Purpose

Put data science and machine learning to work with Python

Objectives

• Learn to use Python the data science stack
• Familiarization with several machine learning algorithms
• Evaluation of machine learning models
• Productionalization of machine learning tools

Audience

Software developers, and analysts wanting to level-up their data science and machine learning skills in Python

Prerequisites

Students should be familiar with basic programming concepts.

Familiarity with Python is helpful but not required.

Course Outline

• Introduction, installation, and overview
• The Python data science stack
  • Pandas for data manipulation
  • Numpy to speed up Python numerical processing
  • Matplotlib for visualization
• Introduction to machine learning and model building
  • Conceptual overview
  • Supervised vs unsupervised
  • Regression vs classification
  • Variance/bias tradeoff
• Python machine learning with Scikit-Learn models
  • Linear models
  • Trees & forests
  • k-nearest neighbors
  • k-means clustering
• Preprocessing
  • Advanced Pandas data-munging
  • Dealing with dates and feature engineering
  • Encoding categorical data
  • Scaling inputs
  • Dimensionality reduction
• Measuring performance
  • Train/test split
  • Tuning models with cross-validation
• Productionalization
  • Moving to modules
  • Working with git
  • Running notebooks in production with papermill

Please contact us for information on pricing and availability