02-skltopics.tex

		\[ \mbox{Machine Learning with Python} \]
	
	
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\textbf{Machine Learning}

*   Machine Learning is a discipline involving algorithms designed to find patterns in and make predictions about data. 
*   It is nearly ubiquitous in our world today, and used in everything from web searches to financial forecasts to studies of the nature of the Universe. *   This workshop will cover an introduction to scikit-learn, a python machine learning package, and to the central concepts of Machine Learning. 




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	\textbf{Machine Learning}\\
	We will introduce the basic categories of learning problems and how to implement them using scikit-learn. 
%	From this foundation, we will explore practical examples of machine learning using real-world data, from handwriting analysis to automated classification of astronomical images.


*   Regression : Predicting Numeric Values
*   Classification :  Predicting Categories
*   Clustering : assigning instances to groups.


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\textbf{Getting ready}
The datasets in scikit-learn are contained within the datasets module. Use the following
command to import these datasets:
<pre>
\begin{verbatim}
>>> from sklearn import datasets
>>> import numpy as np
\end{verbatim}
\end{framed}


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\centering
\includegraphics[width=1.2\linewidth]{images/SKLsite}

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\textbf{Classification}

*   \textbf{Description:} Identifying to which category an object belongs to.
*   \textbf{Applications:} Spam detection, Image recognition.
*   \textbf{Algorithms:} SVM, nearest neighbors, random forest, 



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\textbf{Regression}

*   \textbf{Description:} Predicting a continuous-valued attribute associated with an object.
*   \textbf{Applications:} Drug response, Stock prices.
*   \textbf{Algorithms:} SVR, ridge regression, Lasso, 




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\textbf{Clustering}\\

Automatic grouping of similar objects into sets.
\begin{description}
*  [Applications:] Customer segmentation, Grouping experiment outcomes
*  [Algorithms:] k-Means, spectral clustering, mean-shift, ...
\end{description}

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\textbf{Dimensionality Reduction}\\


*   \textbf{Description: } Reducing the number of random variables to consider.
*   \textbf{Applications:} Visualization, Increased efficiency
*   \textbf{Algorithms:} PCA, feature selection, non-negative matrix factorization. 


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\textbf{Model selection}\\

*   \textbf{Description: } Comparing, validating and choosing parameters and models.
*   \textbf{Goal:} Improved accuracy via parameter tuning
*   \textbf{Modules:} grid search, cross validation, metrics


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\textbf{Preprocessing}\\

*   \textbf{Description:} Feature extraction and normalization.
*   \textbf{Application:} Transforming input data such as text for use with machine learning algorithms.
*   \textbf{Modules:} preprocessing, feature extraction.



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