Machine Learning Engineer Nanodegree from Udacity

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.

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Well-suited for those with intermediate Python, statistical, and calculus/linear algebra backgrounds

Instructors include notable figures in machine learning: Sebastian Thrun and Vincent Vanhoucke

Provides practical application of machine learning models in various industries, such as healthcare and education

May require additional resources for preparation due to the prerequisite knowledge required

Reviews summary

Comprehensive machine learning nanodegree

This course is geared towards individuals with a solid foundation in Python, statistics, calculus, and linear algebra. Students appreciate the course's breadth and depth, as well as the quality of its instructors and the connections drawn between different topics.

Demanding yet Rewarding

"I particularly appreciated..."

"...how we should choose..."

Broad and Deep

"An excellent overview..."

"...very little meat (maths) left."

Strong Foundation Needed

"Poor delivery, outdated..."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning Engineer Nanodegree with these activities:

Review Python programming concepts

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Refining foundational Python programming concepts will help you understand and apply predictive models to massive data sets.

Browse courses on Python Programming

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Review variables, data types, and basic arithmetic operations
Practice working with strings, lists, and dictionaries
Polish your skills in writing functions and modules

Practice supervised and unsupervised learning algorithms

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Working through these tutorials will strengthen your proficiency in applying machine learning algorithms to real-world scenarios.

Browse courses on Supervised Learning

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Find tutorials on different supervised and unsupervised learning algorithms
Follow the tutorials step-by-step and implement the algorithms in your own code
Test and evaluate your implementations on different datasets
Optional: Experiment with different hyperparameters to improve the performance of your algorithms

Solve practice problems on machine learning platforms

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Solving practice problems on platforms like Kaggle, LeetCode, and HackerRank will reinforce your understanding and help you develop problem-solving skills.

Browse courses on Kaggle

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Create an account on a machine learning practice platform
Start solving practice problems related to machine learning concepts
Optional: Participate in competitions to challenge yourself and learn from others

Show all three activities

Career center

Learners who complete Machine Learning Engineer Nanodegree will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer is responsible for designing, developing, and deploying machine learning models. This course provides a comprehensive overview of the machine learning lifecycle, from data collection and preparation to model training and deployment. You will learn about the different types of machine learning algorithms, how to evaluate their performance, and how to deploy them in production. By completing this course, you will gain the skills and knowledge necessary to succeed as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist is responsible for developing and maintaining mathematical and statistical models that can be used to predict future events or outcomes. This course helps build a foundation in machine learning, which is a subfield of data science that focuses on using data to train computers to learn. Machine learning is used in a variety of applications, such as predicting customer churn, identifying fraud, and recommending products. By completing this course, you will gain the skills and knowledge necessary to succeed as a Data Scientist.

See salaries and explore the career path for Data Scientist

Software Architect

A Software Architect is responsible for designing and developing the architecture of a software system. This course provides a foundation in the fundamentals of software architecture, including software design patterns, software quality assurance, and software testing. You will also learn about the different types of software architectures and how to use them to design and develop software systems. By completing this course, you will gain the skills and knowledge necessary to succeed as a Software Architect.

See salaries and explore the career path for Software Architect

Software Engineer

A Software Engineer is responsible for designing, developing, and maintaining software applications. This course provides a foundation in the fundamentals of software engineering, including object-oriented programming, data structures, and algorithms. You will also learn about the different phases of the software development lifecycle, from requirements gathering to deployment. By completing this course, you will gain the skills and knowledge necessary to succeed as a Software Engineer.

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Computer Scientist

A Computer Scientist is responsible for developing new algorithms and data structures to solve real-world problems. This course provides a foundation in the fundamentals of computer science, including algorithms, data structures, and complexity theory. You will also learn about the different types of computer science research areas and how to conduct computer science research. By completing this course, you will gain the skills and knowledge necessary to succeed as a Computer Scientist.

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Data Analyst

A Data Analyst is responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course provides a foundation in the fundamentals of data analysis, including statistics, data visualization, and data mining. You will also learn about the different types of data analysis techniques and how to use them to solve real-world problems. By completing this course, you will gain the skills and knowledge necessary to succeed as a Data Analyst.

See salaries and explore the career path for Data Analyst

Data Engineer

A Data Engineer is responsible for designing, building, and maintaining data pipelines. This course provides a foundation in the fundamentals of data engineering, including data integration, data cleansing, and data warehousing. You will also learn about the different types of data engineering tools and techniques and how to use them to build and maintain data pipelines. By completing this course, you will gain the skills and knowledge necessary to succeed as a Data Engineer.

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Quantitative Analyst

A Quantitative Analyst is responsible for using mathematical and statistical models to analyze financial data. This course provides a foundation in the fundamentals of quantitative analysis, including probability, statistics, and financial modeling. You will also learn about the different types of quantitative analysis techniques and how to use them to make investment decisions. By completing this course, you will gain the skills and knowledge necessary to succeed as a Quantitative Analyst.

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Risk Analyst

A Risk Analyst is responsible for identifying and assessing risks to an organization. This course provides a foundation in the fundamentals of risk management, including risk identification, risk assessment, and risk mitigation. You will also learn about the different types of risks that an organization can face and how to develop strategies to mitigate them. By completing this course, you will gain the skills and knowledge necessary to succeed as a Risk Analyst.

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Actuary

An Actuary is responsible for using mathematical and statistical models to assess risk and uncertainty. This course provides a foundation in the fundamentals of actuarial science, including probability, statistics, and financial modeling. You will also learn about the different types of actuarial analysis techniques and how to use them to develop insurance and pension plans. By completing this course, you will gain the skills and knowledge necessary to succeed as an Actuary.

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Operations Research Analyst

An Operations Research Analyst is responsible for using mathematical and statistical models to improve the efficiency of an organization. This course provides a foundation in the fundamentals of operations research, including linear programming, network optimization, and simulation. You will also learn about the different types of operations research techniques and how to use them to solve real-world problems. By completing this course, you will gain the skills and knowledge necessary to succeed as an Operations Research Analyst.

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Business Analyst

A Business Analyst is responsible for identifying and solving business problems. This course provides a foundation in the fundamentals of business analysis, including problem solving, stakeholder management, and data analysis. You will also learn about the different types of business analysis techniques and how to use them to improve business outcomes. By completing this course, you will gain the skills and knowledge necessary to succeed as a Business Analyst.

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Statistician

A Statistician is responsible for collecting, analyzing, and interpreting data. This course provides a foundation in the fundamentals of statistics, including probability, sampling, and hypothesis testing. You will also learn about the different types of statistical analysis techniques and how to use them to solve real-world problems. By completing this course, you will gain the skills and knowledge necessary to succeed as a Statistician.

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Database Administrator

A Database Administrator is responsible for designing, building, and maintaining databases. This course provides a foundation in the fundamentals of database administration, including database design, database optimization, and data recovery. You will also learn about the different types of databases and how to use them to store and manage data. By completing this course, you will gain the skills and knowledge necessary to succeed as a Database Administrator.

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Financial Analyst

A Financial Analyst is responsible for analyzing financial data to make investment decisions. This course provides a foundation in the fundamentals of financial analysis, including financial statement analysis, valuation, and portfolio management. You will also learn about the different types of financial analysis techniques and how to use them to make investment decisions. By completing this course, you will gain the skills and knowledge necessary to succeed as a Financial Analyst.

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