandas Library
Data Wrangling
Data Visualization
Data Aggregation
Working with Time Series Data
Applications of Data Analysis Today
The content of this book is all about data analysis with Python programming language using NumPy, Pandas, and IPython. It has been grouped into chapters, with each chapter exploring a different aspect of data analysis. The author has provided Python codes for doing different data analysis tasks. All these codes have been tested to ensure they are working correctly. Corresponding explanations have also been provided alongside each piece of code to help the reader understand the meaning of the various lines of the code. In addition to this, screenshots showing the output that each code should return have been given. The author has used a simple language to make it easy even for beginners to understand. The author begins by exploring the basic to the complex tasks in data analysis.
Автор: Samuel Burns Название: Python Machine Learning: Machine Learning and Deep Learning with Python, Scikit-Learn and Tensorflow ISBN: 1090434162 ISBN-13(EAN): 9781090434166 Издательство: Неизвестно Цена: 17230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: You are interested in becoming a machine learning expert but don't know where to start from? Don't worry you don't need a big boring and expensive Textbook. This book is the best guide for you. Get your copy NOW 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.
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 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
This book will teach you machine learning classifiers using scikit-learn and tenserflow . The book provides a great overview of functions you can use to build a support vector machine, decision tree, perceptron, and k-nearest neighbors. Thanks of this book you will be able to set up a learning pipeline that handles input and output data, pre-processes it, selects meaningful features, and applies a classifier on it. This book offers a lot of insight into machine learning for both beginners, as well as for professionals, who already use some machine learning techniques. Concepts and the background of these concepts are explained clearly in this tutorial.
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