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    Python Machine Learning - Second Edition

    Posted By: Grev27
    Python Machine Learning - Second Edition

    Sebastian Raschka,‎ Vahid Mirjalili, "Python Machine Learning - Second Edition"
    English | ISBN: 1787125939 | 2017 | EPUB/MOBI | 622 pages | 68,5 MB

    Key Features
    Second edition of the bestselling book on Machine Learning
    A practical approach to key frameworks in data science, machine learning, and deep learning
    Use the most powerful Python libraries to implement machine learning and deep learning
    Get to know the best practices to improve and optimize your machine learning systems and algorithms

    Book Description
    Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.

    Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.

    Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world.

    If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.

    What you will learn
    Understand the key frameworks in data science, machine learning, and deep learning
    Harness the power of the latest Python open source libraries in machine learning
    Explore machine learning techniques using challenging real-world data
    Master deep neural network implementation using the TensorFlow library
    Learn the mechanics of classification algorithms to implement the best tool for the job
    Predict continuous target outcomes using regression analysis
    Uncover hidden patterns and structures in data with clustering
    Delve deeper into textual and social media data using sentiment analysis

    Table of Contents
    Giving Computers the Ability to Learn from Data
    Training Simple Machine Learning Algorithms for Classification
    A Tour of Machine Learning Classifiers Using Scikit-Learn
    Building Good Training Sets - Data Preprocessing
    Compressing Data via Dimensionality Reduction
    Learning Best Practices for Model Evaluation and Hyperparameter Tuning
    Combining Different Models for Ensemble Learning
    Applying Machine Learning to Sentiment Analysis
    Embedding a Machine Learning Model into a Web Application
    Predicting Continuous Target Variables with Regression Analysis
    Working with Unlabeled Data - Clustering Analysis
    Implementing a Multilayer Artificial Neural Network from Scratch
    Parallelizing Neural Network Training with TensorFlow
    Going Deeper - The Mechanics of TensorFlow
    Classifying Images with Deep Convolutional Neural Networks
    Modeling Sequential Data using Recurrent Neural Networks