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Janani Ravi
This course covers the important differences between various canonical problems in machine learning, as well as the considerations in choosing the right solution techniques, based on the specifics of the problem you are trying to solve and the data that you have available.
Read more
This course covers the important differences between various canonical problems in machine learning, as well as the considerations in choosing the right solution techniques, based on the specifics of the problem you are trying to solve and the data that you have available.
Read more
This course covers the important differences between various canonical problems in machine learning, as well as the considerations in choosing the right solution techniques, based on the specifics of the problem you are trying to solve and the data that you have available.
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, Designing a Machine Learning Model you will gain the ability to appropriately frame your use-case and then choose the right solution technique to model it.
First, you will learn how rule-based systems and ML systems differ and how traditional and deep learning models work. Next, you will discover how supervised, unsupervised, and reinforcement learning techniques differ from each other. You will learn how classic supervised learning techniques such as regression and classification complement classic unsupervised techniques such as clustering and dimensionality reduction. You will then understand the assumptions and outcomes of these four classes of techniques and how solutions can be evaluated.
Finally, you will round out your knowledge by designing end-to-end ML workflows for canonical ML problems, ensemble learning, and neural networks.
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 technique for your use-case.
Register for this course and see more details by visiting:
OpenCourser.com/course/vd09tg/designing
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What's inside
Syllabus
Course Overview
Exploring Approaches to Machine Learning
Choosing the Right Machine Learning Problem
Choosing the Right Machine Learning Solution
Read more
Syllabus
Course Overview
Exploring Approaches to Machine Learning
Choosing the Right Machine Learning Problem
Choosing the Right Machine Learning Solution
Read more
Building Simple Machine Learning Solutions
Designing Machine Learning Workflows
Building Ensemble Solutions and Neural Network Solutions
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches the differences between rule-based systems and ML systems
Covers traditional and deep learning models, their functions, and their uses
Compares the characteristics and functions of supervised, unsupervised, and reinforcement learning techniques
Covers ensemble learning and neural networks, which are powerful techniques for solving complex ML problems
Provides end-to-end ML workflow design for canonical ML problems, giving learners a real-world perspective
Provides a strong foundation in ML model design and selection, which is crucial for building effective ML solutions
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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 Designing a Machine Learning Model with these
activities:
Data Mining: Concepts and Techniques, 3rd Edition by Jiawei Han, Micheline Kamber, and Jian Pei
Show steps
Review key data mining concepts from a classic textbook, refreshing your knowledge base and building a foundation for advanced topics.
View
Data Mining: Concepts and Techniques
on Amazon
Show steps
Organize Course Resources
Show steps
Establish a systematic way to review course materials by organizing notes, slides, assignments, and other resources, aiding effective recall and knowledge retention.
Show steps
-
Gather and sort notes, slides, and assignments
-
Create a clear and organized file structure
-
Consider using note-taking or organizational software
Review linear algebra fundamentals
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Revisit the basics of linear algebra to strengthen your foundation for understanding machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
-
Review the concepts of vectors, matrices, and linear transformations.
-
Practice solving systems of linear equations using methods like Gaussian elimination.
-
Understand the basics of vector spaces, including subspaces, span, and linear independence.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Review supervised and unsupervised learning basics
Show steps
Revisit basic concepts of supervised and unsupervised learning to refresh understanding and prepare for advanced topics in this course.
Browse courses on
Supervised Learning
Show steps
-
Re-read course textbook chapters on supervised and unsupervised learning
-
Review online tutorials on these topics
Practice implementing supervised and unsupervised learning algorithms in Python
Show steps
Develop hands-on experience in applying supervised and unsupervised learning concepts through guided tutorials, solidifying understanding and building skills.
Browse courses on
Python
Show steps
-
Follow online tutorials on implementing specific supervised and unsupervised learning algorithms
-
Practice implementing these algorithms on sample datasets
Kaggle ML Competitions
Show steps
Apply your ML knowledge and coding skills by participating in Kaggle competitions, enhancing your practical abilities.
Browse courses on
Supervised Learning
Show steps
-
Register for a Kaggle account and join a relevant competition
-
Download the dataset and familiarize yourself with the problem
-
Explore different ML algorithms and techniques
-
Train, evaluate, and iterate your models
-
Submit your solution and analyze your results
Explore supervised learning algorithms with scikit-learn
Show steps
Get hands-on experience implementing supervised learning algorithms using Python's scikit-learn library.
Browse courses on
Supervised Learning
Show steps
-
Install scikit-learn and familiarize yourself with its basic functionality.
-
Follow tutorials to implement regression algorithms like linear regression and decision trees.
-
Explore classification algorithms like logistic regression and support vector machines.
-
Gain insights into model evaluation metrics and performance analysis.
Solve regression and classification problems
Show steps
Practicing solving different types of machine learning problems will deepen understanding of different model types
Browse courses on
Classification Problems
Show steps
-
Identify the type of machine learning problem you are trying to solve
-
Choose the appropriate machine learning model
-
Train the model on your data
-
Evaluate the model's performance
Create a side-by-side comparison of supervised and unsupervised learning algorithms
Show steps
By comparing and contrasting supervised and unsupervised learning algorithms, you'll enhance your understanding of their strengths, limitations, and appropriate applications.
Browse courses on
Supervised Learning
Show steps
-
Gather information and research on both types of algorithms
-
Create a table or diagram outlining their key differences, similarities, and use cases
Create a cheat sheet of common machine learning algorithms
Show steps
Develop a comprehensive summary of machine learning algorithms, covering their types, strengths, and applications.
Browse courses on
Machine Learning Algorithms
Show steps
-
Identify the different types of machine learning algorithms (supervised, unsupervised, reinforcement learning).
-
Summarize the key characteristics, strengths, and weaknesses of each algorithm.
-
Provide examples of real-world applications where each algorithm is commonly used.
Practice identifying appropriate machine learning models for specific problems
Show steps
Refine your decision-making skills in selecting the most suitable machine learning models for various problems, which is crucial for effective machine learning projects.
Browse courses on
Machine Learning Models
Show steps
-
Analyze real-world machine learning problems and identify their characteristics
-
Research different machine learning models and their capabilities
-
Practice matching specific problems to appropriate models
Solve coding challenges on LeetCode
Show steps
Enhance your problem-solving skills and deepen your understanding of machine learning concepts by solving coding challenges.
Browse courses on
Coding Challenges
Show steps
-
Sign up for a LeetCode account and select problems related to machine learning.
-
Attempt to solve the problems on your own, focusing on understanding the underlying principles.
-
Review solutions and explanations to gain insights into alternative approaches.
-
Practice regularly to improve your coding proficiency and problem-solving abilities.
Develop a machine learning workflow for a specific problem
Show steps
By designing and implementing a complete machine learning workflow, you'll gain practical experience in the entire lifecycle of machine learning problem-solving.
Browse courses on
Machine Learning Workflow
Show steps
-
Define a specific problem and gather relevant data
-
Choose appropriate machine learning techniques and algorithms
-
Implement the workflow and evaluate its performance
Build an End-to-End Machine Learning Pipeline
Show steps
Integrate your knowledge from the course by designing and implementing a complete ML pipeline, demonstrating your understanding of the ML process.
Show steps
-
Define the problem and gather data
-
Preprocess and explore the data
-
Select and train ML models
-
Evaluate and fine-tune the models
-
Deploy and monitor the ML solution
Participate in online discussions to share knowledge and clarify doubts
Show steps
Engaging in discussions will help reinforce your understanding, clarify misconceptions, and broaden your perspective through interactions with peers.
Browse courses on
Machine Learning
Show steps
-
Join online discussion forums or meetups
-
Participate in discussions, ask questions, and share insights
Data Mining: Concepts and Techniques, 3rd Edition by Jiawei Han, Micheline Kamber, and Jian Pei
Show steps
Review key data mining concepts from a classic textbook, refreshing your knowledge base and building a foundation for advanced topics.
View
Data Mining: Concepts and Techniques
on Amazon
Show steps
Show steps
Show steps
Organize Course Resources
Show steps
Establish a systematic way to review course materials by organizing notes, slides, assignments, and other resources, aiding effective recall and knowledge retention.
Show steps
Show steps
Show steps
- Gather and sort notes, slides, and assignments
- Create a clear and organized file structure
- Consider using note-taking or organizational software
Review linear algebra fundamentals
Show steps
Revisit the basics of linear algebra to strengthen your foundation for understanding machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review the concepts of vectors, matrices, and linear transformations.
- Practice solving systems of linear equations using methods like Gaussian elimination.
- Understand the basics of vector spaces, including subspaces, span, and linear independence.
12 other activities
Expand to see all activities and additional details
Show all 15 activities
Review supervised and unsupervised learning basics
Show steps
Revisit basic concepts of supervised and unsupervised learning to refresh understanding and prepare for advanced topics in this course.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Re-read course textbook chapters on supervised and unsupervised learning
- Review online tutorials on these topics
Practice implementing supervised and unsupervised learning algorithms in Python
Show steps
Develop hands-on experience in applying supervised and unsupervised learning concepts through guided tutorials, solidifying understanding and building skills.
Browse courses on
Python
Show steps
Show steps
Show steps
- Follow online tutorials on implementing specific supervised and unsupervised learning algorithms
- Practice implementing these algorithms on sample datasets
Kaggle ML Competitions
Show steps
Apply your ML knowledge and coding skills by participating in Kaggle competitions, enhancing your practical abilities.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Register for a Kaggle account and join a relevant competition
- Download the dataset and familiarize yourself with the problem
- Explore different ML algorithms and techniques
- Train, evaluate, and iterate your models
- Submit your solution and analyze your results
Explore supervised learning algorithms with scikit-learn
Show steps
Get hands-on experience implementing supervised learning algorithms using Python's scikit-learn library.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Install scikit-learn and familiarize yourself with its basic functionality.
- Follow tutorials to implement regression algorithms like linear regression and decision trees.
- Explore classification algorithms like logistic regression and support vector machines.
- Gain insights into model evaluation metrics and performance analysis.
Solve regression and classification problems
Show steps
Practicing solving different types of machine learning problems will deepen understanding of different model types
Browse courses on
Classification Problems
Show steps
Show steps
Show steps
- Identify the type of machine learning problem you are trying to solve
- Choose the appropriate machine learning model
- Train the model on your data
- Evaluate the model's performance
Create a side-by-side comparison of supervised and unsupervised learning algorithms
Show steps
By comparing and contrasting supervised and unsupervised learning algorithms, you'll enhance your understanding of their strengths, limitations, and appropriate applications.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Gather information and research on both types of algorithms
- Create a table or diagram outlining their key differences, similarities, and use cases
Create a cheat sheet of common machine learning algorithms
Show steps
Develop a comprehensive summary of machine learning algorithms, covering their types, strengths, and applications.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Identify the different types of machine learning algorithms (supervised, unsupervised, reinforcement learning).
- Summarize the key characteristics, strengths, and weaknesses of each algorithm.
- Provide examples of real-world applications where each algorithm is commonly used.
Practice identifying appropriate machine learning models for specific problems
Show steps
Refine your decision-making skills in selecting the most suitable machine learning models for various problems, which is crucial for effective machine learning projects.
Browse courses on
Machine Learning Models
Show steps
Show steps
Show steps
- Analyze real-world machine learning problems and identify their characteristics
- Research different machine learning models and their capabilities
- Practice matching specific problems to appropriate models
Solve coding challenges on LeetCode
Show steps
Enhance your problem-solving skills and deepen your understanding of machine learning concepts by solving coding challenges.
Browse courses on
Coding Challenges
Show steps
Show steps
Show steps
- Sign up for a LeetCode account and select problems related to machine learning.
- Attempt to solve the problems on your own, focusing on understanding the underlying principles.
- Review solutions and explanations to gain insights into alternative approaches.
- Practice regularly to improve your coding proficiency and problem-solving abilities.
Develop a machine learning workflow for a specific problem
Show steps
By designing and implementing a complete machine learning workflow, you'll gain practical experience in the entire lifecycle of machine learning problem-solving.
Browse courses on
Machine Learning Workflow
Show steps
Show steps
Show steps
- Define a specific problem and gather relevant data
- Choose appropriate machine learning techniques and algorithms
- Implement the workflow and evaluate its performance
Build an End-to-End Machine Learning Pipeline
Show steps
Integrate your knowledge from the course by designing and implementing a complete ML pipeline, demonstrating your understanding of the ML process.
Show steps
Show steps
Show steps
- Define the problem and gather data
- Preprocess and explore the data
- Select and train ML models
- Evaluate and fine-tune the models
- Deploy and monitor the ML solution
Participate in online discussions to share knowledge and clarify doubts
Show steps
Engaging in discussions will help reinforce your understanding, clarify misconceptions, and broaden your perspective through interactions with peers.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Join online discussion forums or meetups
- Participate in discussions, ask questions, and share insights
Career center
Learners who complete Designing a Machine Learning Model will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and implement systems to automate data analysis processes, including data collection, cleaning, feature engineering, and model evaluation. This course covers how to effectively frame ML models, choose the right solution technique, and design end-to-end ML workflows for common ML problems. With hands-on experience in building ensemble learning and neural network solutions, you will be well-prepared for a career as a Machine Learning Engineer.
Data Analyst
Data Analyst
Data Analysts translate raw data into insights that drive decision-making and strategic planning. As a Machine Learning enthusiast, this course introduces you to the important differences between various canonical problems in machine learning and considerations when choosing techniques to solve these problems. With the increasing popularity of Machine Learning, Data Analysts with expertise in recognizing the right ML problem setup and solution technique are highly sought after because they can efficiently translate raw data into valuable insights.
Research Scientist
Research Scientist
Research Scientists conduct research in various scientific fields, including Machine Learning. This course provides a solid foundation for Research Scientists who want to focus on Machine Learning research. By gaining a comprehensive understanding of different ML problem setups and solution techniques, you will be well-equipped to design and conduct innovative research in the field of Machine Learning.
Data Scientist
Data Scientist
Data Scientists extract knowledge and insights from data using Machine Learning techniques. This course will help you build foundational Machine Learning knowledge that is essential for Data Scientists to frame use cases and choose appropriate solution techniques for different ML problems. By gaining a comprehensive understanding of supervised, unsupervised, and reinforcement learning techniques, you will be equipped to excel as a Data Scientist.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain Artificial Intelligence systems. This course introduces fundamental Machine Learning concepts and techniques, which are essential for Artificial Intelligence Engineers to build and deploy effective AI systems. By learning about different ML problem setups and solution techniques, you will be able to effectively apply Machine Learning to solve complex AI problems.
Data Engineer
Data Engineer
Data Engineers design, develop, and maintain data pipelines that collect, store, and process data. This course will help you build a foundation in Machine Learning that is increasingly relevant for Data Engineers. By learning about different ML problem setups and solution techniques, you will be able to effectively collaborate with Machine Learning Engineers and Data Scientists to build and maintain data pipelines that support Machine Learning applications.
Solution Architect
Solution Architect
Solution Architects design and implement technology solutions. This course will help you build a foundation in Machine Learning that is increasingly relevant for Solution Architects. By learning about different ML problem setups and solution techniques, you will be able to effectively design and implement technology solutions that incorporate Machine Learning.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to improve the efficiency and effectiveness of complex systems. This course can be beneficial for Operations Research Analysts who want to incorporate Machine Learning techniques into their optimization models. By learning about different ML problem setups and solution techniques, you will be able to leverage Machine Learning to enhance the accuracy and effectiveness of your optimization models.
Data Architect
Data Architect
Data Architects design and manage data systems, including databases and data warehouses. This course will help you build a foundation in Machine Learning that is increasingly relevant for Data Architects. By learning about different ML problem setups and solution techniques, you will be able to effectively design and manage data systems that support Machine Learning applications.
Cloud Architect
Cloud Architect
Cloud Architects design and manage cloud computing systems. This course will help you build a foundation in Machine Learning that is increasingly relevant for Cloud Architects. By learning about different ML problem setups and solution techniques, you will be able to effectively design and manage cloud computing systems that support Machine Learning applications.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course provides a solid foundation for Software Engineers who want to integrate Machine Learning into their software applications. By learning about different ML problem setups and solution techniques, Software Engineers can make informed decisions and effectively apply Machine Learning to enhance their software applications.
Enterprise Architect
Enterprise Architect
Enterprise Architects design and manage the overall IT architecture of an organization. This course will help you build a foundation in Machine Learning that is increasingly relevant for Enterprise Architects. By learning about different ML problem setups and solution techniques, you will be able to effectively design and manage IT architectures that support Machine Learning applications.
Product Manager
Product Manager
Product Managers develop and manage products, including defining product requirements, gathering customer feedback, and working with engineering teams to develop and deliver products. This course can be beneficial for Product Managers who want to gain a deeper understanding of Machine Learning, as it provides insights on how to identify the right ML problem setup and solution technique for different product use cases.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course can be useful for Quantitative Analysts who want to incorporate Machine Learning techniques into their financial models. By learning about different ML problem setups and solution techniques, you will be able to leverage Machine Learning to enhance the accuracy and effectiveness of your financial models.
Business Analyst
Business Analyst
Business Analysts analyze business processes and develop solutions to improve efficiency and effectiveness. This course may be useful for Business Analysts who want to gain a basic understanding of Machine Learning techniques. By learning about different ML problem setups and solution techniques, you will be able to identify opportunities to apply Machine Learning to solve business problems and improve decision-making.
For more career information including salaries, visit:
OpenCourser.com/course/vd09tg/designing
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
Designing a Machine Learning Model.
Save
Comprehensive guide to deep learning, covering the latest techniques and algorithms. It is an essential resource for anyone who wants to learn more about deep learning.
Provides a probabilistic perspective on machine learning, covering topics such as Bayesian inference and graphical models. It valuable resource for anyone who wants to learn more about the theoretical foundations of machine learning.
Provides a comprehensive overview of pattern recognition and machine learning, covering both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about pattern recognition and machine learning.
Provides a comprehensive overview of statistical learning with sparsity, covering both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about statistical learning with sparsity.
Provides a comprehensive overview of statistical learning, covering both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about statistical learning.
Provides a comprehensive overview of statistical learning, covering both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about statistical learning.
Provides a practical introduction to machine learning, covering both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about machine learning.
Provides a practical introduction to machine learning using Scikit-Learn, Keras, and TensorFlow. It valuable resource for anyone who wants to learn more about machine learning and how to apply it to real-world problems.
Provides a practical introduction to machine learning, covering both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about machine learning.
Provides a comprehensive overview of deep learning for natural language processing, covering both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about deep learning for natural language processing.
Provides a comprehensive overview of natural language processing, covering both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about natural language processing.
Save
Provides a comprehensive overview of computer vision, covering both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about computer vision.
Provides a comprehensive overview of speech and language processing, covering both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about speech and language processing.
Provides a comprehensive overview of reinforcement learning, covering both the theoretical foundations and practical applications. It valuable resource for anyone who wants to learn more about reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/vd09tg/designing
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Time
205 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 (viewing)
- Creating Machine Learning Models
- Deploying Machine Learning Solutions
- Building Your First Machine Learning Solution
Good to know
Teaches the differences between rule-based systems and ML systems
Covers traditional and deep learning models, their functions, and their uses
Compares the characteristics and functions of supervised, unsupervised, and reinforcement learning techniques
Covers ensemble learning and neural networks, which are powerful techniques for solving complex ML problems
Provides end-to-end ML workflow design for canonical ML problems, giving learners a real-world perspective
Provides a strong foundation in ML model design and selection, which is crucial for building effective ML solutions
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