Follow a Machine Learning Workflow
- Offered byCoursera
Follow a Machine Learning Workflow at Coursera Overview
Duration | 20 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Follow a Machine Learning Workflow at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 2 of 5 in the CertNexus Certified Artificial Intelligence Practitioner
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level General knowledge of AI is required, as is experience with Python or similar languages. Basic knowledge of math and statistics is also recommended.
- Approx. 20 hours to complete
- English Subtitles: English
Follow a Machine Learning Workflow at Coursera Course details
- 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.
Follow a Machine Learning Workflow at Coursera Curriculum
Collect the Dataset
Follow a Machine Learning Workflow Course Introduction
CAIP Specialization Introduction
Collect the Dataset Module Introduction
Machine Learning Datasets
Data Structure Terminology
Data Quality Issues
Data Sources
Guidelines for Selecting a Machine Learning Dataset
ETL and Machine Learning Pipelines
Overview
Open Datasets
Guidelines for Loading a Dataset
Open Datasets Quiz
Collecting the Dataset
Analyze the Dataset
Analyze the Dataset Module Introduction
Dataset Content and Format
Distributions
Descriptive Statistical Analysis
Central Tendency
Variability and Range
Variance and Standard Deviation
Skewness
Kurtosis
Correlation Coefficient
Visualizations
Histogram
Box Plot
Scatterplot
Maps
Overview
Guidelines for Exploring the Structure of a Dataset
Statistical Moments
Guidelines for Analyzing a Dataset
Guidelines for Using Visualizations to Analyze Data
Analyzing the Dataset
Prepare the Dataset
Prepare the Dataset Module Introduction
Data Preparation
Data Types
Continuous vs. Discrete Variables
Data Encoding
Dimensionality Reduction
Missing and Duplicate Values
Normalization and Standardization
Holdout Method
Overview
Operations You Can Perform on Different Types of Data
Summarization
Guidelines for Preparing Training and Testing Data
Data Types Quiz
Preparing the Dataset
Set Up and Train a Model
Set Up and Train a Model Module Introduction
Design of Experiments
Hypothesis Testing
p-value and Confidence Interval
Machine Learning Algorithms
Iterative Tuning
Bias and Generalizations
Cross-Validation
Feature Transformation
The Bias?Variance Tradeoff
Parameters
Regularization
Training Efficiency
Overview
Guidelines for Setting Up a Machine Learning Model
Guidelines for Training and Tuning the Model
Setting Up and Training the Model
Finalize the Model
Finalize the Model Module Introduction
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
Overview
Consumer-Oriented Applications
Guidelines for Incorporating Machine Learning into a Long-Term Solution
Finalizing a Model
Apply What You've Learned
Follow a Machine Learning Workflow at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Student Forum
Anything you would want to ask experts?
Data ScienceMachine LearningMachine Learning and Pattern RecognitionFollow a Machine Learning Workflow