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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.
Register for this course and see more details by visiting:
OpenCourser.com/course/ct4kxr/practical
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
Read more
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
Read more
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
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
Save this course
Save Practical Machine Learning to your list so you can find it easily later:
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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."
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 Practical Machine Learning with these
activities:
Recap key topics in data analysis
Show steps
Refreshes your knowledge in data analysis, providing a solid foundation for building and applying prediction functions.
Browse courses on
Exploratory Data Analysis
Show steps
-
Review techniques for exploratory data analysis, such as summary statistics and graphical representations.
-
Go over data visualization methods for effectively communicating insights.
-
Revisit data cleaning procedures for handling missing values, outliers, and other data quality issues.
Review fundamental statistics concepts
Show steps
Strengthens your foundation in statistics, ensuring a better understanding of the principles underlying prediction and machine learning.
Browse courses on
Probability
Show steps
-
Review key concepts in probability theory, such as conditional probability and Bayes' theorem.
-
Go over hypothesis testing procedures, including p-values and confidence intervals.
-
Brush up on regression analysis techniques, such as linear regression and logistic regression.
Organize and review course materials
Show steps
Ensures you have a solid foundation in the course concepts, maximizing retention and recall.
Show steps
-
Gather all lecture notes, readings, assignments, and quizzes.
-
Review and organize the materials, identifying key concepts and relationships.
-
Create summaries or flashcards to aid memorization.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow tutorials on machine learning algorithms
Show steps
Provides practical guidance on implementing and applying different machine learning algorithms, enhancing your understanding and practical skills.
Browse courses on
Machine Learning Algorithms
Show steps
-
Identify a machine learning algorithm you want to learn.
-
Find a high-quality tutorial or course on that algorithm.
-
Follow the tutorial step-by-step, implementing the algorithm in your preferred programming language.
-
Test your implementation on a dataset and evaluate its performance.
-
Experiment with different parameters and hyperparameters to optimize the algorithm's performance.
Practice building prediction functions using caret package
Show steps
Helps you develop proficiency in using the caret package for building accurate and efficient prediction functions, a crucial capability for data scientists and analysts.
Show steps
-
Import the caret package and explore its functionality.
-
Load and preprocess a dataset using caret's preprocessing functions.
-
Build a prediction model using caret's modeling functions.
-
Evaluate the performance of the prediction model using caret's evaluation functions.
Participate in peer review sessions for prediction functions
Show steps
Promotes collaboration, critical analysis, and the exchange of ideas, leading to improved understanding and refinement of prediction functions.
Browse courses on
Peer Review
Show steps
-
Join or create a peer review group with fellow learners.
-
Share your prediction function with the group and provide feedback on others' functions.
-
Provide specific and constructive feedback on the approach, implementation, and results of the prediction functions.
Develop a prediction function for a real-world problem
Show steps
Challenges you to apply the concepts learned in the course to solve a practical problem, fostering critical thinking, problem-solving, and project management skills.
Show steps
-
Define a real-world problem that you want to solve using prediction.
-
Collect data relevant to the problem.
-
Clean and preprocess the data, identifying and handling missing values and outliers.
-
Engineer features to enhance the predictive power of the model.
-
Select and train a machine learning model based on the problem and data characteristics.
-
Evaluate the performance of the model on a hold-out dataset and make necessary adjustments.
Recap key topics in data analysis
Show steps
Refreshes your knowledge in data analysis, providing a solid foundation for building and applying prediction functions.
Browse courses on
Exploratory Data Analysis
Show steps
Show steps
Show steps
- Review techniques for exploratory data analysis, such as summary statistics and graphical representations.
- Go over data visualization methods for effectively communicating insights.
- Revisit data cleaning procedures for handling missing values, outliers, and other data quality issues.
Review fundamental statistics concepts
Show steps
Strengthens your foundation in statistics, ensuring a better understanding of the principles underlying prediction and machine learning.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review key concepts in probability theory, such as conditional probability and Bayes' theorem.
- Go over hypothesis testing procedures, including p-values and confidence intervals.
- Brush up on regression analysis techniques, such as linear regression and logistic regression.
Organize and review course materials
Show steps
Ensures you have a solid foundation in the course concepts, maximizing retention and recall.
Show steps
Show steps
Show steps
- Gather all lecture notes, readings, assignments, and quizzes.
- Review and organize the materials, identifying key concepts and relationships.
- Create summaries or flashcards to aid memorization.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow tutorials on machine learning algorithms
Show steps
Provides practical guidance on implementing and applying different machine learning algorithms, enhancing your understanding and practical skills.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Identify a machine learning algorithm you want to learn.
- Find a high-quality tutorial or course on that algorithm.
- Follow the tutorial step-by-step, implementing the algorithm in your preferred programming language.
- Test your implementation on a dataset and evaluate its performance.
- Experiment with different parameters and hyperparameters to optimize the algorithm's performance.
Practice building prediction functions using caret package
Show steps
Helps you develop proficiency in using the caret package for building accurate and efficient prediction functions, a crucial capability for data scientists and analysts.
Show steps
Show steps
Show steps
- Import the caret package and explore its functionality.
- Load and preprocess a dataset using caret's preprocessing functions.
- Build a prediction model using caret's modeling functions.
- Evaluate the performance of the prediction model using caret's evaluation functions.
Participate in peer review sessions for prediction functions
Show steps
Promotes collaboration, critical analysis, and the exchange of ideas, leading to improved understanding and refinement of prediction functions.
Browse courses on
Peer Review
Show steps
Show steps
Show steps
- Join or create a peer review group with fellow learners.
- Share your prediction function with the group and provide feedback on others' functions.
- Provide specific and constructive feedback on the approach, implementation, and results of the prediction functions.
Develop a prediction function for a real-world problem
Show steps
Challenges you to apply the concepts learned in the course to solve a practical problem, fostering critical thinking, problem-solving, and project management skills.
Show steps
Show steps
Show steps
- Define a real-world problem that you want to solve using prediction.
- Collect data relevant to the problem.
- Clean and preprocess the data, identifying and handling missing values and outliers.
- Engineer features to enhance the predictive power of the model.
- Select and train a machine learning model based on the problem and data characteristics.
- Evaluate the performance of the model on a hold-out dataset and make necessary adjustments.
Career center
Learners who complete Practical Machine Learning will develop knowledge and skills
that may be useful to these careers:
Data Scientist
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.
Data Analyst
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.
Machine Learning Engineer
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.
Statistician
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.
Consultant
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.
Business Analyst
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.
Quantitative 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.
Risk Manager
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.
Operations Research Analyst
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.
Software Engineer
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.
Market Researcher
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.
Financial Analyst
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.
Actuary
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.
Epidemiologist
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.
Biostatistician
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.
For more career information including salaries, visit:
OpenCourser.com/course/ct4kxr/practical
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.
Save
Provides a comprehensive overview of deep learning, with a focus on the mathematical foundations and algorithms behind deep learning models. It valuable resource for anyone who wants to learn more about the theory and practice of deep learning.
Classic reference on statistical learning methods. It provides a deep dive into the theory and algorithms behind many of the methods covered in the course, and it valuable resource for anyone who wants to learn more about the foundations of machine learning.
Provides a comprehensive overview of pattern recognition and machine learning, with a focus on the mathematical foundations and algorithms behind pattern recognition and machine learning models. It valuable resource for anyone who wants to learn more about the theory and practice of pattern recognition and machine learning.
Provides a comprehensive overview of reinforcement learning, with a focus on the mathematical foundations and algorithms behind reinforcement learning models. It valuable resource for anyone who wants to learn more about the theory and practice of reinforcement learning.
Provides a comprehensive overview of causal inference, with a focus on the mathematical foundations and algorithms behind causal inference models. It valuable resource for anyone who wants to learn more about the theory and practice of causal inference.
Provides a comprehensive overview of statistical learning methods, with a focus on practical applications in R. It covers a wide range of topics, including supervised and unsupervised learning, model selection, and resampling methods.
Provides a more theoretical introduction to machine learning, with a focus on Bayesian methods and optimization. It valuable resource for anyone who wants to learn more about the mathematical foundations of machine learning.
Provides a comprehensive overview of computer vision, with a focus on the mathematical foundations and algorithms behind computer vision models. It valuable resource for anyone who wants to learn more about the theory and practice of computer vision.
Provides a comprehensive overview of Bayesian data analysis, with a focus on practical applications. It covers a wide range of topics, including Bayesian inference, model selection, and resampling methods.
Provides a comprehensive overview of Bayesian statistical modeling, with a focus on practical applications in R and Stan. It covers a wide range of topics, including Bayesian inference, model selection, and resampling methods.
Provides a comprehensive overview of econometric analysis of cross section and panel data, with a focus on practical applications. It covers a wide range of topics, including linear regression, logistic regression, and panel data models.
Provides a comprehensive overview of natural language processing, with a focus on practical applications in Python. It covers a wide range of topics, including text preprocessing, feature engineering, model selection, and evaluation.
Provides a practical guide to interpretable machine learning methods. It covers a wide range of topics, including model interpretability, feature importance, and model explainability.
Provides a practical introduction to machine learning for non-technical readers. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation.
Save
This practical guide focuses on building predictive models for real-world problems, covering topics such as data preparation, model selection, and model evaluation. It useful resource for those who want to apply machine learning techniques to solve specific business problems.
Covers a range of practical machine learning techniques, from data preprocessing and feature engineering to model training and evaluation. It also includes a focus on the caret package, which is used in the course.
This practical guide focuses on using R for machine learning tasks, covering topics such as data exploration, model building, and model evaluation. It useful resource for those who want to apply machine learning techniques using R.
This hands-on guide focuses on using Python for machine learning tasks, covering topics such as data preprocessing, model building, and model evaluation. It useful resource for those who want to apply machine learning techniques using Python.
This practical guide focuses on using Python for machine learning tasks, covering topics such as data preprocessing, model building, and model evaluation. It useful resource for those who want to apply machine learning techniques using Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ct4kxr/practical
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Via
Coursera
Institution
Johns Hopkins University
Instructors
Roger D. Peng, PhD
Jeff Leek, PhD
Brian Caffo, PhD
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
Data Science. It's comprised of 11 courses:
Good to know
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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