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Python tutorials in both Jupyter Notebook and youtube format.
Installations for Data Science. Anaconda, RStudio, Spark, TensorFlow, AWS (Amazon Web Services).
POSTS BY MICHAEL
Making your First Machine Learning Classifier in Scikit-learn (Python)
One of the most amazing things about Python's scikit-learn library is that is has a 4-step modeling pattern that makes it easy to code a machine learning classifier. While this tutorial uses a classifier called Logistic Regression, the coding process in this tutorial applies to other classifiers in sklearn (Decision Tree, K-Nearest Neighbors etc). In this tutorial, we use Logistic Regression to predict digit labels based on images. The image above shows a bunch of training digits (observations) from the MNIST dataset whose category membership is known (labels 0–9). After training a model with logistic regression, it can be used to predict an image label (labels 0–9) given an image.
Web Scraping in Python using Scrapy
When I first started working in industry, one of the things I quickly realized is sometimes you have to gather, organize, and clean your own data. For this tutorial, we will gather data from a crowdfunding website called FundRazr. Like many websites, the site has its own structure, form, and has tons of accessible useful data, but it is hard to get data from the site as it doesn’t have a structured API. In result, we will web scrape the site to get that unstructured website data and put into an ordered form to build our own dataset.