Build Machine Learning Applications with Linux, Python, and Spark


Using a scenario-based, outside-in development methodology, you will build and assemble modules built from Linux, Python, and Spark into a Machine Learning Application.

* You start by finding observations data on the web which is information rich (financial orsports data are good choices).
* You will use Linux to pull new observations into a data store (CSV, Postgres, or HDFS perhaps) each minute (or hour or day).
* You will use Python (Pandas, NumPy, psycopg2, SFrame, PySpark) to transform observations into taining data and test data.
* You will use Python APIs of respected Machine Learning libraries to learn from data (scikit-learn, Theano, and TensorFlow).
* From your Machine Learning models you will predict past observations and then gauge accuracy and effectiveness of your models.
* You will use Python Data Visualization technology to show model behavior to your end-users: Matplotlib, Bokeh
* You will use Python web technology to serve visualizations (and API data) to your end-users: Django, Flask
* You will use cloud technology to present predictions from your Machine Learning Application to end-users and investors: Amazon EC2, Heroku
* You will use Linux and Python to monitor your Machine Learning Application to maximize its uptime and performance: urllib, BeautifulSoup, Selenium

For more information go to  Course Outline

Prerequisite: Having taken the Programming in Python course at the High Tech Academy or working familiarity with Python.

Textbook: Discussed in class


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