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Machine Learning Concepts with Python and the Jupyter Notebook Environment

Create, execute, modify, and share machine learning applications with Python and TensorFlow 2.0 in the Jupyter Notebook environment. This;

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Learning Jupyter 5

cleaning and transformation, numerical simulation, statistical modeling, and machine learning. Learning Jupyter 5 will help you get to grips with;

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Learning Jupyter

simulation, statistical modeling, machine learning, and much more. This book starts with a detailed overview of the Jupyter Notebook system and its;

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The The Applied Data Science Workshop

learning Explore the different functionalities and features of a Jupyter Notebook Discover how Python libraries are used with Jupyter for data;

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Docker for Data Science

-scale data sets, using Jupyter as the master controller. It is not uncommon for a real-world data set to fail to be easily managed. The set may;

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Foundational Python for Data Science

through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data;

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IPython Interactive Computing and Visualization Cookbook

Learn to use IPython and Jupyter Notebook for your data analysis and visualization work. Key Features Leverage the Jupyter Notebook for;

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Jupyter Cookbook

Leverage the power of the popular Jupyter notebooks to simplify your data science tasks without any hassle Key Features Create and share;

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Beginning Data Science with Python and Jupyter

Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers;

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Learning Ipython for Interactive Computing and Data Visualization

Get started with Python for data analysis and numerical computing in the Jupyter notebook About This Book * Learn the basics;

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Deep Learning with Structured Data

performance of your models For readers with an intermediate knowledge of Python, Jupyter notebooks, and machine learning.;

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Machine Learning with TensorFlow

This fully revised second edition of Machine Learning with TensorFlow teaches you the foundational concepts of machine learning and how to;

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Applied Deep Learning with TensorFlow 2

fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects. This;

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Applied Deep Learning with TensorFlow 2

fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects. This book;

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Data Science

Data Science: An Introduction focuses on using the R programming language in Jupyter notebooks to perform basic data manipulation and;

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The Art of Feature Engineering

When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model;

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Deploy Machine Learning Models to Production

Build and deploy machine learning and deep learning models in production with end-to-end examples. This book begins with a focus on the;

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Practical Python Data Visualization

. You'll begin by installing Python 3, see how to work in Jupyter notebook, and explore Leather, Python's popular data visualization charting;

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Practical Python Data Visualization

begin by installing Python 3, see how to work in Jupyter notebook, and explore Leather, Pythons popular data visualization charting library;

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Deep Learning Illustrated

Deep learning is one of today's hottest fields. This approach to machine learning is achieving breakthrough results in some of today's;

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Python for Programmers

individual snippets to 40 large scripts and full implementation case studies, you'll use the interactive IPython interpreter with code in Jupyter;

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Business Analytics

foundational understanding of: 1. statistical, econometric, and machine learning techniques; 2. data handling capabilities; 3;

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TensorFlow 2.0 Quick Start Guide

Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks. Key Features Train;

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Python Data Science Handbook

: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models;

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Machine Learning with Microsoft Technologies

, Azure ML Server Developer, Anaconda Python, Jupyter Notebook, Power BI Desktop, Cognitive Services, machine learning and data analytics tools;

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Machine Learning with Microsoft Technologies

Know how to do machine learning with Microsoft technologies. This book teaches you to do predictive, descriptive, and prescriptive analyses;

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Applied Machine Learning for Health and Fitness

Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other;

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