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Andrew Ng,
Eddy Shyu,
Aarti Bagul,
and
Geoff Ladwig
In the first course of the Machine Learning Specialization, you will:
• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression
Read more
In the first course of the Machine Learning Specialization, you will:
• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression
Read more
In the first course of the Machine Learning Specialization, you will:
• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.
It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)
By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
Register for this course and see more details by visiting:
OpenCourser.com/course/u54csk/supervised
What's inside
Syllabus
Week 1: Introduction to Machine Learning
Welcome to the Machine Learning Specialization! You're joining millions of others who have taken either this or the original course, which led to the founding of Coursera, and has helped millions of other learners, like you, take a look at the exciting world of machine learning!
Read more
Syllabus
Week 1: Introduction to Machine Learning
Welcome to the Machine Learning Specialization! You're joining millions of others who have taken either this or the original course, which led to the founding of Coursera, and has helped millions of other learners, like you, take a look at the exciting world of machine learning!
Read more
Week 2: Regression with multiple input variables
This week, you'll extend linear regression to handle multiple input features. You'll also learn some methods for improving your model's training and performance, such as vectorization, feature scaling, feature engineering and polynomial regression. At the end of the week, you'll get to practice implementing linear regression in code.
Week 3: Classification
This week, you'll learn the other type of supervised learning, classification. You'll learn how to predict categories using the logistic regression model. You'll learn about the problem of overfitting, and how to handle this problem with a method called regularization. You'll get to practice implementing logistic regression with regularization at the end of this week!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers various advanced machine learning techniques, catering to those seeking deep expertise
Taught by Andrew Ng, an acclaimed AI visionary recognized for his groundbreaking contributions to the field
Suitable for beginners in machine learning, offering a strong foundation in core concepts
Provides practical experience through coding exercises, enabling learners to apply concepts to real-world problems
Part of a 3-course specialization, offering a comprehensive learning path in machine learning
Requires learners to have a basic understanding of Python programming and mathematics
Save this course
Save Supervised Machine Learning: Regression and Classification to your list so you can find it easily later:
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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 Supervised Machine Learning: Regression and Classification with these
activities:
Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'
Show steps
Gain a comprehensive understanding of machine learning concepts and techniques by reading this foundational book.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Read the book carefully and take notes.
-
Implement the examples and exercises in the book.
-
Discuss the concepts with other learners.
Review linear regression and logistic regression concepts
Show steps
Refresh your knowledge of linear regression and logistic regression to enhance your understanding of the concepts covered in the course.
Browse courses on
Linear Regression
Show steps
-
Review the mathematical equations and assumptions of linear regression.
-
Review the mathematical equations and assumptions of logistic regression.
-
Practice applying these concepts to solve problems.
Attend a machine learning meetup or conference
Show steps
Connect with other machine learning enthusiasts and professionals to expand your knowledge and network.
Browse courses on
Machine Learning
Show steps
-
Find a machine learning meetup or conference in your area.
-
Register for the event.
-
Attend the event and actively participate in discussions and networking opportunities.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice coding with machine learning algorithms
Show steps
Practice implementing machine learning algorithms in Python to reinforce your understanding of the concepts and techniques covered in the course.
Browse courses on
Machine Learning Algorithms
Show steps
-
Choose a machine learning algorithm to practice with.
-
Find a dataset that is suitable for the algorithm you chose.
-
Implement the algorithm in Python using NumPy and scikit-learn.
-
Test and evaluate the performance of your model.
-
Repeat steps 1-4 for different algorithms and datasets.
Explore advanced machine learning techniques in scikit-learn
Show steps
Extend your knowledge of machine learning by exploring advanced techniques and algorithms implemented in the scikit-learn library.
Browse courses on
Advanced Machine Learning
Show steps
-
Find a tutorial or documentation on the advanced technique you want to learn.
-
Follow the tutorial and implement the technique using scikit-learn.
-
Experiment with different parameters and datasets to understand the impact on performance.
Mentor a junior machine learning learner
Show steps
Reinforce your understanding of machine learning concepts by mentoring a junior learner.
Browse courses on
Mentoring
Show steps
-
Volunteer to mentor a junior learner through a mentoring program.
-
Meet with your mentee regularly to provide guidance and support.
-
Review their work and provide constructive feedback.
-
Help them develop their machine learning skills and knowledge.
Build a machine learning model for a real-world problem
Show steps
Apply the concepts and techniques learned in the course to solve a real-world problem by building and deploying a machine learning model.
Browse courses on
Machine Learning Model
Show steps
-
Identify a real-world problem that can be addressed with machine learning.
-
Gather and prepare the necessary data.
-
Choose and train a machine learning model.
-
Evaluate and refine the performance of the model.
-
Deploy the model and monitor its performance.
Contribute to an open-source machine learning project
Show steps
Gain practical experience in machine learning and contribute to the community by contributing to an open-source project.
Browse courses on
Machine Learning
Show steps
-
Find an open-source machine learning project to contribute to.
-
Identify an area where you can make a meaningful contribution.
-
Submit a pull request with your changes.
-
Work with the project maintainers to refine and merge your contribution.
-
Continue to contribute to the project as needed.
Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'
Show steps
Gain a comprehensive understanding of machine learning concepts and techniques by reading this foundational book.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Read the book carefully and take notes.
- Implement the examples and exercises in the book.
- Discuss the concepts with other learners.
Review linear regression and logistic regression concepts
Show steps
Refresh your knowledge of linear regression and logistic regression to enhance your understanding of the concepts covered in the course.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Review the mathematical equations and assumptions of linear regression.
- Review the mathematical equations and assumptions of logistic regression.
- Practice applying these concepts to solve problems.
Attend a machine learning meetup or conference
Show steps
Connect with other machine learning enthusiasts and professionals to expand your knowledge and network.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a machine learning meetup or conference in your area.
- Register for the event.
- Attend the event and actively participate in discussions and networking opportunities.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice coding with machine learning algorithms
Show steps
Practice implementing machine learning algorithms in Python to reinforce your understanding of the concepts and techniques covered in the course.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Choose a machine learning algorithm to practice with.
- Find a dataset that is suitable for the algorithm you chose.
- Implement the algorithm in Python using NumPy and scikit-learn.
- Test and evaluate the performance of your model.
- Repeat steps 1-4 for different algorithms and datasets.
Explore advanced machine learning techniques in scikit-learn
Show steps
Extend your knowledge of machine learning by exploring advanced techniques and algorithms implemented in the scikit-learn library.
Browse courses on
Advanced Machine Learning
Show steps
Show steps
Show steps
- Find a tutorial or documentation on the advanced technique you want to learn.
- Follow the tutorial and implement the technique using scikit-learn.
- Experiment with different parameters and datasets to understand the impact on performance.
Mentor a junior machine learning learner
Show steps
Reinforce your understanding of machine learning concepts by mentoring a junior learner.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Volunteer to mentor a junior learner through a mentoring program.
- Meet with your mentee regularly to provide guidance and support.
- Review their work and provide constructive feedback.
- Help them develop their machine learning skills and knowledge.
Build a machine learning model for a real-world problem
Show steps
Apply the concepts and techniques learned in the course to solve a real-world problem by building and deploying a machine learning model.
Browse courses on
Machine Learning Model
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed with machine learning.
- Gather and prepare the necessary data.
- Choose and train a machine learning model.
- Evaluate and refine the performance of the model.
- Deploy the model and monitor its performance.
Contribute to an open-source machine learning project
Show steps
Gain practical experience in machine learning and contribute to the community by contributing to an open-source project.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find an open-source machine learning project to contribute to.
- Identify an area where you can make a meaningful contribution.
- Submit a pull request with your changes.
- Work with the project maintainers to refine and merge your contribution.
- Continue to contribute to the project as needed.
Career center
Learners who complete Supervised Machine Learning: Regression and Classification will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for developing and deploying machine learning models. They use their knowledge of machine learning algorithms and statistical techniques to extract insights from data and solve complex business problems. This course can help you build a strong foundation in supervised machine learning, which is a critical skill for Data Scientists. By learning how to build and train machine learning models, you can gain the skills you need to succeed in this challenging and rewarding field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning systems. They work closely with Data Scientists to translate machine learning models into production-ready systems. This course can help you build a strong foundation in supervised machine learning, which is a key skill for Machine Learning Engineers. By learning how to build and train machine learning models, you can gain the skills you need to succeed in this exciting and growing field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for developing and implementing mathematical and statistical models to solve financial problems. They use their knowledge of financial theory and econometrics to make investment decisions. This course can help you build a strong foundation in supervised machine learning, which is a valuable skill for Quantitative Analysts. By learning how to build and train machine learning models, you can gain the skills you need to develop models that are more accurate and reliable.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for developing and implementing mathematical and statistical models to solve operational problems. They use their knowledge of optimization techniques and simulation to improve the efficiency and effectiveness of operations. This course can help you build a strong foundation in supervised machine learning, which is a valuable skill for Operations Research Analysts. By learning how to build and train machine learning models, you can gain the skills you need to develop models that are more accurate and reliable.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. They use their knowledge of statistical methods to draw conclusions about the world around us. This course can help you build a strong foundation in supervised machine learning, which is a valuable skill for Statisticians. By learning how to build and train machine learning models, you can gain the skills you need to develop models that are more accurate and reliable.
Data Engineer
Data Engineer
Data Engineers are responsible for designing and building the infrastructure that stores and processes data. They work closely with Data Scientists and Machine Learning Engineers to ensure that data is available and accessible for analysis and modeling. This course can help you build a strong foundation in supervised machine learning, which is a valuable skill for Data Engineers. By learning how to build and train machine learning models, you can gain the skills you need to develop systems that are more efficient and effective.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data and making investment recommendations. They use their knowledge of financial markets and accounting to make informed decisions about where to invest money. This course can help you build a strong foundation in supervised machine learning, which is a valuable skill for Financial Analysts. By learning how to build and train machine learning models, you can gain the skills you need to develop models that are more accurate and reliable.
Actuary
Actuary
Actuaries are responsible for assessing and managing risk. They use their knowledge of mathematics and statistics to develop models that predict the likelihood of future events. This course can help you build a strong foundation in supervised machine learning, which is a valuable skill for Actuaries. By learning how to build and train machine learning models, you can gain the skills you need to develop models that are more accurate and reliable.
Consultant
Consultant
Consultants are responsible for providing advice and guidance to organizations on a wide range of topics. They use their knowledge of business, technology, and finance to help organizations improve their performance. This course can help you build a strong foundation in supervised machine learning, which is a valuable skill for Consultants. By learning how to build and train machine learning models, you can gain the skills you need to develop solutions that are more data-driven and effective.
Data Analyst
Data Analyst
Data Analysts play a critical role in today's data-driven business world. They collect, clean, and analyze data to help businesses make informed decisions. This course can help you build a strong foundation in supervised machine learning, which is a key skill for Data Analysts. By learning how to build and train machine learning models, you can gain valuable insights from data and help businesses improve their operations.
Teacher
Teacher
Teachers are responsible for educating and inspiring students. They use their knowledge of subject matter and teaching methods to help students learn and grow. This course can help you build a strong foundation in supervised machine learning, which is a valuable skill for Teachers. By learning how to build and train machine learning models, you can gain the skills you need to create more engaging and effective lesson plans.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. They use their knowledge of programming languages and software development techniques to create software that meets the needs of users. This course can help you build a strong foundation in supervised machine learning, which is a valuable skill for Software Engineers. By learning how to build and train machine learning models, you can gain the skills you need to develop software that is more intelligent and user-friendly.
Researcher
Researcher
Researchers are responsible for conducting original research and pushing the boundaries of knowledge. The course can help you build a strong foundation in supervised machine learning, which may be useful for conducting research projects in various domains, such as natural language processing, medical imaging, and finance.
Product Manager
Product Manager
Product Managers are responsible for developing and managing products. They work closely with engineers, designers, and marketers to create products that meet the needs of users. This course can help you build a strong foundation in supervised machine learning, which is a valuable skill for Product Managers. By learning how to build and train machine learning models, you can gain the skills you need to develop products that are more intelligent and user-friendly.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business processes and identifying areas for improvement. They use their knowledge of business analysis techniques to develop solutions that improve efficiency and effectiveness. This course can help you build a strong foundation in supervised machine learning, which is a valuable skill for Business Analysts. By learning how to build and train machine learning models, you can gain the skills you need to develop solutions that are more data-driven and effective.
For more career information including salaries, visit:
OpenCourser.com/course/u54csk/supervised
Reading list
We've selected 11 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
Supervised Machine Learning: Regression and Classification .
Provides a comprehensive overview of statistical learning methods, including linear regression, logistic regression, and decision trees. It valuable reference for anyone interested in learning more about machine learning.
Practical guide to deep learning with Python. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone who wants to learn more about deep learning with Python.
Gentle introduction to statistical learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and data mining. It valuable resource for anyone who wants to learn more about statistical learning.
Practical guide to machine learning with Python. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone who wants to learn more about machine learning with Python.
Comprehensive overview of reinforcement learning. It covers a wide range of topics, including Markov decision processes, value functions, and policy gradients. It valuable resource for anyone who wants to learn more about reinforcement learning.
Practical guide to natural language processing with Python. It covers a wide range of topics, including text classification, sentiment analysis, and machine translation. It valuable resource for anyone who wants to learn more about natural language processing with Python.
Practical guide to machine learning with R. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone who wants to learn more about machine learning with R.
Practical guide to machine learning with Java. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone who wants to learn more about machine learning with Java.
Provides a theoretical foundation for machine learning. It covers a wide range of topics, including probability theory, linear algebra, and optimization. It valuable resource for anyone who wants to understand the mathematical foundations of machine learning.
Practical guide to machine learning for programmers. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone who wants to learn more about machine learning.
Save
Provides a theoretical foundation for machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to understand the mathematical foundations of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/u54csk/supervised
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Effort
At the rate of 5 hours a week, it typically takes 3 weeks to complete this course.
Level
Introductory
Via
Coursera
Institutions
Stanford University
DeepLearning.AI
Instructors
Andrew Ng
Eddy Shyu
Aarti Bagul
Geoff Ladwig
Language
English
Transcripts
Español
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Deutsch
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آذربایجان دیلی
This course belongs to a
collection
called
Machine Learning. It's comprised of three courses:
- Advanced Learning Algorithms
- Supervised Machine Learning: Regression and Classification (viewing)
- Unsupervised Learning, Recommenders, Reinforcement Learning
Good to know
Covers various advanced machine learning techniques, catering to those seeking deep expertise
Taught by Andrew Ng, an acclaimed AI visionary recognized for his groundbreaking contributions to the field
Suitable for beginners in machine learning, offering a strong foundation in core concepts
Provides practical experience through coding exercises, enabling learners to apply concepts to real-world problems
Part of a 3-course specialization, offering a comprehensive learning path in machine learning
Requires learners to have a basic understanding of Python programming and mathematics
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