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Python Machine Learning - Code Examples


## Chapter 8: Applying Machine Learning to Sentiment Analysis

### Chapter Outline

- Artificial neurons – a brief glimpse into the early history of machine learning
- The formal definition of an artificial neuron
- The perceptron learning rule
- Implementing a perceptron learning algorithm in Python
- An object-oriented perceptron API
- Training a perceptron model on the Iris dataset
- Adaptive linear neurons and the convergence of learning
- Minimizing cost functions with gradient descent
- Implementing an Adaptive Linear Neuron in Python
- Improving gradient descent through feature scaling
- Large scale machine learning and stochastic gradient descent
- 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: [`ch08.ipynb`](ch08.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.
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