Before getting into training specific algorithms on this dataset, I want to briefly go over the machine learning process. This is often applied in many ML projects, so it's kind of nice to review it. In short, many ML projects occur in the following sequence:

1. Get the data. Preprocess it, if necessary.
2. Pick an algorithm or classifier to use.
3. Train the algorith. Check its accuracy and optimize, if needed.
4. Test the algorithm on new data. 

More details in the video...
___
As a reminder: 

In this series I'm going to explore the cancer dataset that comes pre-loaded with scikit-learn. The purpose is to train the classifiers on this dataset, which consists of labeled data: ~569 tumor samples, each labeled malignant or benign, and then use them on new, unlabeled data. 
___
Previous videos in this series:
1. [Machine Learning on a Cancer Dataset - Part 1](https://steemit.com/machine-learning/@cristi/machine-learning-on-a-cancer-dataset-part-1)
2. [Machine Learning on a Cancer Dataset - Part 2](https://steemit.com/machine-learning/@cristi/machine-learning-on-a-cancer-dataset-part-2)
3. [Machine Learning on a Cancer Dataset - Part 3](https://steemit.com/machine-learning/@cristi/machine-learning-on-a-cancer-dataset-part-3)
___
<center>https://www.youtube.com/watch?v=995PCli2Ny4</center>
___
### <center>To stay in touch with me, follow @cristi</center>

#machine-learning #science #python
___
[Cristi Vlad](http://cristivlad.com), Self-Experimenter and Author