About the course

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. The course is based on the “Intro to Machine Learning” that is provided by Udicity under the supervision of Sebastian Thrun, who is a research professor at Stanford University, and the founder of Udicity with Katie Malon who is a physicist that uses ML in her researches to analysis, monitor, and enhance data.

Outline

• Welcome to Machine Learning

• Naive Bayes

• SVM

• Decision Trees

• Choose your algorithm

• Datasets and questions

• Regressions

• Outliers

• Clustering

• Feature scaling

• Text learning

• Feature selection

• PCA

• Validation

• Evaluation metrics

• Final project

Are there any course requirements or prerequisites? : Yes, you will need to be familiar with Programming in general, and knowing python specifically is a PLUS.

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