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Janani Ravi
This course covers the important types of machine learning algorithms, solution techniques based on the specifics of the problem you are trying to solve, as well as the classic machine learning workflow.
As Machine Learning explodes in popularity, it is becoming ever more important to know precisely how to frame a machine learning model in a manner appropriate to the problem we are trying to solve, and the data that we have available.
In this course, Creating Machine Learning Models you will gain the ability to choose the right type of model for your problem, then build that model, and evaluate its performance.
Read more
This course covers the important types of machine learning algorithms, solution techniques based on the specifics of the problem you are trying to solve, as well as the classic machine learning workflow.
As Machine Learning explodes in popularity, it is becoming ever more important to know precisely how to frame a machine learning model in a manner appropriate to the problem we are trying to solve, and the data that we have available.
In this course, Creating Machine Learning Models you will gain the ability to choose the right type of model for your problem, then build that model, and evaluate its performance.
Read more
This course covers the important types of machine learning algorithms, solution techniques based on the specifics of the problem you are trying to solve, as well as the classic machine learning workflow.
As Machine Learning explodes in popularity, it is becoming ever more important to know precisely how to frame a machine learning model in a manner appropriate to the problem we are trying to solve, and the data that we have available.
In this course, Creating Machine Learning Models you will gain the ability to choose the right type of model for your problem, then build that model, and evaluate its performance.
First, you will learn how rule-based and ML-based systems differ and their strengths and weaknesses and how supervised and unsupervised learning models differ from each other.
Next, you will discover how to implement a range of techniques to solve the supervised learning problems of classification and regression. You will gain an intuitive understanding of the the model algorithms you can use for classification and regression. Finally, you will round out your knowledge by building clustering models using a couple of different algorithms, and validating the results.
When you’re finished with this course, you will have the skills and knowledge to identify the correct machine learning problem setup, and the appropriate solution and evaluation techniques for your use-case.
Register for this course and see more details by visiting:
OpenCourser.com/course/ij0e2d/creating
What's inside
Syllabus
Course Overview
Understanding Approaches to Machine Learning
Understanding and Implementing Regression Models
Understanding and Implementing Classification Models
Read more
Syllabus
Course Overview
Understanding Approaches to Machine Learning
Understanding and Implementing Regression Models
Understanding and Implementing Classification Models
Read more
Understanding and Implementing Clustering Model
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Helps students understand how to choose the best model for their needs
Covers a range of techniques for solving supervised learning problems
Provides an intuitive understanding of classification and regression model algorithms
Teaches how to build clustering models using different algorithms
Helps students validate the results of their machine learning models
Save this course
Save Creating Machine Learning Models 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 Creating Machine Learning Models with these
activities:
Connect with professionals in the machine learning field
Show steps
Expand your professional network and gain insights from other practitioners in the field.
Browse courses on
Machine Learning
Show steps
-
Attend industry meetups, conferences, or online forums related to machine learning
-
Introduce yourself to other attendees
Review machine learning terminology
Show steps
Become accustomed to the concepts of Machine Learning models by reviewing related terminology.
Browse courses on
Machine Learning
Show steps
-
Review the key terms and concepts of machine learning.
-
Create flashcards or a glossary of terms.
-
Practice using the terms in context.
Organize and review learning materials
Show steps
Become accustomed to the concepts of Machine Learning models by reviewing course materials.
Browse courses on
Machine Learning
Show steps
-
Review the notes and materials from past courses in machine learning
-
Review foundational statistics
-
Review previous coding projects you have completed
Five other activities
Expand to see all activities and additional details
Show all eight activities
Build a simple machine learning model
Show steps
Build upon the concepts of supervised machine learning by completing a tutorial on building a simple model.
Browse courses on
Machine Learning
Show steps
-
Choose a tutorial for building a simple machine learning model
-
Follow the steps in the tutorial to build the model.
-
Test your model with some data.
-
Evaluate the performance of the machine learning model
Practice implementing common machine learning algorithms
Show steps
Practice applying concepts of machine learning algorithms with hands-on exercises.
Browse courses on
Machine Learning
Show steps
-
Solve practice problems or coding challenges involving machine learning algorithms
-
Participate in online coding competitions or hackathons that focus on machine learning
Experiment with different machine learning algorithms for a project
Show steps
Apply the knowledge gained through this course in a project that involves experimenting with different machine learning algorithms.
Browse courses on
Machine Learning
Show steps
-
Choose a project idea that aligns with your interests and career goals.
-
Gather the necessary data and resources.
-
Explore and compare various machine learning algorithms for your project.
-
Train and evaluate your machine learning models.
-
Present your findings and discuss the implications of your project.
Attend a workshop on advanced machine learning techniques
Show steps
Extend your knowledge further by attending an industry-based machine learning workshop.
Browse courses on
Machine Learning
Show steps
-
Identify and register for a relevant workshop on advanced machine learning techniques.
-
Attend the workshop with a focus on actively learning and engaging in discussions.
-
Apply the knowledge and insights gained in your future machine learning endeavors.
Mentor or tutor students in Machine Learning topics
Show steps
Deepen your understanding of Machine Learning concepts by teaching and assisting others.
Browse courses on
Machine Learning
Show steps
-
Identify opportunities to volunteer as a mentor or tutor for students in Machine Learning courses.
-
Prepare and deliver learning materials, provide guidance, and answer questions for your mentees.
Connect with professionals in the machine learning field
Show steps
Expand your professional network and gain insights from other practitioners in the field.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Attend industry meetups, conferences, or online forums related to machine learning
- Introduce yourself to other attendees
Review machine learning terminology
Show steps
Become accustomed to the concepts of Machine Learning models by reviewing related terminology.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review the key terms and concepts of machine learning.
- Create flashcards or a glossary of terms.
- Practice using the terms in context.
Organize and review learning materials
Show steps
Become accustomed to the concepts of Machine Learning models by reviewing course materials.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review the notes and materials from past courses in machine learning
- Review foundational statistics
- Review previous coding projects you have completed
Five other activities
Expand to see all activities and additional details
Show all eight activities
Build a simple machine learning model
Show steps
Build upon the concepts of supervised machine learning by completing a tutorial on building a simple model.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a tutorial for building a simple machine learning model
- Follow the steps in the tutorial to build the model.
- Test your model with some data.
- Evaluate the performance of the machine learning model
Practice implementing common machine learning algorithms
Show steps
Practice applying concepts of machine learning algorithms with hands-on exercises.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Solve practice problems or coding challenges involving machine learning algorithms
- Participate in online coding competitions or hackathons that focus on machine learning
Experiment with different machine learning algorithms for a project
Show steps
Apply the knowledge gained through this course in a project that involves experimenting with different machine learning algorithms.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a project idea that aligns with your interests and career goals.
- Gather the necessary data and resources.
- Explore and compare various machine learning algorithms for your project.
- Train and evaluate your machine learning models.
- Present your findings and discuss the implications of your project.
Attend a workshop on advanced machine learning techniques
Show steps
Extend your knowledge further by attending an industry-based machine learning workshop.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify and register for a relevant workshop on advanced machine learning techniques.
- Attend the workshop with a focus on actively learning and engaging in discussions.
- Apply the knowledge and insights gained in your future machine learning endeavors.
Mentor or tutor students in Machine Learning topics
Show steps
Deepen your understanding of Machine Learning concepts by teaching and assisting others.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify opportunities to volunteer as a mentor or tutor for students in Machine Learning courses.
- Prepare and deliver learning materials, provide guidance, and answer questions for your mentees.
Career center
Learners who complete Creating Machine Learning Models will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
To be successful as a Data Scientist, one needs to be able to frame machine learning models in a manner appropriate to the problem they are trying to solve, and the data that they have available. This course, Creating Machine Learning Models, can help you develop the skills and knowledge you need to do just that.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers need to be able to implement a range of techniques to solve supervised learning problems of classification and regression. This course can help you gain an understanding of the model algorithms you can use for these tasks.
Data Analyst
Data Analyst
Data Analysts need to be able to use machine learning techniques to solve business problems. This course can help you develop the skills you need to do just that by teaching you about the different types of machine learning algorithms and how to implement them.
Software Engineer
Software Engineer
Software Engineers who specialize in machine learning need to have a strong foundation in the principles of machine learning. This course can help you build that foundation by teaching you about the important types of machine learning algorithms and the classic machine learning workflow.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use machine learning techniques to solve business problems. This course can help you develop the skills you need to do just that by teaching you about the different types of machine learning algorithms and how to implement them.
Data Architect
Data Architect
Data Architects who work with machine learning need to have a good understanding of the principles of machine learning. This course can help you build that understanding by teaching you about the different types of machine learning algorithms and the classic machine learning workflow.
Database Administrator
Database Administrator
Database Administrators who work with machine learning need to have a good understanding of the principles of machine learning. This course can help you build that understanding by teaching you about the different types of machine learning algorithms and the classic machine learning workflow.
Product Manager
Product Manager
Product Managers who work on machine learning products need to have a good understanding of the principles of machine learning. This course can help you build that understanding by teaching you about the different types of machine learning algorithms and the classic machine learning workflow.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use machine learning techniques to analyze data and make predictions. This course can help you develop the skills you need to do just that by teaching you about the different types of machine learning algorithms and how to implement them.
Statistician
Statistician
Statisticians who work with machine learning need to have a good understanding of the principles of machine learning. This course can help you build that understanding by teaching you about the different types of machine learning algorithms and the classic machine learning workflow.
Business Analyst
Business Analyst
Business Analysts who work with machine learning projects need to have a good understanding of the principles of machine learning. This course can help you build that understanding by teaching you about the different types of machine learning algorithms and the classic machine learning workflow.
Information Scientist
Information Scientist
Information Scientists who work with machine learning need to have a good understanding of the principles of machine learning. This course can help you build that understanding by teaching you about the different types of machine learning algorithms and the classic machine learning workflow.
Systems Engineer
Systems Engineer
Systems Engineers who work with machine learning need to have a good understanding of the principles of machine learning. This course can help you build that understanding by teaching you about the different types of machine learning algorithms and the classic machine learning workflow.
Computer Scientist
Computer Scientist
Computer Scientists who work with machine learning need to have a good understanding of the principles of machine learning. This course can help you build that understanding by teaching you about the different types of machine learning algorithms and the classic machine learning workflow.
Network Engineer
Network Engineer
Network Engineers who work with machine learning need to have a good understanding of the principles of machine learning. This course can help you build that understanding by teaching you about the different types of machine learning algorithms and the classic machine learning workflow.
For more career information including salaries, visit:
OpenCourser.com/course/ij0e2d/creating
Reading list
We've selected 13 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
Creating Machine Learning Models.
Provides a practical introduction to machine learning using Python. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for anyone who wants to learn how to apply machine learning to real-world problems.
Provides a theoretical foundation for machine learning. It covers a wide range of topics, from probability theory to Bayesian inference. It valuable resource for anyone who wants to understand the mathematical foundations of machine learning.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, from neural networks to convolutional neural networks. It valuable resource for anyone who wants to learn more about deep learning.
Provides a gentle introduction to machine learning. It covers a wide range of topics, from basic concepts to advanced techniques. It valuable resource for anyone who wants to learn more about machine learning without getting too bogged down in the details.
Provides a hands-on introduction to machine learning. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for anyone who wants to learn how to apply machine learning to real-world problems.
Provides a comprehensive overview of statistical learning. It covers a wide range of topics, from linear regression to support vector machines. It valuable resource for anyone who wants to learn more about the mathematical foundations of machine learning.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, from neural networks to graphical models. It valuable resource for anyone who wants to learn more about the mathematical foundations of machine learning.
Save
Provides a comprehensive overview of machine learning algorithms. It covers a wide range of topics, from linear regression to support vector machines. It valuable resource for anyone who wants to learn more about the mathematical foundations of machine learning.
Provides a comprehensive overview of machine learning algorithms and techniques. It covers a wide range of topics, from basic concepts to advanced techniques. It valuable resource for anyone who wants to learn more about machine learning.
Provides a comprehensive overview of machine learning using R. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for anyone who wants to learn how to apply machine learning to real-world problems using R.
Provides a comprehensive overview of machine learning using Python. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for anyone who wants to learn how to apply machine learning to real-world problems using Python.
Provides a comprehensive overview of machine learning for business. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for anyone who wants to learn how to apply machine learning to real-world business problems.
Provides a comprehensive overview of machine learning for healthcare. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for anyone who wants to learn how to apply machine learning to real-world healthcare problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ij0e2d/creating
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Time
164 hours
Level
Intermediate
Via
Pluralsight
Instructor
Janani Ravi
Language
English
This course belongs to a
collection
called
Machine Learning Literacy. It's comprised of five courses:
- Preparing Data for Machine Learning
- Designing a Machine Learning Model
- Creating Machine Learning Models (viewing)
- Deploying Machine Learning Solutions
- Building Your First Machine Learning Solution
Good to know
Helps students understand how to choose the best model for their needs
Covers a range of techniques for solving supervised learning problems
Provides an intuitive understanding of classification and regression model algorithms
Teaches how to build clustering models using different algorithms
Helps students validate the results of their machine learning models
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