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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
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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
Save this course
Save Supervised Learning to your list so you can find it easily later:
Save
Save
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 Learning with these
activities:
Review Basic Descriptive Statistics
Show steps
Review the basics of descriptive statistics to strengthen your foundation for machine learning.
Browse courses on
Descriptive Statistics
Show steps
-
Read a textbook chapter or online article on descriptive statistics.
-
Complete practice problems on measures of central tendency and dispersion.
Review Linear Algebra concepts
Show steps
Review the fundamentals of linear algebra to strengthen your understanding of the mathematical concepts underlying machine learning algorithms.
Browse courses on
Vector Spaces
Show steps
-
Review notes or textbooks on vector spaces, matrices, and linear transformations.
-
Practice solving problems involving matrix operations and vector manipulation.
-
Consider taking a refresher course or online tutorial on linear algebra.
Study Group Discussions
Show steps
Engaging in regular study group discussions will reinforce your understanding of supervised learning concepts and techniques.
Browse courses on
Supervised Learning
Show steps
-
Form a study group with fellow students.
-
Meet regularly to discuss course materials, share insights, and work on practice problems together.
-
Prepare questions and topics for discussion in advance.
16 other activities
Expand to see all activities and additional details
Show all 19 activities
Review Linear Regression Models
Show steps
Understanding the conceptual basis of linear regression models will enhance your comprehension of supervised learning classification techniques.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
-
Read Chapters 2 and 3 of 'Elements of Statistical Learning'.
-
Complete the corresponding practice problems.
-
Summarize the key concepts of linear regression in your own words.
Create a Mind Map of Supervised Machine Learning Concepts
Show steps
Create a visual representation of supervised machine learning concepts to improve your understanding and recall.
Show steps
-
Gather key concepts and terms from the course materials.
-
Organize the concepts into a hierarchical structure using a mind mapping tool or software.
-
Add examples, explanations, and connections between concepts.
Supervised Learning Metrics Workshop
Show steps
Participating in a supervised learning metrics workshop will enhance your ability to evaluate the performance of machine learning models.
Browse courses on
Data Analysis
Show steps
-
Attend a supervised learning metrics workshop.
-
Actively participate in discussions and exercises.
-
Apply what you learn to your own machine learning projects.
Follow a Tutorial on Naive Bayes Algorithm
Show steps
Enhance your understanding of Naive Bayes, a fundamental classification algorithm.
Browse courses on
Naive Bayes
Show steps
-
Find a reputable online tutorial on Naive Bayes.
-
Step through the tutorial, taking notes and practicing the concepts.
Perceptron Algorithm Tutorial
Show steps
Hands-on exploration of the perceptron algorithm will solidify your understanding of its role in pattern recognition and machine learning.
Show steps
-
Follow the guided tutorial on perceptron algorithm implementation.
-
Implement the algorithm in your preferred programming language.
-
Test your implementation on a simple dataset.
Join a Peer Study Group and Assist Other Learners
Show steps
Reinforce your understanding by sharing your knowledge and assisting fellow learners.
Show steps
-
Join or create a peer study group for the course.
-
Actively participate in discussions and provide support to other members.
Solve Practice Problems on Support Vector Machines
Show steps
Strengthen your skills in solving problems involving support vector machines.
Browse courses on
Support Vector Machines
Show steps
-
Find a collection of practice problems on support vector machines.
-
Solve the problems, checking your answers against provided solutions.
-
Seek clarification on any problems you have difficulty with.
Decision Tree Exercises
Show steps
Regular practice with decision tree exercises will enhance your ability to apply them effectively in supervised learning tasks.
Browse courses on
Decision Trees
Show steps
-
Solve the decision tree exercises provided in the course materials.
-
Create your own decision tree for a given dataset.
-
Evaluate the performance of your decision tree using cross-validation.
Apply supervised learning algorithms to real-world datasets
Show steps
Gain hands-on experience implementing and evaluating supervised learning models on practical datasets, reinforcing your understanding of their strengths and weaknesses.
Show steps
-
Find publicly available datasets related to your interests, such as Kaggle or UCI Machine Learning Repository.
-
Choose appropriate supervised learning algorithms based on the dataset and task.
-
Implement the algorithms using a programming language like Python or R.
-
Evaluate the performance of your models using metrics such as accuracy, precision, and recall.
-
Experiment with different hyperparameters and model architectures to optimize performance.
Attend a Workshop on Bagging and Boosting Techniques
Show steps
Deepen your knowledge of bagging and boosting through a hands-on workshop.
Browse courses on
Bagging
Show steps
-
Research and identify a reputable workshop on bagging and boosting.
-
Register and attend the workshop, actively participating in exercises and discussions.
Naive Bayes Classifier Project
Show steps
Embarking on a Naive Bayes classifier project will allow you to apply your knowledge to a practical problem, deepening your understanding of its applications.
Show steps
-
Choose a text classification dataset.
-
Preprocess the data, including tokenization and feature extraction.
-
Train and evaluate a Naive Bayes classifier on the dataset.
-
Analyze the results and identify areas for improvement.
Develop a tutorial or guide on a specific supervised learning topic
Show steps
Enhance your comprehension by teaching a particular supervised learning topic to others, reinforcing your understanding and fostering effective communication.
Show steps
-
Choose a supervised learning topic that you are proficient in.
-
Create an outline for your tutorial or guide.
-
Write clear and concise content, explaining the concepts and providing examples.
-
Consider creating visual aids, such as diagrams or code snippets.
-
Share your tutorial or guide online or present it to peers.
Build a Machine Learning Model to Predict Customer Churn
Show steps
Apply your supervised machine learning skills to a real-world problem by building a model to predict customer churn.
Show steps
-
Gather and prepare a dataset of customer behavior data.
-
Select and train a suitable machine learning model using supervised learning techniques.
-
Evaluate the performance of the model using appropriate metrics.
Support Vector Machine Presentation
Show steps
Creating a presentation on support vector machines will help you synthesize your knowledge and effectively communicate its applications.
Browse courses on
Support Vector Machines
Show steps
-
Research and gather information on support vector machines.
-
Develop a clear and concise presentation structure.
-
Design visually appealing and informative slides.
-
Practice your presentation and obtain feedback.
Build a machine learning model for a specific business or research problem
Show steps
Deepen your understanding by applying supervised learning techniques to solve a real-world problem, fostering critical thinking and practical application skills.
Show steps
-
Identify a specific problem that can be addressed using supervised learning.
-
Gather and prepare the necessary data.
-
Choose and implement appropriate supervised learning algorithms.
-
Evaluate the performance of your model and make necessary adjustments.
-
Create a report or presentation showcasing your findings and insights.
Ensemble Methods Summary
Show steps
Creating a summary of ensemble methods will consolidate your understanding of their advantages and applications in supervised learning.
Browse courses on
Ensemble Methods
Show steps
-
Review the different ensemble methods covered in the course.
-
Identify the key advantages and disadvantages of each method.
-
Provide examples of real-world applications where ensemble methods have been successfully used.
Review Basic Descriptive Statistics
Show steps
Review the basics of descriptive statistics to strengthen your foundation for machine learning.
Browse courses on
Descriptive Statistics
Show steps
Show steps
Show steps
- Read a textbook chapter or online article on descriptive statistics.
- Complete practice problems on measures of central tendency and dispersion.
Review Linear Algebra concepts
Show steps
Review the fundamentals of linear algebra to strengthen your understanding of the mathematical concepts underlying machine learning algorithms.
Browse courses on
Vector Spaces
Show steps
Show steps
Show steps
- Review notes or textbooks on vector spaces, matrices, and linear transformations.
- Practice solving problems involving matrix operations and vector manipulation.
- Consider taking a refresher course or online tutorial on linear algebra.
Study Group Discussions
Show steps
Engaging in regular study group discussions will reinforce your understanding of supervised learning concepts and techniques.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Form a study group with fellow students.
- Meet regularly to discuss course materials, share insights, and work on practice problems together.
- Prepare questions and topics for discussion in advance.
16 other activities
Expand to see all activities and additional details
Show all 19 activities
Review Linear Regression Models
Show steps
Understanding the conceptual basis of linear regression models will enhance your comprehension of supervised learning classification techniques.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 2 and 3 of 'Elements of Statistical Learning'.
- Complete the corresponding practice problems.
- Summarize the key concepts of linear regression in your own words.
Create a Mind Map of Supervised Machine Learning Concepts
Show steps
Create a visual representation of supervised machine learning concepts to improve your understanding and recall.
Show steps
Show steps
Show steps
- Gather key concepts and terms from the course materials.
- Organize the concepts into a hierarchical structure using a mind mapping tool or software.
- Add examples, explanations, and connections between concepts.
Supervised Learning Metrics Workshop
Show steps
Participating in a supervised learning metrics workshop will enhance your ability to evaluate the performance of machine learning models.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Attend a supervised learning metrics workshop.
- Actively participate in discussions and exercises.
- Apply what you learn to your own machine learning projects.
Follow a Tutorial on Naive Bayes Algorithm
Show steps
Enhance your understanding of Naive Bayes, a fundamental classification algorithm.
Browse courses on
Naive Bayes
Show steps
Show steps
Show steps
- Find a reputable online tutorial on Naive Bayes.
- Step through the tutorial, taking notes and practicing the concepts.
Perceptron Algorithm Tutorial
Show steps
Hands-on exploration of the perceptron algorithm will solidify your understanding of its role in pattern recognition and machine learning.
Show steps
Show steps
Show steps
- Follow the guided tutorial on perceptron algorithm implementation.
- Implement the algorithm in your preferred programming language.
- Test your implementation on a simple dataset.
Join a Peer Study Group and Assist Other Learners
Show steps
Reinforce your understanding by sharing your knowledge and assisting fellow learners.
Show steps
Show steps
Show steps
- Join or create a peer study group for the course.
- Actively participate in discussions and provide support to other members.
Solve Practice Problems on Support Vector Machines
Show steps
Strengthen your skills in solving problems involving support vector machines.
Browse courses on
Support Vector Machines
Show steps
Show steps
Show steps
- Find a collection of practice problems on support vector machines.
- Solve the problems, checking your answers against provided solutions.
- Seek clarification on any problems you have difficulty with.
Decision Tree Exercises
Show steps
Regular practice with decision tree exercises will enhance your ability to apply them effectively in supervised learning tasks.
Browse courses on
Decision Trees
Show steps
Show steps
Show steps
- Solve the decision tree exercises provided in the course materials.
- Create your own decision tree for a given dataset.
- Evaluate the performance of your decision tree using cross-validation.
Apply supervised learning algorithms to real-world datasets
Show steps
Gain hands-on experience implementing and evaluating supervised learning models on practical datasets, reinforcing your understanding of their strengths and weaknesses.
Show steps
Show steps
Show steps
- Find publicly available datasets related to your interests, such as Kaggle or UCI Machine Learning Repository.
- Choose appropriate supervised learning algorithms based on the dataset and task.
- Implement the algorithms using a programming language like Python or R.
- Evaluate the performance of your models using metrics such as accuracy, precision, and recall.
- Experiment with different hyperparameters and model architectures to optimize performance.
Attend a Workshop on Bagging and Boosting Techniques
Show steps
Deepen your knowledge of bagging and boosting through a hands-on workshop.
Browse courses on
Bagging
Show steps
Show steps
Show steps
- Research and identify a reputable workshop on bagging and boosting.
- Register and attend the workshop, actively participating in exercises and discussions.
Naive Bayes Classifier Project
Show steps
Embarking on a Naive Bayes classifier project will allow you to apply your knowledge to a practical problem, deepening your understanding of its applications.
Show steps
Show steps
Show steps
- Choose a text classification dataset.
- Preprocess the data, including tokenization and feature extraction.
- Train and evaluate a Naive Bayes classifier on the dataset.
- Analyze the results and identify areas for improvement.
Develop a tutorial or guide on a specific supervised learning topic
Show steps
Enhance your comprehension by teaching a particular supervised learning topic to others, reinforcing your understanding and fostering effective communication.
Show steps
Show steps
Show steps
- Choose a supervised learning topic that you are proficient in.
- Create an outline for your tutorial or guide.
- Write clear and concise content, explaining the concepts and providing examples.
- Consider creating visual aids, such as diagrams or code snippets.
- Share your tutorial or guide online or present it to peers.
Build a Machine Learning Model to Predict Customer Churn
Show steps
Apply your supervised machine learning skills to a real-world problem by building a model to predict customer churn.
Show steps
Show steps
Show steps
- Gather and prepare a dataset of customer behavior data.
- Select and train a suitable machine learning model using supervised learning techniques.
- Evaluate the performance of the model using appropriate metrics.
Support Vector Machine Presentation
Show steps
Creating a presentation on support vector machines will help you synthesize your knowledge and effectively communicate its applications.
Browse courses on
Support Vector Machines
Show steps
Show steps
Show steps
- Research and gather information on support vector machines.
- Develop a clear and concise presentation structure.
- Design visually appealing and informative slides.
- Practice your presentation and obtain feedback.
Build a machine learning model for a specific business or research problem
Show steps
Deepen your understanding by applying supervised learning techniques to solve a real-world problem, fostering critical thinking and practical application skills.
Show steps
Show steps
Show steps
- Identify a specific problem that can be addressed using supervised learning.
- Gather and prepare the necessary data.
- Choose and implement appropriate supervised learning algorithms.
- Evaluate the performance of your model and make necessary adjustments.
- Create a report or presentation showcasing your findings and insights.
Ensemble Methods Summary
Show steps
Creating a summary of ensemble methods will consolidate your understanding of their advantages and applications in supervised learning.
Browse courses on
Ensemble Methods
Show steps
Show steps
Show steps
- Review the different ensemble methods covered in the course.
- Identify the key advantages and disadvantages of each method.
- Provide examples of real-world applications where ensemble methods have been successfully used.
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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