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Follow a Machine Learning Workflow
  • Level Professional
  • Duration 20 hours
  • Course by CertNexus
  • Offered by

About

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.

Modules

Overview

    • 3 Videos
    • 2 Readings
Show info about module content

3 Videos

  • Follow a Machine Learning Workflow Course Introduction
  • CAIP Specialization Introduction
  • Collect the Dataset Module Introduction

2 Readings

  • Overview
  • Get help and meet other learners. Join your Community!

Data Collection

    • 6 Videos
    • 2 Readings
    • 2 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Open Datasets Quiz

2 Labs

  • Examining the Structure of a Machine Learning Dataset
  • Loading the Dataset

6 Videos

  • Machine Learning Datasets
  • Data Structure Terminology
  • Data Quality Issues
  • Data Sources
  • Guidelines for Selecting a Machine Learning Dataset
  • ETL and Machine Learning Pipelines

2 Readings

  • Open Datasets
  • Guidelines for Loading a Dataset

Evaluate What You've Learned

    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Collecting the Dataset

1 Discussions

  • Reflect on What You've Learned

Overview

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Analyze the Dataset Module Introduction

1 Readings

  • Overview

Statistical Analysis

    • 9 Videos
    • 3 Readings
    • 2 Lab
Show info about module content

2 Labs

  • Exploring the General Structure of the Dataset
  • Analyzing a Dataset Using Statistical Measures

9 Videos

  • Dataset Content and Format
  • Distributions
  • Descriptive Statistical Analysis
  • Central Tendency
  • Variability and Range
  • Variance and Standard Deviation
  • Skewness
  • Kurtosis
  • Correlation Coefficient

3 Readings

  • Guidelines for Exploring the Structure of a Dataset
  • Statistical Moments
  • Guidelines for Analyzing a Dataset

Visual Analysis

    • 5 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Analyzing a Dataset Using Visualizations

5 Videos

  • Visualizations
  • Histogram
  • Box Plot
  • Scatterplot
  • Maps

1 Readings

  • Guidelines for Using Visualizations to Analyze Data

Evaluate What You've Learned

    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Analyzing the Dataset

1 Discussions

  • Reflect on What You've Learned

Overview

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Prepare the Dataset Module Introduction

1 Readings

  • Overview

Data Preparation

    • 8 Videos
    • 3 Readings
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Data Types Quiz

1 Labs

  • Splitting the Training and Testing Datasets and Labels

8 Videos

  • Data Preparation
  • Data Types
  • Continuous vs. Discrete Variables
  • Data Encoding
  • Dimensionality Reduction
  • Missing and Duplicate Values
  • Normalization and Standardization
  • Holdout Method

3 Readings

  • Operations You Can Perform on Different Types of Data
  • Summarization
  • Guidelines for Preparing Training and Testing Data

Evaluate What You've Learned

    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Preparing the Dataset

1 Discussions

  • Reflect on What You've Learned

Overview

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Set Up and Train a Model Module Introduction

1 Readings

  • Overview

Set Up a Machine Learning Model

    • 4 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Setting Up a Machine Learning Model

4 Videos

  • Design of Experiments
  • Hypothesis Testing
  • p-value and Confidence Interval
  • Machine Learning Algorithms

1 Readings

  • Guidelines for Setting Up a Machine Learning Model

Train the Model

    • 8 Videos
    • 1 Readings
    • 3 Lab
Show info about module content

3 Labs

  • Dealing with Outliers
  • Scaling and Normalizing Features
  • Refitting and Testing the Model

8 Videos

  • Iterative Tuning
  • Bias and Generalizations
  • Cross-Validation
  • Feature Transformation
  • The Bias–Variance Tradeoff
  • Parameters
  • Regularization
  • Training Efficiency

1 Readings

  • Guidelines for Training and Tuning the Model

Evaluate What You've Learned

    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Setting Up and Training the Model

1 Discussions

  • Reflect on What You've Learned

Overview

    • 1 Videos
    • 1 Readings
Show info about module content

1 Videos

  • Finalize the Model Module Introduction

1 Readings

  • Overview

Model Finalization

    • 7 Videos
    • 2 Readings
    • 2 PeerReview
Show info about module content

2 Peer Review

  • Translating Results into Business Actions
  • Incorporating a Model into a Long-Term Solution

7 Videos

  • Know Your Audience
  • Use Visualization to Present Your Findings
  • Put Together a Machine Learning Presentation
  • Communicate Your Findings Clearly
  • Put a Model into Production
  • Pipeline Automation
  • Testing and Maintenance

2 Readings

  • Consumer-Oriented Applications
  • Guidelines for Incorporating Machine Learning into a Long-Term Solution

Evaluate What You've Learned

    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Finalizing a Model

1 Discussions

  • Reflect on What You've Learned

Project

    • 1 PeerReview
    • 1 Lab
Show info about module content

1 Peer Review

  • Following a Machine Learning Workflow to Predict Demand for Bicycle Rentals

1 Labs

  • Course 2 Project

Auto Summary

Explore the comprehensive machine learning workflow with this professional-level course in Data Science & AI, guided by Coursera. Delve into each crucial step from problem formulation to model deployment, with a focus on data analysis and model training. The 1200-minute course is part of the Certified Artificial Intelligence Practitioner (CAIP) series and offers Starter and Professional subscription options. Ideal for professionals looking to deepen their ML expertise and automate key tasks for recurring business processes.

Renée Cummings
Instructor

Renée Cummings