Introduction to Machine Learning with Python PDF

Introduction to Machine Learning with Python EPUB

Download Introduction to Machine Learning with Python EPUB by  Andreas C. Müller, and Sarah Guido published in 2016.

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    Contents

    1. Introduction/Definition
    2. Where and Why ML is used
    3. Types of Learning
    4. Supervised Learning – Linear Regression & GradientDescent
    5. Code Example
    6. Unsupervised Learning – Clustering, and K-Means
    7. Code Example
    8. Neural Networks
    9. Code Example
    10. Introduction to Scikit-Learn

    Inside this book

    Supervised Learning: – The set of data (training data) consists of a set of input data and correct responses corresponding to every piece of data. – Based on this training data, the algorithm has to generalize such that it is able to correctly (or with a low margin of error) respond to all possible inputs.. – In essence: The algorithm should produce sensible outputs for inputs that weren’t encountered during training. – Also called learning from exemplars
    Supervised Learning: Classification Problems “ Consists of taking input vectors and deciding which of the N classes they belong to, based on training from exemplars of each class.“ – Is discrete (most of the time). i.e. an example belongs to precisely one class, and the set of classes covers the whole possible output space. How it’s done: Find ‘decision boundaries’ that can be used to separate out the different classes. Given the features that are used as inputs to the classifier, we need to identify some values of those features that will enable us to decide which class the current input belongs to

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