Follow a Machine Learning Workflow
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Follow a Machine Learning Workflow
This course is part of CertNexus Certified Artificial Intelligence Practitioner Professional Certificate
Instructor: Stacey McBrine
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What you'll learn
Collect and prepare a dataset to use for training and testing a machine learning model.
Analyze a dataset to gain insights.
Set up and train a machine learning model as needed to meet business requirements.
Communicate the findings of a machine learning project back to the organization.
Skills you'll gain
Tools you'll learn
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See how employees at top companies are mastering in-demand skills
Build your Machine Learning expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from CertNexus
There are 6 modules in this course
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.
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.
What's included
9 videos4 readings2 assignments1 discussion prompt2 ungraded labs
9 videos•Total 31 minutes
- Follow a Machine Learning Workflow Course Introduction•2 minutes
- CAIP Specialization Introduction•4 minutes
- Collect the Dataset Module Introduction•1 minute
- Machine Learning Datasets•3 minutes
- Data Structure Terminology•4 minutes
- Data Quality Issues•6 minutes
- Data Sources•3 minutes
- Guidelines for Selecting a Machine Learning Dataset•4 minutes
- ETL and Machine Learning Pipelines•4 minutes
4 readings•Total 27 minutes
- Overview•2 minutes
- Get help and meet other learners. Join your Community!•5 minutes
- Open Datasets•15 minutes
- Guidelines for Loading a Dataset•5 minutes
2 assignments•Total 35 minutes
- Open Datasets Quiz•5 minutes
- Collecting the Dataset•30 minutes
1 discussion prompt•Total 5 minutes
- Reflect on What You've Learned•5 minutes
2 ungraded labs•Total 50 minutes
- Examining the Structure of a Machine Learning Dataset•20 minutes
- Loading the Dataset•30 minutes
You've formulated a machine learning problem, and have identified a potential dataset to use. Now you'll analyze the dataset to develop ideas on how to make the best use of the information it contains as you prepare to create your initial machine learning model.
What's included
15 videos5 readings1 assignment1 discussion prompt3 ungraded labs
15 videos•Total 38 minutes
- Analyze the Dataset Module Introduction•1 minute
- Dataset Content and Format•2 minutes
- Distributions•2 minutes
- Descriptive Statistical Analysis•1 minute
- Central Tendency•3 minutes
- Variability and Range•3 minutes
- Variance and Standard Deviation•7 minutes
- Skewness•2 minutes
- Kurtosis•4 minutes
- Correlation Coefficient•3 minutes
- Visualizations•2 minutes
- Histogram•1 minute
- Box Plot•2 minutes
- Scatterplot•2 minutes
- Maps•3 minutes
5 readings•Total 24 minutes
- Overview•2 minutes
- Guidelines for Exploring the Structure of a Dataset•5 minutes
- Statistical Moments•2 minutes
- Guidelines for Analyzing a Dataset•10 minutes
- Guidelines for Using Visualizations to Analyze Data•5 minutes
1 assignment•Total 30 minutes
- Analyzing the Dataset•30 minutes
1 discussion prompt•Total 5 minutes
- Reflect on What You've Learned•5 minutes
3 ungraded labs•Total 115 minutes
- Exploring the General Structure of the Dataset•25 minutes
- Analyzing a Dataset Using Statistical Measures•30 minutes
- Analyzing a Dataset Using Visualizations•60 minutes
Before a dataset can be used with a machine learning model, there are typically various tasks you need to perform to ensure that data is an optimal state. In this module, you'll use various methods to prepare the data.
What's included
9 videos4 readings2 assignments1 discussion prompt1 ungraded lab
9 videos•Total 24 minutes
- Prepare the Dataset Module Introduction•2 minutes
- Data Preparation•3 minutes
- Data Types•2 minutes
- Continuous vs. Discrete Variables•2 minutes
- Data Encoding•4 minutes
- Dimensionality Reduction•3 minutes
- Missing and Duplicate Values•4 minutes
- Normalization and Standardization•2 minutes
- Holdout Method•2 minutes
4 readings•Total 17 minutes
- Overview•2 minutes
- Operations You Can Perform on Different Types of Data•10 minutes
- Summarization•2 minutes
- Guidelines for Preparing Training and Testing Data•3 minutes
2 assignments•Total 35 minutes
- Data Types Quiz•5 minutes
- Preparing the Dataset•30 minutes
1 discussion prompt•Total 10 minutes
- Reflect on What You've Learned•10 minutes
1 ungraded lab•Total 30 minutes
- Splitting the Training and Testing Datasets and Labels•30 minutes
To set up a machine learning model in an environment like Python, you must determine the algorithm that will produce the results you're after, and then use it to create a model based on your training data. After the initial setup, it may take multiple tests and refinements to produce a model that meets your requirements.
What's included
13 videos3 readings1 assignment1 discussion prompt4 ungraded labs
13 videos•Total 43 minutes
- Set Up and Train a Model Module Introduction•1 minute
- Design of Experiments•3 minutes
- Hypothesis Testing•9 minutes
- p-value and Confidence Interval•4 minutes
- Machine Learning Algorithms•2 minutes
- Iterative Tuning•1 minute
- Bias and Generalizations•6 minutes
- Cross-Validation•4 minutes
- Feature Transformation•4 minutes
- The Bias–Variance Tradeoff•3 minutes
- Parameters•2 minutes
- Regularization•2 minutes
- Training Efficiency•3 minutes
3 readings•Total 7 minutes
- Overview•2 minutes
- Guidelines for Setting Up a Machine Learning Model•2 minutes
- Guidelines for Training and Tuning the Model•3 minutes
1 assignment•Total 30 minutes
- Setting Up and Training the Model•30 minutes
1 discussion prompt•Total 5 minutes
- Reflect on What You've Learned•5 minutes
4 ungraded labs•Total 165 minutes
- Setting Up a Machine Learning Model•45 minutes
- Dealing with Outliers•30 minutes
- Scaling and Normalizing Features•30 minutes
- Refitting and Testing the Model•60 minutes
Now that you've finished training and tuning a machine learning model, you can turn your attention to deploying it. This may amount to producing a report based on your findings, or it may be much more involved, particularly if it will be incorporated into repeatable processes or become part of a software solution. In either case, finalization is the crucial conclusion to the machine learning workflow.
What's included
8 videos3 readings1 assignment2 peer reviews1 discussion prompt
8 videos•Total 18 minutes
- Finalize the Model Module Introduction•1 minute
- Know Your Audience•1 minute
- Use Visualization to Present Your Findings•1 minute
- Put Together a Machine Learning Presentation•3 minutes
- Communicate Your Findings Clearly•5 minutes
- Put a Model into Production•3 minutes
- Pipeline Automation•1 minute
- Testing and Maintenance•2 minutes
3 readings•Total 9 minutes
- Overview•2 minutes
- Consumer-Oriented Applications•2 minutes
- Guidelines for Incorporating Machine Learning into a Long-Term Solution•5 minutes
1 assignment•Total 30 minutes
- Finalizing a Model•30 minutes
2 peer reviews•Total 40 minutes
- Translating Results into Business Actions•20 minutes
- Incorporating a Model into a Long-Term Solution•20 minutes
1 discussion prompt•Total 5 minutes
- Reflect on What You've Learned•5 minutes
You'll work on a project in which you'll apply your knowledge of the material in this course to a practical scenario.
What's included
1 peer review1 ungraded lab
1 peer review•Total 360 minutes
- Following a Machine Learning Workflow to Predict Demand for Bicycle Rentals•360 minutes
1 ungraded lab•Total 10 minutes
- Course 2 Project•10 minutes
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Reviewed on Aug 31, 2023
Great course and content. Useful information I can apply to future machine learning workflows.
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