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Stacey McBrine,
Megan Smith Branch,
and
Sarah Haq
This course is designed for business professionals that wish to identify basic concepts that make up machine learning, test model hypothesis using a design of experiments and train, tune and evaluate models using algorithms that solve classification, regression and forecasting, and clustering problems.
To be successful in this course a learner should have a background in computing technology, including some aptitude in computer programming.
Register for this course and see more details by visiting:
OpenCourser.com/course/q0ernc/train
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What's inside
Syllabus
Prepare to Train a Machine Learning Model
In the previous courses in the CDSP specialization, your data underwent a great deal of preparation. It's time to start looking at developing machine learning models. These models will be instrumental in achieving your business objectives because they can intelligently estimate much about the world. But before you start building these models, you need to have a firm grasp on what goes into machine learning and what it means to use machine learning to test a hypothesis.
Read more
Syllabus
Prepare to Train a Machine Learning Model
In the previous courses in the CDSP specialization, your data underwent a great deal of preparation. It's time to start looking at developing machine learning models. These models will be instrumental in achieving your business objectives because they can intelligently estimate much about the world. But before you start building these models, you need to have a firm grasp on what goes into machine learning and what it means to use machine learning to test a hypothesis.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
This course is designed for business professionals who wish to identify basic concepts that make up machine learning, test model hypothesis using a design of experiments and train, tune and evaluate models using algorithms that solve classification, regression and forecasting, and clustering problems
Suitable for business professionals who have a background in computing technology
Provides hands-on experience with machine learning tasks using Jupyter notebooks
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Reviews summary
Practical machine learning for business
According to students, this course offers a strong practical foundation in machine learning tailored for business professionals with programming aptitude. It excels in covering classification, regression, and clustering models, emphasizing hands-on application through Jupyter notebooks and the iterative process of model training, tuning, and evaluation. Recent feedback indicates significant improvements in content clarity and lab functionality, making it a highly valuable learning experience for those looking to apply ML in real-world scenarios. While some older reviews noted a lack of dedicated forecasting content or found certain areas to lack sufficient depth, the overall consensus from recent students is positive, particularly regarding its applicability to business objectives.
Assumes prior programming aptitude, which is essential for success in this course.
"Definitely better if you are comfortable with coding already."
"If you're coming in with a strong Python background, it's quite manageable."
"I sometimes felt that certain concepts were introduced too quickly, especially for individuals who are not strong in programming."
"I struggled with the math behind some algorithms, which wasn't adequately covered."
Provides solid foundational coverage of classification, regression, and clustering models.
"It covers the basics of classification and regression well, and the clustering section was a good introduction."
"The explanation of model evaluation was particularly strong, breaking down complex ideas effectively."
"This course provided me with a strong foundation in using ML for common tasks."
Course excels in hands-on application of ML concepts to business scenarios.
"The Jupyter notebooks were truly invaluable. It really helped me apply ML concepts in my business context immediately."
"The practical exercises in the final module really cemented the learning."
"I learned how to use practical tools and strategies that I could apply immediately to my work."
"The hands-on coding and projects are the strongest part of the course for me."
Recent updates have significantly enhanced content clarity and lab functionality.
"The quality of explanations has improved a lot since I started other courses in this specialization."
"The course has clearly been updated. The Jupyter notebooks now run smoothly, and the explanations are much clearer."
"The recent updates have made the labs much smoother."
Some learners wished for more in-depth coverage, particularly for forecasting.
"I was hoping for more depth in forecasting, which was mentioned in the description but not a dedicated module."
"I found this course to be superficial, touching on many topics but not going deep enough into any one area."
"It felt more like a very basic introduction that sometimes skipped crucial details."
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 Train Machine Learning Models with these
activities:
Review statistical concepts
Show steps
This course assumes that you have a working knowledge of basic statistical concepts. Review these concepts to ensure that you are ready to succeed in this course.
Browse courses on
Statistics
Show steps
-
Review the concepts of probability and statistics.
-
Practice solving statistical problems.
Review Python basics
Show steps
This course requires a basic understanding of Python. Review the basics to ensure that you are ready to succeed in this course.
Browse courses on
Python
Show steps
-
Review the syntax of Python.
-
Practice writing simple Python programs.
Learn different regression algorithms
Show steps
Tutorials provide step-by-step guidance on how to perform important tasks in the field of machine learning.
Browse courses on
Regression
Show steps
-
Find 3 different regression algorithms that are covered in the course.
-
Find a tutorial or video that explains the algorithm's benefits and implementation.
-
Follow along with the tutorial and implement the algorithm in your own environment.
-
Write the steps of the algorithm in your own words.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attempt practice problems on regression
Show steps
Practice problems solitify your understanding of topics through practical application and exposure.
Browse courses on
Regression
Show steps
-
Review class coverage of regression.
-
Find a list of practice problems or a graded quiz online.
-
Solve at least twenty (20) practice problems.
-
Check your answers against the answer set
-
For incorrect answers, review the problem and material.
Develop a data visualization for a regression model
Show steps
Developing a data visualization is a great way to demonstrate your understanding of a regression model and communicate your findings to others.
Browse courses on
Regression
Show steps
-
Choose a regression model that you have built.
-
Identify the key insights that you want to communicate with your visualization.
-
Select a data visualization tool, such as Tableau or Power BI.
-
Create a visualization that effectively communicates your insights.
-
Share your visualization with others and get feedback.
Write a blog post about a machine learning project you have worked on
Show steps
Writing a blog post about a machine learning project you have worked on is a great way to share your knowledge and insights with others.
Browse courses on
Machine Learning
Show steps
-
Choose a machine learning project that you have worked on.
-
Write a blog post about your project, including the following information:
-
The problem that you were trying to solve.
-
The data that you used.
-
The algorithm that you used.
-
The results that you achieved.
Contribute to an open-source machine learning project
Show steps
Contributing to an open-source machine learning project is a great way to gain experience and learn from others.
Browse courses on
Machine Learning
Show steps
-
Find an open-source machine learning project that you are interested in.
-
Read the project's documentation and contribute something.
-
Communicate with the project maintainers.
Mentor a junior machine learning engineer
Show steps
Mentoring a junior machine learning engineer is a great way to give back to the community and help others to succeed.
Browse courses on
Machine Learning
Show steps
-
Find a junior machine learning engineer who is looking for a mentor.
-
Meet with the mentee regularly to provide guidance and support.
-
Help the mentee to develop their skills and knowledge.
Review statistical concepts
Show steps
This course assumes that you have a working knowledge of basic statistical concepts. Review these concepts to ensure that you are ready to succeed in this course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review the concepts of probability and statistics.
- Practice solving statistical problems.
Review Python basics
Show steps
This course requires a basic understanding of Python. Review the basics to ensure that you are ready to succeed in this course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review the syntax of Python.
- Practice writing simple Python programs.
Learn different regression algorithms
Show steps
Tutorials provide step-by-step guidance on how to perform important tasks in the field of machine learning.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Find 3 different regression algorithms that are covered in the course.
- Find a tutorial or video that explains the algorithm's benefits and implementation.
- Follow along with the tutorial and implement the algorithm in your own environment.
- Write the steps of the algorithm in your own words.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attempt practice problems on regression
Show steps
Practice problems solitify your understanding of topics through practical application and exposure.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Review class coverage of regression.
- Find a list of practice problems or a graded quiz online.
- Solve at least twenty (20) practice problems.
- Check your answers against the answer set
- For incorrect answers, review the problem and material.
Develop a data visualization for a regression model
Show steps
Developing a data visualization is a great way to demonstrate your understanding of a regression model and communicate your findings to others.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Choose a regression model that you have built.
- Identify the key insights that you want to communicate with your visualization.
- Select a data visualization tool, such as Tableau or Power BI.
- Create a visualization that effectively communicates your insights.
- Share your visualization with others and get feedback.
Write a blog post about a machine learning project you have worked on
Show steps
Writing a blog post about a machine learning project you have worked on is a great way to share your knowledge and insights with others.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a machine learning project that you have worked on.
- Write a blog post about your project, including the following information:
- The problem that you were trying to solve.
- The data that you used.
- The algorithm that you used.
- The results that you achieved.
Contribute to an open-source machine learning project
Show steps
Contributing to an open-source machine learning project is a great way to gain experience and learn from others.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find an open-source machine learning project that you are interested in.
- Read the project's documentation and contribute something.
- Communicate with the project maintainers.
Mentor a junior machine learning engineer
Show steps
Mentoring a junior machine learning engineer is a great way to give back to the community and help others to succeed.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a junior machine learning engineer who is looking for a mentor.
- Meet with the mentee regularly to provide guidance and support.
- Help the mentee to develop their skills and knowledge.
Career center
Learners who complete Train Machine Learning Models will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
As a Data Scientist, you'll use machine learning to solve complex business problems. This course can help you build a foundation in machine learning, which is essential for success in this role. You'll learn how to train, tune, and evaluate machine learning models, which will give you the skills you need to develop and implement data-driven solutions.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Data Analyst
Data Analyst
Data Analysts use machine learning to analyze data and extract insights. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Business Analyst
Business Analyst
Business Analysts use machine learning to solve business problems. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Software Engineer
Software Engineer
Software Engineers use machine learning to develop software applications. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Product Manager
Product Manager
Product Managers use machine learning to develop and manage products. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use machine learning to solve operational problems. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Financial Analyst
Financial Analyst
Financial Analysts use machine learning to analyze financial data. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Marketing Analyst
Marketing Analyst
Marketing Analysts use machine learning to analyze marketing data. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Healthcare Analyst
Healthcare Analyst
Healthcare Analysts use machine learning to analyze healthcare data. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Risk Analyst
Risk Analyst
Risk Analysts use machine learning to analyze risk data. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Actuary
Actuary
Actuaries use machine learning to analyze insurance data. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use machine learning to analyze financial data. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Statistician
Statistician
Statisticians use machine learning to analyze data. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
Data Engineer
Data Engineer
Data Engineers use machine learning to build and maintain data pipelines. This course can help you build the skills you need to succeed in this role by teaching you how to train, tune, and evaluate machine learning models. You'll also learn about the different types of machine learning algorithms and how to apply them to real-world problems.
For more career information including salaries, visit:
OpenCourser.com/course/q0ernc/train
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
Train Machine Learning Models.
Provides a practical guide to machine learning using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for those who want to gain hands-on experience with these tools and build their own machine learning models.
Provides a comprehensive introduction to machine learning using Python. It covers a wide range of topics, from data preprocessing to model evaluation, and valuable resource for those who want to learn the basics of machine learning.
Provides a rigorous introduction to machine learning from a probabilistic perspective. It valuable resource for those who want to gain a deeper understanding of the theoretical foundations of machine learning.
Save
Provides a comprehensive overview of deep learning, a subfield of machine learning that has revolutionized many fields in recent years. It valuable resource for those who want to learn about the latest advances in deep learning.
Save
Provides a practical guide to machine learning for business professionals. It covers a wide range of topics, from data collection to model deployment, and valuable resource for those who want to learn how to use machine learning to solve business problems.
Provides a practical guide to machine learning for predictive data analytics. It covers a wide range of topics, from data preprocessing to model evaluation, and valuable resource for those who want to learn how to use machine learning to solve real-world problems.
Save
Provides a comprehensive overview of machine learning from an algorithmic perspective. It covers a wide range of topics, from supervised learning to unsupervised learning, and valuable resource for those who want to learn about the underlying algorithms that power machine learning.
Provides a practical guide to machine learning for hackers. It covers a wide range of topics, from data collection to model deployment, and valuable resource for those who want to learn how to use machine learning to solve real-world problems.
Provides a comprehensive overview of machine learning using the R programming language. It covers a wide range of topics, from data preprocessing to model evaluation, and valuable resource for those who want to learn how to use machine learning with R.
Provides a gentle introduction to machine learning using Python. It covers a wide range of topics, from data preprocessing to model evaluation, and valuable resource for those who want to learn the basics of machine learning with Python.
Provides a non-technical overview of machine learning. It covers a wide range of topics, from data collection to model deployment, and valuable resource for those who want to learn about machine learning without getting bogged down in the technical details.
Provides a practical guide to machine learning using a variety of programming languages. It covers a wide range of topics, from data preprocessing to model evaluation, and valuable resource for those who want to learn how to use machine learning to solve real-world problems.
Save
Provides a comprehensive overview of machine learning for the web. It covers a wide range of topics, from data collection to model deployment, and valuable resource for those who want to learn how to use machine learning to improve their websites and web applications.
Provides a comprehensive overview of machine learning. It covers a wide range of topics, from data preprocessing to model evaluation, and valuable resource for those who want to learn about the latest advances in machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/q0ernc/train
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Effort
4 weeks of study, 1–3 hours per week
Level
Intermediate
Via
Coursera
Institution
CertNexus
Instructors
Stacey McBrine
Megan Smith Branch
Sarah Haq
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
Bahasa Indonesia
ไทย
қазақша
This course belongs to a
collection
called
CertNexus Certified Data Science Practitioner. It's comprised of five courses:
- Extract, Transform, and Load Data
- Train Machine Learning Models (viewing)
- Analyze Data
- Finalize a Data Science Project
- Address Business Issues with Data Science
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