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Intro to Machine Learning

Created/Published/Taught by:
Katie Malone
Sebastian Thrun

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From Udacity:

“Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions.

Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions.

This is a class that will teach you the end-to-end process of investigating data through a machine learning lens. It will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms.”

This course consists of ten lessons:

  1. Welcome to Machine Learning
  2. Naive Bayes
  3. Support Vector Machines
  4. Decision Trees
  5. Choose your own Algorithm
  6. Datasets and Questions
  7. Regressions
  8. Outliers
  9. Clustering
  10. Feature Scaling

This course is also a part of the Udacity Data Analyst Nanodegree.

Recommended Prerequisites: "To succeed in this course, you must be proficient at programming in Python and basic statistics."

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