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Rafael Irizarry
Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.
Read more
Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.
Read more
Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.
In this course,part ofourProfessional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.
You will learn about training data, and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.
Register for this course and see more details by visiting:
OpenCourser.com/course/qkoj7x/data
What's inside
Learning objectives
- The basics of machine learning
- How to perform cross-validation to avoid overtraining
- Several popular machine learning algorithms
- How to build a recommendation system
- What is regularization and why it is useful?
- The basics of machine learning
- How to perform cross-validation to avoid overtraining
- Several popular machine learning algorithms
- How to build a recommendation system
- What is regularization and why it is useful?
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides strong foundation for learners to gain knowledge in Machine Learning
Helps learners develop essential skills and algorithms in Machine Learning
Guides learners through building a movie recommendation system, providing practical experience
Covers popular Machine Learning algorithms and techniques, presenting industry-standard knowledge
Save this course
Save Data Science: Machine Learning to your list so you can find it easily later:
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Reviews summary
Well-regarded machine learning course
Learners say this Data Science: Machine Learning course shines in teaching Python fundamentals. Despite minor inconveniences, the course is well received and part of a comprehensive Data Science series.
Course provides strong Python fundamentals.
"I love the teaching on python."
"it is well taking me through python"
"congratulations...for such a wonderful support that you are giving me"
Course could benefit from fewer bugs and typos.
"some obvious messiness in the Course: assignments on some topics whereas they were not mentioned in the Course content (PCA, Clustering)"
"some bugs in the assignments"
"some important algos not included in the Course content: SVM, Boosting"
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 Data Science: Machine Learning with these
activities:
Review Linear Algebra Concepts
Show steps
Refresh understanding of linear algebra concepts to strengthen foundation for machine learning algorithms
Browse courses on
Linear Algebra
Show steps
-
Review matrix operations
-
Practice solving systems of linear equations
-
Review concepts of vector spaces and subspaces
Follow YouTube Tutorials on Machine Learning
Show steps
Reinforce understanding by watching YouTube tutorials that provide clear explanations and hands-on examples
Show steps
-
Search for relevant YouTube tutorials
-
Watch videos and take notes
-
Follow along with the code examples
Join Machine Learning Study Group
Show steps
Engage with peers to discuss concepts, share knowledge, and work through problems together
Show steps
-
Find or create a study group
-
Set regular meeting times
-
Prepare for each meeting by reviewing materials
-
Discuss concepts and work through problems
Four other activities
Expand to see all activities and additional details
Show all seven activities
Build Simple Regression Model
Show steps
Build a simple regression model on a well known dataset to get a basic understanding of model training
Show steps
-
Import libraries and load dataset
-
Split dataset into train and test sets
-
Train a linear regression model
-
Evaluate model performance
Create a Cheat Sheet on Machine Learning Algorithms
Show steps
Enhance memorization and understanding by creating a concise cheat sheet summarizing key concepts
Browse courses on
Machine Learning Algorithms
Show steps
-
Gather information from course materials
-
Summarize key algorithms and their uses
-
Organize the information into a clear and concise format
Implement KNN and Decision Tree
Show steps
Implement KNN and decision tree algorithms from scratch to gain a deeper understanding of their inner workings
Show steps
-
Define a distance metric for KNN
-
Implement a function to find k nearest neighbors
-
Train a decision tree using ID3 or C4.5 algorithm
-
Evaluate the performance of KNN and decision tree
Build a Recommendation System for a Movie Website
Show steps
Apply knowledge to a practical project that reinforces concepts and skills
Show steps
-
Gather movie data and user ratings
-
Choose and implement a recommendation algorithm
-
Evaluate the performance of the recommendation system
-
Deploy the recommendation system on a website
Review Linear Algebra Concepts
Show steps
Refresh understanding of linear algebra concepts to strengthen foundation for machine learning algorithms
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review matrix operations
- Practice solving systems of linear equations
- Review concepts of vector spaces and subspaces
Follow YouTube Tutorials on Machine Learning
Show steps
Reinforce understanding by watching YouTube tutorials that provide clear explanations and hands-on examples
Show steps
Show steps
Show steps
- Search for relevant YouTube tutorials
- Watch videos and take notes
- Follow along with the code examples
Join Machine Learning Study Group
Show steps
Engage with peers to discuss concepts, share knowledge, and work through problems together
Show steps
Show steps
Show steps
- Find or create a study group
- Set regular meeting times
- Prepare for each meeting by reviewing materials
- Discuss concepts and work through problems
Four other activities
Expand to see all activities and additional details
Show all seven activities
Build Simple Regression Model
Show steps
Build a simple regression model on a well known dataset to get a basic understanding of model training
Show steps
Show steps
Show steps
- Import libraries and load dataset
- Split dataset into train and test sets
- Train a linear regression model
- Evaluate model performance
Create a Cheat Sheet on Machine Learning Algorithms
Show steps
Enhance memorization and understanding by creating a concise cheat sheet summarizing key concepts
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Gather information from course materials
- Summarize key algorithms and their uses
- Organize the information into a clear and concise format
Implement KNN and Decision Tree
Show steps
Implement KNN and decision tree algorithms from scratch to gain a deeper understanding of their inner workings
Show steps
Show steps
Show steps
- Define a distance metric for KNN
- Implement a function to find k nearest neighbors
- Train a decision tree using ID3 or C4.5 algorithm
- Evaluate the performance of KNN and decision tree
Build a Recommendation System for a Movie Website
Show steps
Apply knowledge to a practical project that reinforces concepts and skills
Show steps
Show steps
Show steps
- Gather movie data and user ratings
- Choose and implement a recommendation algorithm
- Evaluate the performance of the recommendation system
- Deploy the recommendation system on a website
Career center
Learners who complete Data Science: Machine Learning will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers use their expertise in machine learning to design and build machine learning systems. This course provides a strong foundation in machine learning, including algorithms, techniques, and best practices. This can help machine learning engineers to develop more effective and efficient machine learning systems.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial intelligence engineers use machine learning to develop and implement AI systems. This course provides a comprehensive introduction to machine learning, including algorithms, techniques, and best practices. This can help artificial intelligence engineers to develop more effective and efficient AI systems.
Data Scientist
Data Scientist
Data scientists use machine learning to develop predictive models and solve business problems. This course provides a comprehensive introduction to machine learning, including algorithms, techniques, and best practices. This can help data scientists to develop more effective data-driven solutions for their organizations.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural language processing engineers use machine learning to develop and implement natural language processing systems. This course provides a strong foundation in machine learning, including algorithms, techniques, and best practices. This can help natural language processing engineers to develop more effective and accurate natural language processing systems.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use machine learning to develop and implement trading strategies. This course provides a strong foundation in machine learning, including algorithms, techniques, and best practices. This can help quantitative analysts to develop more effective trading strategies.
Robotics Engineer
Robotics Engineer
Robotics engineers use machine learning to develop and implement robots. This course provides a strong foundation in machine learning, including algorithms, techniques, and best practices. This can help robotics engineers to develop more effective and efficient robots.
Computer Vision Engineer
Computer Vision Engineer
Computer vision engineers use machine learning to develop and implement computer vision systems. This course provides a strong foundation in machine learning, including algorithms, techniques, and best practices. This can help computer vision engineers to develop more effective and accurate computer vision systems.
Statistician
Statistician
Statisticians use machine learning to analyze data and draw conclusions. This course provides a strong foundation in machine learning, including algorithms, techniques, and best practices. This can help statisticians to develop more effective and accurate statistical models.
Research Scientist
Research Scientist
Research scientists use machine learning to develop new scientific theories and models. This course provides a comprehensive introduction to machine learning, including algorithms, techniques, and best practices. This can help research scientists to develop more effective and accurate scientific models.
Data Analyst
Data Analyst
Machine learning is a core skill for data analysts, who use it to build models and analyze data. This course covers the basics of machine learning, including algorithms, techniques, and best practices. This can help data analysts to develop more effective data-driven solutions for their organizations.
Product Manager
Product Manager
Product managers use machine learning to develop and improve products. This course provides a foundational understanding of machine learning, including algorithms, techniques, and best practices. This can help product managers to make better decisions about how to use machine learning in their products.
Data Engineer
Data Engineer
Data engineers use machine learning to build and maintain data pipelines and systems. This course provides a foundation in machine learning, including algorithms, techniques, and best practices. This can help data engineers to develop more efficient and effective data pipelines.
Software Engineer
Software Engineer
Software engineers use machine learning to develop and improve software applications. This course provides a foundation in machine learning, including algorithms, techniques, and best practices. This can help software engineers to develop more effective and efficient software applications.
Business Analyst
Business Analyst
Machine learning is a key technology for business analysts, who use it to analyze data and identify trends. This course provides a foundation in machine learning, including algorithms, techniques, and best practices. This can help business analysts to develop more effective data-driven solutions for their clients.
Web Developer
Web Developer
Web developers use machine learning to develop and improve web applications. This course provides a foundation in machine learning, including algorithms, techniques, and best practices. This can help web developers to develop more effective and engaging web applications.
For more career information including salaries, visit:
OpenCourser.com/course/qkoj7x/data
Reading list
We've selected 15 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
Data Science: Machine Learning.
This advanced textbook provides a comprehensive introduction to Bayesian statistics, which powerful tool for modeling complex data.
Save
This comprehensive deep learning textbook covers a wide range of deep learning architectures and applications.
This textbook covers a wide range of machine learning algorithms and provides a solid theoretical foundation.
This textbook provides a comprehensive introduction to machine learning, with a focus on the latest advances in the field.
This widely-used textbook provides a comprehensive introduction to machine learning algorithms and techniques.
This popular textbook provides a practical guide to building machine learning models using the Scikit-Learn, Keras, and TensorFlow libraries in Python.
This textbook provides a detailed introduction to the field of data-driven science and engineering, which combines machine learning with scientific modeling.
This textbook provides a probabilistic introduction to machine learning.
This advanced textbook covers regularized regression methods, which are essential for dealing with high-dimensional or sparse datasets.
This comprehensive handbook covers all aspects of recommendation systems, including algorithms, evaluation methods, and case studies.
This textbook provides a comprehensive introduction to reinforcement learning, which powerful technique for learning from experience.
This classic machine learning textbook introduces core concepts and algorithms, and contains practice exercises and review questions.
Teaches the fundamentals of machine learning using a practical approach, focusing on building real-world applications.
Provides a comprehensive introduction to data science and machine learning in Python.
This practical guide walks readers through how to implement machine learning algorithms using the Python programming language.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/qkoj7x/data
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Effort
2 to 4 hours per week for 8 weeks
Level
Introductory
Via
edX
Institution
Harvard University
Instructor
Rafael Irizarry
Sessions
October 16, 2024
- June 18, 2025
April 17, 2024
- December 18, 2024
Language
English
Transcripts
English
Español
Français
Português
Deutsch
中文
العربية
हिंदी
Türkçe
తెలుగు
Bahasa Indonesia
Kiswahili
This course belongs to a
collection
called
Data Science. It's comprised of nine courses:
Good to know
Provides strong foundation for learners to gain knowledge in Machine Learning
Helps learners develop essential skills and algorithms in Machine Learning
Guides learners through building a movie recommendation system, providing practical experience
Covers popular Machine Learning algorithms and techniques, presenting industry-standard knowledge
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