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Renée Cummings
Machine learning is not just a single task or even a small group of tasks; it is an entire process, one that practitioners must follow from beginning to end. It is this process—also called a workflow—that enables the organization to get the most useful results out of their machine learning technologies. No matter what form the final product or service takes, leveraging the workflow is key to the success of the business's AI solution.
Read more
Machine learning is not just a single task or even a small group of tasks; it is an entire process, one that practitioners must follow from beginning to end. It is this process—also called a workflow—that enables the organization to get the most useful results out of their machine learning technologies. No matter what form the final product or service takes, leveraging the workflow is key to the success of the business's AI solution.
Read more
Machine learning is not just a single task or even a small group of tasks; it is an entire process, one that practitioners must follow from beginning to end. It is this process—also called a workflow—that enables the organization to get the most useful results out of their machine learning technologies. No matter what form the final product or service takes, leveraging the workflow is key to the success of the business's AI solution.
This second course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate explores each step along the machine learning workflow, from problem formulation all the way to model presentation and deployment. The overall workflow was introduced in the previous course, but now you'll take a deeper dive into each of the important tasks that make up the workflow, including two of the most hands-on tasks: data analysis and model training. You'll also learn about how machine learning tasks can be automated, ensuring that the workflow can recur as needed, like most important business processes.
Ultimately, this course provides a practical framework upon which you'll build many more machine learning models in the remaining courses.
Register for this course and see more details by visiting:
OpenCourser.com/course/45zfay/follow
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What's inside
Syllabus
Collect the Dataset
The previous course in this specialization provided an overview of the machine learning workflow. Now, in this course, you'll dive deeper and actually go through the process step by step. In this first module, you'll begin by collecting the data that will be used as input to your machine learning projects.
Read more
Syllabus
Collect the Dataset
The previous course in this specialization provided an overview of the machine learning workflow. Now, in this course, you'll dive deeper and actually go through the process step by step. In this first module, you'll begin by collecting the data that will be used as input to your machine learning projects.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Examines each step in the machine learning workflow from problem formulation to deployment
Teaches how to analyze, prepare, and set up data for machine learning models
Develops practical skills in training and finalizing models crucial for the workflow
Describes how to automate machine learning tasks for recurring use in business processes
Offers hands-on labs and interactive materials for practical learning
Save this course
Create your own learning path.
Save this course to your list so you can find it easily later.
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Reviews summary
Practical machine learning workflow guide
According to learners, this course provides a practical and structured framework for understanding the entire machine learning workflow, from data collection and preparation to model training and deployment. Students particularly appreciate the hands-on labs and projects, which effectively bridge the gap between theory and application, making it highly valuable for professionals. While it offers a clear and logical progression, some feedback indicates that those with a strong programming background might find certain sections to be too conceptual or lacking in deep coding examples, preferring more in-depth technical implementation. Overall, it's seen as an excellent follow-up to foundational courses, offering a holistic view of ML project execution.
Ideal for those building on foundational ML knowledge.
"A great follow-up to the introductory course and perfectly suited for professionals looking to apply ML in real-world scenarios."
"Good for absolute beginners in ML workflows, perhaps."
"As someone building on the previous course, this was a logical and essential next step."
"This course helped me connect the dots from the previous introductory course."
Focuses on real-world application through engaging labs.
"The hands-on labs were practical and truly helped solidify my understanding of each step."
"The project at the end allowed me to apply all the learned steps in a cohesive manner."
"I appreciated the emphasis on data preparation and the final deployment steps, which are often overlooked in other courses."
"I learned how to use practical tools and strategies that I could apply immediately to my work."
Covers every crucial stage of the machine learning process.
"This course is exactly what I needed to understand the full ML workflow. The structure is logical, moving from data collection to deployment."
"Excellent practical course! It really fills the gap between theory and application. The emphasis on problem formulation and deployment is a huge plus..."
"I found this course to be quite beneficial in understanding the end-to-end ML process. The explanations for data analysis and preparation were particularly helpful."
"I now feel much more confident in approaching ML projects from a holistic perspective."
Offers a high-level view, but some want deeper coding.
"The course has good intentions, but it feels a bit basic for someone with a programming background. I was hoping for more hands-on coding challenges..."
"I was expecting more direct coding exercises and less conceptual lecturing. It lays a good foundation, but be prepared to do a lot of your own coding practice afterwards..."
"The course content is not as deep as I expected. It feels like a high-level overview rather than a deep dive into the workflow. I was looking for more robust, real-world case studies with associated code."
"I felt some sections could have delved deeper into specific coding examples or more complex techniques."
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 Follow a Machine Learning Workflow with these
activities:
Review Machine Learning Concepts
Show steps
Review the fundamental concepts of machine learning, such as supervised learning, unsupervised learning, and model evaluation.
Show steps
-
Review your notes or textbooks.
-
Take practice quizzes or complete online exercises.
-
Discuss the concepts with a study group or mentor.
Go over Python and Jupyter Notebook
Show steps
This course heavily utilizes Python and Jupyter Notebooks. Reviewing these tools before the course will make learning and completing assignments more accessible.
Browse courses on
Python
Show steps
-
Review the syntax and basics of Python
-
Review how to install and use Jupyter Notebook
Review Machine Learning Mathematics
Show steps
Review the mathematical concepts and techniques used in machine learning, such as linear algebra, probability, and calculus.
View
Mathematics for Machine Learning
on Amazon
Show steps
-
Read the book's introduction and first chapter.
-
Work through the practice problems in the first chapter.
-
Summarize the key concepts covered in the first chapter.
17 other activities
Expand to see all activities and additional details
Show all 20 activities
Organize your materials
Show steps
Keeping your materials organized will help you study more efficiently and prepare for assignments.
Show steps
-
Create folders for notes, assignments, and quizzes
-
Create a system for naming and storing files
-
Review your syllabus and make a note of important dates
Follow a Machine Learning Tutorial
Show steps
Follow a tutorial that provides a hands-on introduction to machine learning, covering topics such as data preprocessing, model training, and evaluation.
Browse courses on
Machine Learning Basics
Show steps
-
Choose a tutorial that aligns with your learning goals.
-
Follow the tutorial step-by-step, completing all exercises.
-
Document your progress and any challenges you encounter.
Follow tutorials on Python and Jupyter
Show steps
This course builds on foundational Python, Jupyter, and Machine Learning skills. Following tutorials will help you review the prerequisites and set you up for success.
Browse courses on
Python
Show steps
-
Find tutorials on Python syntax and data structures
-
Find tutorials on Jupyter Notebook basics and data manipulation
-
Find tutorials on the basics of Machine Learning
-
Complete the tutorials and take notes on key concepts
Join a study group
Show steps
Working with peers can help you understand concepts more deeply, prepare for assignments, and stay motivated.
Show steps
-
Find or create a study group with other students in the course
-
Meet regularly to discuss course materials, work on assignments, and prepare for exams
-
Share notes, resources, and ideas with your group members
-
Provide support and encouragement to each other
Solve Python coding challenges
Show steps
Practicing Python coding challenges will improve your problem-solving and coding skills, which are essential for this course.
Browse courses on
Python
Show steps
-
Find coding challenges websites or platforms
-
Start with easy challenges and gradually increase the difficulty
-
Focus on understanding the problem and developing an efficient solution
-
Review your solutions and identify areas for improvement
Practice Identifying Problem Formulations
Show steps
Clarify problem formulations to ensure a solid foundation for the machine learning process.
Browse courses on
Problem Formulation
Show steps
-
Review common machine learning problem formulations.
-
Examine real-world case studies to identify problem formulations.
-
Practice formulating problems in the context of machine learning.
Practice Data Preprocessing Techniques
Show steps
Complete exercises and practice problems on data preprocessing techniques, such as data cleaning, normalization, and feature scaling.
Browse courses on
Data Preprocessing
Show steps
-
Find a dataset that requires preprocessing.
-
Apply different data preprocessing techniques to the dataset.
-
Evaluate the effectiveness of each technique.
Analyze a machine learning dataset
Show steps
Analyze a dataset to identify patterns, trends, and relationships that can be used to train machine learning models.
Show steps
-
Explore the dataset using visualization tools.
-
Calculate summary statistics and perform data cleaning.
-
Identify potential features and target variables.
Participate in a machine learning study group
Show steps
Collaborate with peers to discuss machine learning concepts, troubleshoot problems, and share knowledge.
Show steps
-
Join or create a study group.
-
Review course materials and prepare for meetings.
-
Actively participate in discussions and Q&A sessions.
Collaborate on a Machine Learning Project
Show steps
Work with peers to apply your knowledge and enhance your problem-solving abilities.
Show steps
-
Form study groups or join online communities related to machine learning.
-
Identify and select a project to work on as a team.
-
Divide responsibilities and work collaboratively on different aspects of the project.
-
Present your project findings and learnings to the group or community.
Develop a Visual Representation of the Machine Learning Workflow
Show steps
Create a visual aid to enhance understanding and retention of the machine learning workflow.
Show steps
-
Summarize the key steps of the machine learning workflow.
-
Identify visual elements to represent each step.
-
Create a visual representation using a tool like PowerPoint or Draw.io.
Write a blog post on a machine learning algorithm
Show steps
Reinforce your understanding of machine learning algorithms by explaining it to others.
Browse courses on
Machine Learning Algorithms
Show steps
-
Research and select an algorithm.
-
Explain the algorithm's concepts and functionality.
-
Provide examples and use cases.
-
Proofread and publish the blog post.
Build a simple Machine Learning model
Show steps
Building a simple Machine Learning model will give you hands-on experience with the process and help you apply the concepts you learn in this course.
Browse courses on
Machine Learning
Show steps
-
Choose a simple dataset and problem statement
-
Research and select a suitable Machine Learning algorithm
-
Train and evaluate the model using the dataset
-
Analyze the results and identify areas for improvement
Analyze and Prepare Datasets for Machine Learning
Show steps
Develop expertise in data analysis and preparation techniques to optimize machine learning models.
Browse courses on
Data Analysis
Show steps
-
Practice exploring and visualizing datasets.
-
Apply data cleaning and transformation techniques to prepare datasets.
-
Evaluate the effectiveness of different data preparation methods.
Explore Advanced Model Training Techniques
Show steps
Enhance your model training skills by exploring advanced techniques and algorithms.
Browse courses on
Model Training
Show steps
-
Research and identify advanced model training techniques.
-
Follow online tutorials or courses to learn these techniques.
-
Apply these techniques to your own machine learning projects.
Contribute to Open-Source Machine Learning Projects
Show steps
Enhance your practical experience and contribute to the machine learning community.
Show steps
-
Identify open-source machine learning projects that align with your interests.
-
Review the project documentation and contribute to discussions or issue tracking.
-
Make code contributions or improvements to the project.
Participate in a Machine Learning Hackathon
Show steps
Challenge yourself in a competitive environment to test your skills and learn from others.
Show steps
-
Identify and register for relevant machine learning hackathons.
-
Form a team or work individually on a project.
-
Develop and submit a machine learning solution within the given time frame.
-
Receive feedback from experts and learn from the experiences of other participants.
Review Machine Learning Concepts
Show steps
Review the fundamental concepts of machine learning, such as supervised learning, unsupervised learning, and model evaluation.
Show steps
Show steps
Show steps
- Review your notes or textbooks.
- Take practice quizzes or complete online exercises.
- Discuss the concepts with a study group or mentor.
Go over Python and Jupyter Notebook
Show steps
This course heavily utilizes Python and Jupyter Notebooks. Reviewing these tools before the course will make learning and completing assignments more accessible.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review the syntax and basics of Python
- Review how to install and use Jupyter Notebook
Review Machine Learning Mathematics
Show steps
Review the mathematical concepts and techniques used in machine learning, such as linear algebra, probability, and calculus.
View
Mathematics for Machine Learning
on Amazon
Show steps
Show steps
Show steps
- Read the book's introduction and first chapter.
- Work through the practice problems in the first chapter.
- Summarize the key concepts covered in the first chapter.
17 other activities
Expand to see all activities and additional details
Show all 20 activities
Organize your materials
Show steps
Keeping your materials organized will help you study more efficiently and prepare for assignments.
Show steps
Show steps
Show steps
- Create folders for notes, assignments, and quizzes
- Create a system for naming and storing files
- Review your syllabus and make a note of important dates
Follow a Machine Learning Tutorial
Show steps
Follow a tutorial that provides a hands-on introduction to machine learning, covering topics such as data preprocessing, model training, and evaluation.
Browse courses on
Machine Learning Basics
Show steps
Show steps
Show steps
- Choose a tutorial that aligns with your learning goals.
- Follow the tutorial step-by-step, completing all exercises.
- Document your progress and any challenges you encounter.
Follow tutorials on Python and Jupyter
Show steps
This course builds on foundational Python, Jupyter, and Machine Learning skills. Following tutorials will help you review the prerequisites and set you up for success.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find tutorials on Python syntax and data structures
- Find tutorials on Jupyter Notebook basics and data manipulation
- Find tutorials on the basics of Machine Learning
- Complete the tutorials and take notes on key concepts
Join a study group
Show steps
Working with peers can help you understand concepts more deeply, prepare for assignments, and stay motivated.
Show steps
Show steps
Show steps
- Find or create a study group with other students in the course
- Meet regularly to discuss course materials, work on assignments, and prepare for exams
- Share notes, resources, and ideas with your group members
- Provide support and encouragement to each other
Solve Python coding challenges
Show steps
Practicing Python coding challenges will improve your problem-solving and coding skills, which are essential for this course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find coding challenges websites or platforms
- Start with easy challenges and gradually increase the difficulty
- Focus on understanding the problem and developing an efficient solution
- Review your solutions and identify areas for improvement
Practice Identifying Problem Formulations
Show steps
Clarify problem formulations to ensure a solid foundation for the machine learning process.
Browse courses on
Problem Formulation
Show steps
Show steps
Show steps
- Review common machine learning problem formulations.
- Examine real-world case studies to identify problem formulations.
- Practice formulating problems in the context of machine learning.
Practice Data Preprocessing Techniques
Show steps
Complete exercises and practice problems on data preprocessing techniques, such as data cleaning, normalization, and feature scaling.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Find a dataset that requires preprocessing.
- Apply different data preprocessing techniques to the dataset.
- Evaluate the effectiveness of each technique.
Analyze a machine learning dataset
Show steps
Analyze a dataset to identify patterns, trends, and relationships that can be used to train machine learning models.
Show steps
Show steps
Show steps
- Explore the dataset using visualization tools.
- Calculate summary statistics and perform data cleaning.
- Identify potential features and target variables.
Participate in a machine learning study group
Show steps
Collaborate with peers to discuss machine learning concepts, troubleshoot problems, and share knowledge.
Show steps
Show steps
Show steps
- Join or create a study group.
- Review course materials and prepare for meetings.
- Actively participate in discussions and Q&A sessions.
Collaborate on a Machine Learning Project
Show steps
Work with peers to apply your knowledge and enhance your problem-solving abilities.
Show steps
Show steps
Show steps
- Form study groups or join online communities related to machine learning.
- Identify and select a project to work on as a team.
- Divide responsibilities and work collaboratively on different aspects of the project.
- Present your project findings and learnings to the group or community.
Develop a Visual Representation of the Machine Learning Workflow
Show steps
Create a visual aid to enhance understanding and retention of the machine learning workflow.
Show steps
Show steps
Show steps
- Summarize the key steps of the machine learning workflow.
- Identify visual elements to represent each step.
- Create a visual representation using a tool like PowerPoint or Draw.io.
Write a blog post on a machine learning algorithm
Show steps
Reinforce your understanding of machine learning algorithms by explaining it to others.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Research and select an algorithm.
- Explain the algorithm's concepts and functionality.
- Provide examples and use cases.
- Proofread and publish the blog post.
Build a simple Machine Learning model
Show steps
Building a simple Machine Learning model will give you hands-on experience with the process and help you apply the concepts you learn in this course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a simple dataset and problem statement
- Research and select a suitable Machine Learning algorithm
- Train and evaluate the model using the dataset
- Analyze the results and identify areas for improvement
Analyze and Prepare Datasets for Machine Learning
Show steps
Develop expertise in data analysis and preparation techniques to optimize machine learning models.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Practice exploring and visualizing datasets.
- Apply data cleaning and transformation techniques to prepare datasets.
- Evaluate the effectiveness of different data preparation methods.
Explore Advanced Model Training Techniques
Show steps
Enhance your model training skills by exploring advanced techniques and algorithms.
Browse courses on
Model Training
Show steps
Show steps
Show steps
- Research and identify advanced model training techniques.
- Follow online tutorials or courses to learn these techniques.
- Apply these techniques to your own machine learning projects.
Contribute to Open-Source Machine Learning Projects
Show steps
Enhance your practical experience and contribute to the machine learning community.
Show steps
Show steps
Show steps
- Identify open-source machine learning projects that align with your interests.
- Review the project documentation and contribute to discussions or issue tracking.
- Make code contributions or improvements to the project.
Participate in a Machine Learning Hackathon
Show steps
Challenge yourself in a competitive environment to test your skills and learn from others.
Show steps
Show steps
Show steps
- Identify and register for relevant machine learning hackathons.
- Form a team or work individually on a project.
- Develop and submit a machine learning solution within the given time frame.
- Receive feedback from experts and learn from the experiences of other participants.
Career center
Learners who complete Follow a Machine Learning Workflow will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst is responsible for collecting, cleaning, and analyzing data to identify trends and patterns. This course can help you develop the skills you need to be a successful Data Analyst, including data collection, data analysis, and data visualization. Additionally, the course provides a foundation in machine learning, which is increasingly being used by Data Analysts to automate tasks and improve the accuracy of their analysis.
Data Scientist
Data Scientist
A Data Scientist is responsible for building and deploying machine learning models. This course can help you develop the skills you need to be a successful Data Scientist, including data analysis, machine learning, and model deployment. Additionally, the course provides a foundation in the machine learning workflow, which is essential for Data Scientists who want to be able to successfully deploy machine learning models into production.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for designing, developing, and deploying machine learning systems. This course can help you develop the skills you need to be a successful Machine Learning Engineer, including data analysis, machine learning, and systems engineering. Additionally, the course provides a foundation in the machine learning workflow, which is essential for Machine Learning Engineers who want to be able to successfully deploy machine learning systems into production.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer is responsible for designing, developing, and deploying artificial intelligence systems. This course can help you develop the skills you need to be a successful Artificial Intelligence Engineer, including data analysis, machine learning, and artificial intelligence. Additionally, the course provides a foundation in the machine learning workflow, which is essential for Artificial Intelligence Engineers who want to be able to successfully deploy artificial intelligence systems into production.
Business Analyst
Business Analyst
A Business Analyst is responsible for analyzing business processes and identifying opportunities for improvement. This course can help you develop the skills you need to be a successful Business Analyst, including data analysis, machine learning, and business process improvement. Additionally, the course provides a foundation in the machine learning workflow, which can be used to automate tasks and improve the accuracy of business analysis.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst is responsible for applying mathematical and analytical techniques to solve business problems. This course can help you develop the skills you need to be a successful Operations Research Analyst, including data analysis, machine learning, and optimization. Additionally, the course provides a foundation in the machine learning workflow, which can be used to automate tasks and improve the efficiency of operations.
Risk Analyst
Risk Analyst
A Risk Analyst is responsible for identifying and assessing risks. This course can help you develop the skills you need to be a successful Risk Analyst, including data analysis, machine learning, and risk assessment. Additionally, the course provides a foundation in the machine learning workflow, which can be used to automate tasks and improve the accuracy of risk assessment.
Fraud Analyst
Fraud Analyst
A Fraud Analyst is responsible for investigating and preventing fraud. This course can help you develop the skills you need to be a successful Fraud Analyst, including data analysis, machine learning, and fraud investigation. Additionally, the course provides a foundation in the machine learning workflow, which can be used to automate tasks and improve the accuracy of fraud detection.
Financial Analyst
Financial Analyst
A Financial Analyst is responsible for analyzing financial data and making investment recommendations. This course can help you develop the skills you need to be a successful Financial Analyst, including data analysis, machine learning, and financial modeling. Additionally, the course provides a foundation in the machine learning workflow, which can be used to automate tasks and improve the accuracy of financial analysis.
Actuary
Actuary
An Actuary is responsible for assessing and managing financial risks. This course can help you develop the skills you need to be a successful Actuary, including data analysis, machine learning, and actuarial science. Additionally, the course provides a foundation in the machine learning workflow, which can be used to automate tasks and improve the accuracy of risk assessment.
Statistician
Statistician
A Statistician is responsible for collecting, analyzing, and interpreting data. This course can help you develop the skills you need to be a successful Statistician, including data analysis, machine learning, and statistical modeling. Additionally, the course provides a foundation in the machine learning workflow, which can be used to automate tasks and improve the accuracy of statistical analysis.
Data Engineer
Data Engineer
A Data Engineer is responsible for designing, building, and maintaining data systems. This course can help you develop the skills you need to be a successful Data Engineer, including data analysis, machine learning, and data engineering. Additionally, the course provides a foundation in the machine learning workflow, which can be used to automate tasks and improve the efficiency of data systems.
Software Engineer
Software Engineer
A Software Engineer is responsible for designing, developing, and testing software applications. This course can help you develop the skills you need to be a successful Software Engineer, including data analysis, machine learning, and software development. Additionally, the course provides a foundation in the machine learning workflow, which can be used to automate tasks and improve the quality of software applications.
Product Manager
Product Manager
A Product Manager is responsible for managing the development and launch of new products. This course can help you develop the skills you need to be a successful Product Manager, including data analysis, machine learning, and product management. Additionally, the course provides a foundation in the machine learning workflow, which can be used to automate tasks and improve the success of new products.
Marketing Manager
Marketing Manager
A Marketing Manager is responsible for developing and implementing marketing campaigns. This course can help you develop the skills you need to be a successful Marketing Manager, including data analysis, machine learning, and marketing. Additionally, the course provides a foundation in the machine learning workflow, which can be used to automate tasks and improve the effectiveness of marketing campaigns.
For more career information including salaries, visit:
OpenCourser.com/course/45zfay/follow
Reading list
We've selected ten 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
Follow a Machine Learning Workflow.
Provides a rigorous treatment of statistical learning methods, including regression, classification, and clustering.
Save
Offers a practical introduction to data mining techniques, including data preprocessing, feature selection, and model evaluation.
Provides a comprehensive overview of machine learning in Python, with a focus on data analysis and modeling.
Provides practical guidance on implementing machine learning models using popular tools and libraries.
Offers a comprehensive treatment of machine learning algorithms, with a focus on statistical methods and pattern recognition.
Save
Explores the practical applications of machine learning in business settings, with a focus on decision-making and problem-solving.
Offers a theoretical foundation for machine learning, covering topics such as probability theory, Bayesian inference, and graphical models.
Demonstrates how to apply machine learning to real-world problems using open-source tools and libraries.
Save
Provides an in-depth exploration of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides an accessible introduction to machine learning concepts and techniques, making it a suitable resource for beginners.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/45zfay/follow
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Rating
4.8
Effort
19 hours to complete
Level
Intermediate
Via
Coursera
Institution
CertNexus
Instructor
Renée Cummings
Language
English
Transcripts
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This course belongs to a
collection
called
CertNexus Certified Artificial Intelligence Practitioner. It's comprised of five courses:
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