# Machine Learning Engineer Nanodegree

Machine learning represents a key evolution in the fields of computer science, data analysis, software engineering, and artificial intelligence.

This program will teach you how to become a machine learning engineer, and apply predictive models to massive data sets in fields like finance, healthcare, education, and more.

This program will equip you with key skills that will prepare you to fill roles with companies seeking machine learning experts (or to introduce machine learning techniques to their organizations). Machine learning is literally everywhere, and is often at work even when we don’t realize it. Google Translate, Siri, and Facebook News Feeds are just a few popular examples of machine learning’s omnipresence. The ability to develop machines and systems that automatically improve, puts machine learning at the absolute forefront of virtually any field that relies on data. Prerequisites and Requirements:
• Intermediate Python programming knowledge, of the sort gained through the Introduction to Programming Nanodegree, other introductory programming courses or programs, or additional real-world software development experience. Including:
• Strings, numbers, and variables
• Statements, operators, and expressions
• Lists, tuples, and dictionaries
• Conditions, loops
• Procedures, objects, modules, and libraries
• Troubleshooting and debugging
• Research & documentation
• Problem solving
• Algorithms and data structures

• Intermediate statistical knowledge, of the sort gained through any of Udacity’s introductory statistics courses. Including:
• Populations, samples
• Mean, median, mode
• Standard error
• Variation, standard deviations
• Normal distribution
• Precision and accuracy

• Intermediate calculus and linear algebra mastery, addressed in the Linear Algebra Refresher Course, including:
• Derivatives
• Integrals
• Series expansions
• Matrix operations through eigenvectors and eigenvalues

Prior to entering the Machine Learning Engineer Nanodegree program, the student should have the following knowledge:

• Intermediate Python programming knowledge, of the sort gained through the Introduction to Programming Nanodegree, other introductory programming courses or programs, or additional real-world software development experience. Including:
• Strings, numbers, and variables
• Statements, operators, and expressions
• Lists, tuples, and dictionaries
• Conditions, loops
• Procedures, objects, modules, and libraries
• Troubleshooting and debugging
• Research & documentation
• Problem solving
• Algorithms and data structures

• Intermediate statistical knowledge, of the sort gained through any of Udacity’s introductory statistics courses. Including:
• Populations, samples
• Mean, median, mode
• Standard error
• Variation, standard deviations
• Normal distribution
• Precision and accuracy

• Intermediate calculus and linear algebra mastery, addressed in the Linear Algebra Refresher Course, including:
• Derivatives
• Integrals
• Series expansions
• Matrix operations through eigenvectors and eigenvalues

We have compiled additional resources for preparation here.

OpenCourser is an affiliate partner of Udacity and may earn a commission when you buy through our links.

Rating Not enough ratings On Demand (Start anytime) \$0 Udacity Arpan Chakraborty, David Joyner, Luis Serrano, Sebastian Thrun, Vincent Vanhoucke, Katie Malone Only via web browser English Programming Computer Science Software Development

## Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

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