This blog about machine learning was written by Emily Barry. Emily is a Data Scientist in San Francisco, California. She really loves emoji. Another thing she loves is data science. The more she learns about machine learning algorithms, the more challenging it is to keep these subjects organized in her brain to recall at a later time. So, she decided to marry these two loves in as productive a fashion as possible.

This is by no means a comprehensive guide to machine learning, but rather a study in the basics for herself and the likely small overlap of people who like machine learning and love emoji as much as she do.

A step-by-step guide for this sheet: 

• Learning Styles
• Regressions
• Classification
• Clustering
• The Curse of Dimensionality
• Our * Wildcard * Section

Check out all this information, here. For more articles about machine learning, click here. For other cheat sheets, click here. 

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Additional Reading

• What statisticians think about data scientists
• Data Science Compared to 16 Analytic Disciplines
• 10 types of data scientists
• 91 job interview questions for data scientists
• 50 Questions to Test True Data Science Knowledge
• 24 Uses of Statistical Modeling
• 21 data science systems used by Amazon to operate its business
• Top 20 Big Data Experts to Follow (Includes Scoring Algorithm)
• 5 Data Science Leaders Share their Predictions for 2016 and Beyond
• 50 Articles about Hadoop and Related Topics
• 10 Modern Statistical Concepts Discovered by Data Scientists
• Top data science keywords on DSC
• 4 easy steps to becoming a data scientist
• 22 tips for better data science
• How to detect spurious correlations, and how to find the real ones
• 17 short tutorials all data scientists should read (and practice)
• High versus low-level data science

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