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Supervised Learning
Josh Bernhard
and
Luis Serrano
Josh Bernhard
and
Luis Serrano
Take Udacity's Supervised Machine Learning course and improve your understanding of supervised machine learning methods including regression and classification techniques.
Prerequisite details
Read more
Take Udacity's Supervised Machine Learning course and improve your understanding of supervised machine learning methods including regression and classification techniques.
Prerequisite details
Read more
Take Udacity's Supervised Machine Learning course and improve your understanding of supervised machine learning methods including regression and classification techniques.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Basic descriptive statistics
- Basic probability
- Intermediate Python
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/lameje/supervised
What's inside
Syllabus
Before diving into the many algorithms of machine learning, it is important to take a step back and understand the big picture associated with the entire field.
Read more
Syllabus
Before diving into the many algorithms of machine learning, it is important to take a step back and understand the big picture associated with the entire field.
Read more
Linear regression is one of the most fundamental algorithms in machine learning. In this lesson, learn how linear regression works!
The perceptron algorithm is an algorithm for classifying data. It is the building block of neural networks.
Decision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.
Naive Bayesian Algorithms are powerful tools for creating classifiers for incoming labeled data. Specifically Naive Bayes is frequently used with text data and classification problems.
Support vector machines are a common method used for classification problems. They have been proven effective using what is known as the 'kernel' trick!
Bagging and boosting are two common ensemble methods for combining simple algorithms to make more advanced models that work better than the simple algorithms would on their own.
Learn the main metrics to evaluate models, such as accuracy, precision, recall, and more!
Learn the main types of errors that can occur during training, and several methods to deal with them and optimize your machine learning models.
You've covered a wide variety of methods for performing supervised learning -- now it's time to put those into action!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines topics such as regression, classification, and model evaluation, which are fundamentals in machine learning
Led by renowned instructors Josh Bernhard and Luis Serrano, who bring extensive experience and expertise in machine learning
Develops foundational skills in machine learning, including linear regression, decision trees, and support vector machines, which are valuable for various applications
Emphasizes practical implementation through interactive labs and exercises, which enhance learning and retention
Requires learners to have basic knowledge in statistics, probability, and Python, which may limit accessibility for beginners
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Activities
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Supervised Learning.
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Career center
Learners who complete Supervised Learning will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. This course can help you develop the skills you need to become a Machine Learning Engineer by providing you with a strong foundation in supervised machine learning methods. You will learn how to use these methods to design, develop, and deploy machine learning models.
Data Scientist
Data Scientist
Data Scientists use statistical techniques to analyze data and build models that can be used to make predictions and decisions. This course can help you develop the skills you need to become a Data Scientist by providing you with a strong foundation in supervised machine learning methods. You will learn how to use these methods to analyze data, build models, and make predictions.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. This course can help you develop the skills you need to become a Data Engineer by providing you with a strong foundation in supervised machine learning methods. You will learn how to use these methods to design, build, and maintain data pipelines.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems. This course can help you develop the skills you need to become a Data Analyst by providing you with a strong foundation in supervised machine learning methods. You will learn how to use these methods to analyze data and solve business problems.
Business Analyst
Business Analyst
Business Analysts use data to improve business processes. This course can help you develop the skills you need to become a Business Analyst by providing you with a strong foundation in supervised machine learning methods. You will learn how to use these methods to analyze data and improve business processes.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical methods to analyze financial data. This course can help you develop the skills you need to become a Quantitative Analyst by providing you with a strong foundation in supervised machine learning methods. You will learn how to use these methods to analyze financial data and make investment decisions.
Statistician
Statistician
Statisticians use statistical methods to collect, analyze, and interpret data. This course can help you develop the skills you need to become a Statistician by providing you with a strong foundation in supervised machine learning methods. You will learn how to use these methods to collect, analyze, and interpret data.
Computer Scientist
Computer Scientist
Computer Scientists research and develop new computer technologies. While this course does not cover computer science, it can help you develop the analytical skills you need to be successful in this field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. While this course does not cover software development, it can help you develop the analytical skills you need to be successful in this field.
Consultant
Consultant
Consultants help organizations solve problems and improve performance. This course can help you develop the analytical skills you need to be successful in this field.
Product Manager
Product Manager
Product Managers develop and launch new products. This course can help you develop the analytical skills you need to be successful in this field.
Marketer
Marketer
Marketers develop and execute marketing campaigns. This course can help you develop the analytical skills you need to be successful in this field.
Salesperson
Salesperson
Salespeople sell products and services. While this course does not cover sales techniques, it can help you develop the analytical skills you need to be successful in this field.
Customer Service Representative
Customer Service Representative
Customer Service Representatives provide support to customers. While this course does not cover customer service, it can help you develop the communication skills you need to be successful in this field.
Teacher
Teacher
Teachers educate students. While this course does not cover teaching methods, it can help you develop the communication skills you need to be successful in this field.
For more career information including salaries, visit:
OpenCourser.com/course/lameje/supervised
Reading list
We've selected 14 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 Learning.
A comprehensive textbook covering various supervised learning methods, providing a deeper understanding of their mathematical foundations and statistical properties.
A classic work that provides a broad introduction to supervised learning, including linear and nonlinear models, decision trees, and boosting.
Provides a comprehensive treatment of supervised learning methods, including Bayesian approaches.
A hands-on guide that covers supervised learning methods using Python, with a focus on practical implementation.
Save
Provides an overview of deep learning techniques, including their application in supervised learning problems.
A practical guide to implementing supervised learning algorithms using popular Python libraries.
Save
A comprehensive textbook covering various supervised learning methods, including their theoretical foundations and practical applications.
Save
A practical guide to data mining techniques, including supervised learning algorithms.
Save
A comprehensive textbook covering various aspects of machine learning, including supervised learning methods.
A practical guide to implementing supervised learning algorithms for solving real-world problems.
Provides a concise overview of supervised learning algorithms, making it a useful reference for practitioners.
A practical guide to implementing supervised learning algorithms using Python, with a focus on model selection and evaluation.
Provides a thorough treatment of linear regression models, which are commonly used in supervised learning for prediction.
Provides a comprehensive overview of Bayesian data analysis, which can be useful for understanding supervised learning methods from a probabilistic perspective.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/lameje/supervised
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Effort
3 weeks
Via
Udacity
Instructors
Josh Bernhard
Luis Serrano
Language
English
Good to know
Examines topics such as regression, classification, and model evaluation, which are fundamentals in machine learning
Led by renowned instructors Josh Bernhard and Luis Serrano, who bring extensive experience and expertise in machine learning
Develops foundational skills in machine learning, including linear regression, decision trees, and support vector machines, which are valuable for various applications
Emphasizes practical implementation through interactive labs and exercises, which enhance learning and retention
Requires learners to have basic knowledge in statistics, probability, and Python, which may limit accessibility for beginners
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