Copyright (c) 2015 - 2017 Sebastian Raschka
Python Machine Learning - Code Examples¶
Chapter 1 - Giving Computers the Ability to Learn from Data¶
Overview¶
- Building intelligent machines to transform data into knowledge
- The three different types of machine learning
- Making predictions about the future with supervised learning
- Solving interactive problems with reinforcement learning
- Discovering hidden structures with unsupervised learning
- [Finding subgroups with clustering](#Finding-subgroups-with clustering-7 Dimensionality reduction for data compression)
- Dimensionality reduction for data compression
- An introduction to the basic terminology and notations
- A roadmap for building machine learning systems
- Using Python for machine learning
- Installing Python packages
- Summary
In [1]:
from IPython.display import Image
Building intelligent machines to transform data into knowledge¶
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The three different types of machine learning¶
In [2]:
Image(filename='./images/01_01.png', width=500)
Out[2]:
Making predictions about the future with supervised learning¶
In [3]:
Image(filename='./images/01_02.png', width=500)
Out[3]:
Classification for predicting class labels¶
In [4]:
Image(filename='./images/01_03.png', width=300)
Out[4]:
Regression for predicting continuous outcomes¶
In [5]:
Image(filename='./images/01_04.png', width=300)
Out[5]:
Solving interactive problems with reinforcement learning¶
In [6]:
Image(filename='./images/01_05.png', width=300)
Out[6]:
Discovering hidden structures with unsupervised learning¶
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Finding subgroups with clustering¶
In [7]:
Image(filename='./images/01_06.png', width=300)
Out[7]:
Dimensionality reduction for data compression¶
In [8]:
Image(filename='./images/01_07.png', width=500)
Out[8]:
An introduction to the basic terminology and notations¶
In [9]:
Image(filename='./images/01_08.png', width=500)
Out[9]:
A roadmap for building machine learning systems¶
In [10]:
Image(filename='./images/01_09.png', width=700)
Out[10]:
Preprocessing - getting data into shape¶
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Training and selecting a predictive model¶
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Evaluating models and predicting unseen data instances¶
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Using Python for machine learning¶
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Installing Python packages¶
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Summary¶
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