Follow a Machine Learning Workflow

This course is part of CertNexus Certified Artificial Intelligence Practitioner Professional Certificate

Instructor: Stacey McBrine

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3,473 already enrolled

6 modules

Gain insight into a topic and learn the fundamentals.

19 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

19 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

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

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

7 assignments¹

AI Graded see disclaimer

Taught in English

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Build your Machine Learning expertise

This course is part of the CertNexus Certified Artificial Intelligence Practitioner Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

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

Collecting the Dataset 30 minutes
Open Datasets Quiz 5 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

Preparing the Dataset 30 minutes
Data Types Quiz 5 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.