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ch03/README.md

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+Python Machine Learning - Code Examples
+
+
+##  Chapter 3: A Tour of Machine Learning Classifiers Using scikit-learn
+
+### Chapter Outline
+
+- Choosing a classification algorithm
+- First steps with scikit-learn -- training a perceptron
+- Modeling class probabilities via logistic regression
+  - Logistic regression intuition and conditional probabilities
+  - Learning the weights of the logistic cost function
+  - Converting an Adaline implementation into an algorithm for logistic regression
+  - Training a logistic regression model with scikit-learn
+  - Tackling over tting via regularization
+- Maximum margin classification with support vector machines
+  - Maximum margin intuition
+  - Dealing with a nonlinearly separable case using slack variables
+  - Alternative implementations in scikit-learn
+- Solving nonlinear problems using a kernel SVM
+  - Kernel methods for linearly inseparable data
+  - Using the kernel trick to find separating hyperplanes in high-dimensional space 
+- Decision tree learning
+  - Maximizing information gain – getting the most bang for your buck
+  - Building a decision tree
+  - Combining multiple decision trees via random forests
+- K-nearest neighbors – a lazy learning algorithm
+- Summary
+
+### A note on using the code examples
+
+The recommended way to interact with the code examples in this book is via Jupyter Notebook (the `.ipynb` files). Using Jupyter Notebook, you will be able to execute the code step by step and have all the resulting outputs (including plots and images) all in one convenient document.
+
+![](../ch02/images/jupyter-example-1.png)
+
+
+
+Setting up Jupyter Notebook is really easy: if you are using the Anaconda Python distribution, all you need to install jupyter notebook is to execute the following command in your terminal:
+
+    conda install jupyter notebook
+
+Then you can launch jupyter notebook by executing
+
+    jupyter notebook
+
+A window will open up in your browser, which you can then use to navigate to the target directory that contains the `.ipynb` file you wish to open.
+
+**More installation and setup instructions can be found in the [README.md file of Chapter 1](../ch01/README.md)**.
+
+**(Even if you decide not to install Jupyter Notebook, note that you can also view the notebook files on GitHub by simply clicking on them: [`ch03.ipynb`](ch03.ipynb))**
+
+In addition to the code examples, I added a table of contents to each Jupyter notebook as well as section headers that are consistent with the content of the book. Also, I included the original images and figures in hope that these make it easier to navigate and work with the code interactively as you are reading the book.
+
+![](../ch02/images/jupyter-example-2.png)
+
+
+When I was creating these notebooks, I was hoping to make your reading (and coding) experience as convenient as possible! However, if you don't wish to use Jupyter Notebooks, I also converted these notebooks to regular Python script files (`.py` files) that can be viewed and edited in any plaintext editor.