Analyze Datasets and Train ML Models using AutoML
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Course Summary
Learn how to use AutoML to train machine learning models without the need for extensive coding experience. This course covers various datasets and models that can be used for different purposes.Key Learning Points
- Discover how to use AutoML tools to create machine learning models quickly and easily.
- Explore different datasets and models that can be used for various applications.
- Learn how to interpret and analyze the results of your machine learning models.
Job Positions & Salaries of people who have taken this course might have
- Data Scientist
- USA: $113,000
- India: ₹1,200,000
- Spain: €45,000
- Machine Learning Engineer
- USA: $125,000
- India: ₹1,500,000
- Spain: €55,000
- AI Consultant
- USA: $150,000
- India: ₹2,000,000
- Spain: €70,000
Related Topics for further study
Learning Outcomes
- Ability to create machine learning models using AutoML tools
- Skills in interpreting and analyzing machine learning model results
- Knowledge of different datasets and models for various applications
Prerequisites or good to have knowledge before taking this course
- Basic understanding of machine learning concepts
- Familiarity with Python programming language
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
- Hands-on projects
Similar Courses
- Applied Data Science with Python
- Machine Learning
- Data Analysis with Python
Related Education Paths
Related Books
Description
In the first course of the Practical Data Science Specialization, you will learn foundational concepts for exploratory data analysis (EDA), automated machine learning (AutoML), and text classification algorithms. With Amazon SageMaker Clarify and Amazon SageMaker Data Wrangler, you will analyze a dataset for statistical bias, transform the dataset into machine-readable features, and select the most important features to train a multi-class text classifier. You will then perform automated machine learning (AutoML) to automatically train, tune, and deploy the best text-classification algorithm for the given dataset using Amazon SageMaker Autopilot. Next, you will work with Amazon SageMaker BlazingText, a highly optimized and scalable implementation of the popular FastText algorithm, to train a text classifier with very little code.
Knowledge
- Prepare data, detect statistical data biases, and perform feature engineering at scale to train models with pre-built algorithms.
Outline
- Week 1: Explore the Use Case and Analyze the Dataset
- Specialization overview
- Welcome
- Practical Data Science
- Use case and data set
- Data ingestion and exploration
- Data visualization
- Week 1 summary
- Additional reading material
- Have questions? Meet us on Discourse!
- Week 1
- Week 2: Data Bias and Feature Importance
- Introduction
- Statistical bias
- Statistical bias causes
- Measuring statistical bias
- Detecting statistical bias
- Detect statistical bias with Amazon SageMaker Clarify
- Approaches to statistical bias detection
- Feature importance: SHAP
- Summary
- Additional reading material
- Week 2
- Week 3: Use Automated Machine Learning to train a Text Classifier
- Introduction
- Automated Machine Learning (AutoML)
- AutoML Workflow
- Amazon SageMaker Autopilot
- Running experiments with Amazon SageMaker Autopilot
- Amazon SageMaker Autopilot: evaluating output
- Amazon SageMaker Autopilot demo
- Model hosting
- Week 3 summary
- Additional reading material
- Week 3
- Week 4: Built-in algorithms
- Introduction
- Built in algorithms
- Use cases and algorithms
- Text analysis
- Train a text classifier
- Deploy the text classifier
- Week 4 summary
- Additional reading material
- Course 1 Optional References
- Acknowledgements
- Week 4
Summary of User Reviews
The Automl Datasets and Machine Learning Models course on Coursera has received positive reviews from many users. The course teaches students how to build and deploy machine learning models using AutoML technology. One key aspect that many users found beneficial is that the course is well-structured and easy to follow.Pros from User Reviews
- Well-structured and easy to follow
- Great for beginners to learn AutoML technology
- Instructor explains concepts clearly
- Hands-on projects are useful for practical learning
Cons from User Reviews
- Some users found the course too basic
- Not enough depth on certain topics
- Some technical difficulties with the platform
Antje Barth
4.6 Raiting
Managing Machine Learning Projects with Google Cloud
- 4.6
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Business professionals in non-technical roles have a unique opportunity to lead or influence machine learning projects. Module 1: Introduction Introduction How to download course resources...
Analyze Datasets and Train ML Models using AutoML
- 4.6
-
![]()
![]()
![]()
![]()
![]()
In the first course of the Practical Data Science Specialization, you will learn foundational concepts for exploratory data analysis (EDA), automated machine learning (AutoML), and text classification algorithms....
