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Practical Machine Learning

Roger D. Peng, PhD, Jeff Leek, PhD, and Brian Caffo, PhD

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

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What's inside

Syllabus

Week 1: Prediction, Errors, and Cross Validation

This week will cover prediction, relative importance of steps, errors, and cross validation.

Week 2: The Caret Package

This week will introduce the caret package, tools for creating features and preprocessing.

Week 3: Predicting with trees, Random Forests, & Model Based Predictions

This week we introduce a number of machine learning algorithms you can use to complete your course project.

Week 4: Regularized Regression and Combining Predictors

This week, we will cover regularized regression and combining predictors.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Learners will develop core prediction and machine learning skills that are highly relevant in academia and industry

Students who wish to pursue data science or data analysis will benefit most from this course

Students with background knowledge in statistics and programming may find this course more accessible

This course is a part of a series and may be best taken in order with the other courses in the series

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Reviews summary

Practical machine learning techniques

Learners say that Practical Machine Learning is a largely positive course that gives an engaging introduction to machine learning using R. It covers a wide range of topics, including supervised and unsupervised learning, and provides plenty of opportunities to practice what you learn. Quizzes are invaluable and can greatly enhance knowledge of the subject if done with a purpose. However, it is important to note that the quizzes and assignments may have some outdated content, requiring additional effort to complete. Despite this, the course is a well-received resource for those interested in learning practical data science techniques.

Lots of practical exercises

"Nice course with many practical exercises and useful information."

Helpful, but may be outdated

"The quizzes and peer assignments are invaluable and if done with a purpose can augment knowledge of the subject immensely."

Some content may be out of date

"This course is getting too old."

"I found this extremely frustrating and disheartening and had to repeat the quizzes several times."

Career center

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

Data Scientist

Data Scientists use their knowledge of machine learning, statistics, and data analysis to extract insights from data. This course may be helpful for those wishing to enter this field as it provides a comprehensive overview of the machine learning process, from data collection to evaluation.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts use their skills in data analysis and machine learning to identify trends and patterns in data. This course may be useful for those wishing to enter this field as it provides a strong foundation in the fundamentals of machine learning, including topics such as data collection, feature creation, algorithms, and evaluation.

See salaries and explore the career path for Data Analyst

Machine Learning Engineer

Machine Learning Engineers apply their understanding of machine learning theory to develop and implement machine learning solutions to real-world problems. This course may be useful for those wishing to enter this field as it provides a solid foundation in the fundamentals of machine learning, including topics such as data collection, feature creation, algorithms, and evaluation.

See salaries and explore the career path for Machine Learning Engineer

Statistician

Statisticians use their knowledge of statistics and data analysis to collect, analyze, and interpret data. This course may be useful for those wishing to enter this field as it provides a strong foundation in the fundamentals of machine learning, including topics such as data collection, feature creation, algorithms, and evaluation.

See salaries and explore the career path for Statistician

Consultant

Consultants use their knowledge of a variety of fields to help organizations solve problems and improve performance. This course may be useful for those wishing to enter this field as it provides a solid foundation in the fundamentals of machine learning, including topics such as data collection, feature creation, algorithms, and evaluation.

See salaries and explore the career path for Consultant

Business Analyst

Business Analysts use their knowledge of business, statistics, and economics to analyze business data and make recommendations for improvement. This course may be useful for those wishing to enter this field as it provides a solid foundation in the fundamentals of machine learning, including topics such as data collection, feature creation, algorithms, and evaluation.

See salaries and explore the career path for Business Analyst

Quantitative Analyst

Quantitative Analysts use their knowledge of mathematics, statistics, and computer science to develop and implement trading strategies. This course may be useful for those wishing to enter this field as it provides a solid foundation in the fundamentals of machine learning, including topics such as data collection, feature creation, algorithms, and evaluation.

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Risk Manager

Risk Managers use their knowledge of statistics, finance, and economics to assess and manage risk. This course may be useful for those wishing to enter this field as it provides a solid foundation in the fundamentals of machine learning, including topics such as data collection, feature creation, algorithms, and evaluation.

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Operations Research Analyst

Operations Research Analysts use their knowledge of mathematics, statistics, and computer science to solve complex problems in a variety of industries. This course may be useful for those wishing to enter this field as it provides a solid foundation in the fundamentals of machine learning, including topics such as data collection, feature creation, algorithms, and evaluation.

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Software Engineer

Software Engineers apply their knowledge of computer science to design, develop, and maintain software systems. This course may be useful for those wishing to enter this field as it provides a solid foundation in the fundamentals of machine learning, including topics such as data collection, feature creation, algorithms, and evaluation.

See salaries and explore the career path for Software Engineer

Market Researcher

Market Researchers use their knowledge of marketing and statistics to collect and analyze data about consumer behavior. This course may be useful for those wishing to enter this field as it provides a solid foundation in the fundamentals of machine learning, including topics such as data collection, feature creation, algorithms, and evaluation.

See salaries and explore the career path for Market Researcher

Financial Analyst

Financial Analysts use their knowledge of finance and economics to analyze financial data and make investment recommendations. This course may be useful for those wishing to enter this field as it provides a solid foundation in the fundamentals of machine learning, including topics such as data collection, feature creation, algorithms, and evaluation.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries use their knowledge of mathematics, statistics, and economics to assess risk and uncertainty. This course may be useful for those wishing to enter this field as it provides a solid foundation in the fundamentals of machine learning, including topics such as data collection, feature creation, algorithms, and evaluation.

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Epidemiologist

Epidemiologists use their knowledge of statistics and public health to investigate the causes and spread of disease. This course may be useful for those wishing to enter this field as it provides a solid foundation in the fundamentals of machine learning, including topics such as data collection, feature creation, algorithms, and evaluation.

See salaries and explore the career path for Epidemiologist

Biostatistician

Biostatisticians use their knowledge of statistics and biology to design and analyze studies in the biomedical field. This course may be useful for those wishing to enter this field as it provides a solid foundation in the fundamentals of machine learning, including topics such as data collection, feature creation, algorithms, and evaluation.

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Reading list

We've selected 19 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Practical Machine Learning.

Deep Learning