#!/usr/bin/env python
# coding: utf-8

# Copyright (c) 2015 - 2017 [Sebastian Raschka](sebastianraschka.com)
# 
# https://github.com/rasbt/python-machine-learning-book
# 
# [MIT License](https://github.com/rasbt/python-machine-learning-book/blob/master/LICENSE.txt)

# # Python Machine Learning - Code Examples

# # Chapter 1 - Giving Computers the Ability to Learn from Data

# ### Overview

# - [Building intelligent machines to transform data into knowledge](#Building-intelligent-machines-to-transform-data-into-knowledge)
# - [The three different types of machine learning](#The-three-different-types-of-machine-learning)
#     - [Making predictions about the future with supervised learning](#Making-predictions-about-the-future-with-supervised-learning)
#         - [Classification for predicting class labels](#Classification-for-predicting-class-labels)
#         - [Regression for predicting continuous outcomes](#Regression-for-predicting-continuous-outcomes)
#     - [Solving interactive problems with reinforcement learning](#Solving-interactive-problems-with-reinforcement-learning)
#     - [Discovering hidden structures with unsupervised 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](#Dimensionality-reduction-for-data-compression)
#         - [An introduction to the basic terminology and notations](#An-introduction-to-the-basic-terminology-and-notations)
# - [A roadmap for building machine learning systems](#A-roadmap-for-building-machine-learning-systems)
#     - [Preprocessing - getting data into shape](#Preprocessing--getting-data-into-shape)
#     - [Training and selecting a predictive model](#Training-and-selecting-a-predictive-model)
#     - [Evaluating models and predicting unseen data instances](#Evaluating-models-and-predicting-unseen-data-instances)
# - [Using Python for machine learning](#Using-Python-for-machine-learning)
# - [Installing Python packages](#Installing-Python-packages)
# - [Summary](#Summary)

# <br>
# <br>

# In[1]:


from IPython.display import Image


# # Building intelligent machines to transform data into knowledge

# ...

# # The three different types of machine learning

# In[2]:


Image(filename='./images/01_01.png', width=500) 


# <br>
# <br>

# ## Making predictions about the future with supervised learning

# In[3]:


Image(filename='./images/01_02.png', width=500) 


# <br>
# <br>

# ### Classification for predicting class labels

# In[4]:


Image(filename='./images/01_03.png', width=300) 


# <br>
# <br>

# ### Regression for predicting continuous outcomes

# In[5]:


Image(filename='./images/01_04.png', width=300) 


# <br>
# <br>

# ## Solving interactive problems with reinforcement learning

# In[6]:


Image(filename='./images/01_05.png', width=300) 


# <br>
# <br>

# ## Discovering hidden structures with unsupervised learning

# ...

# ### Finding subgroups with clustering

# In[7]:


Image(filename='./images/01_06.png', width=300) 


# <br>
# <br>

# ### Dimensionality reduction for data compression

# In[8]:


Image(filename='./images/01_07.png', width=500) 


# <br>
# <br>

# ### An introduction to the basic terminology and notations

# In[9]:


Image(filename='./images/01_08.png', width=500) 


# <br>
# <br>

# # A roadmap for building machine learning systems

# In[10]:


Image(filename='./images/01_09.png', width=700) 


# <br>
# <br>

# ## Preprocessing - getting data into shape

# ...

# ## Training and selecting a predictive model

# ...

# ## Evaluating models and predicting unseen data instances

# ...

# # Using Python for machine learning

# ...

# # Installing Python packages

# ...

# # Summary

# ...