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AWS AutoGluon for Machine Learning Classification
Ryan Ahmed
Ryan Ahmed
Hello everyone and welcome to this new hands-on project on ML classification with AWS AutoGluon.
In this project, we will train several machine learning classifiers to detect and classify disease using a super powerful library known as AutoGluon.
Read more
Hello everyone and welcome to this new hands-on project on ML classification with AWS AutoGluon.
In this project, we will train several machine learning classifiers to detect and classify disease using a super powerful library known as AutoGluon.
Read more
Hello everyone and welcome to this new hands-on project on ML classification with AWS AutoGluon.
In this project, we will train several machine learning classifiers to detect and classify disease using a super powerful library known as AutoGluon.
AutoGluon is the library behind Amazon Web Services (AWS) autopilot and it allows for quick prototyping of several powerful models using a few lines of code.
Register for this course and see more details by visiting:
OpenCourser.com/course/tsd20h/aws
What's inside
Syllabus
Project Overview
In this new hands-on guided project, we will learn how to train several machine learning classifier models to detect and classify disease using a powerful library known as AutoGluon. AutoGluon is the library behind Amazon Web Services (AWS) autopilot, and it allows for quick prototyping of several powerful models using a few lines of code.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for learners with no background in autogluon or ML
Leverages industry-leading AWS Autogluon library for hands-on experience
Develops skills in disease detection and classification through practical examples
Taught by experienced instructors with expertise in the field
Introduces the cutting-edge Autogluon library for automated machine learning tasks
Provides a comprehensive overview of ML classification techniques for disease analysis
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Activities
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We're preparing activities for
AWS AutoGluon for Machine Learning Classification.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete AWS AutoGluon for Machine Learning Classification will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models to solve various business challenges. Familiarity with machine learning classification is crucial for building effective models, such as identifying fraud or classifying images. The AWS AutoGluon for Machine Learning Classification course equips you with practical skills to develop and evaluate classification models, preparing you to excel in this rapidly growing field.
Data Scientist
Data Scientist
Data Scientists gather, analyze, and interpret data to develop predictive models and make informed decisions to solve business problems. As a Data Scientist, knowledge of machine learning classification will be useful for building accurate and efficient models to identify patterns and trends in data. The AWS AutoGluon for Machine Learning Classification course provides hands-on experience in training various classification models, preparing you to make impactful contributions in data science and analytics.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to provide insights and make recommendations to organizations. Knowledge of machine learning classification is beneficial for automating data analysis tasks, such as classifying customer segments or predicting customer churn. The AWS AutoGluon for Machine Learning Classification course offers a solid foundation in building classification models, enabling you to extract valuable insights from complex data.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data analysis to identify business opportunities, make recommendations, and support decision-making. Machine learning classification is becoming increasingly important for automating data analysis and extracting actionable insights. The AWS AutoGluon for Machine Learning Classification course provides you with practical experience in building classification models, equipping you to drive data-driven decision-making in business settings.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. Machine learning classification is gaining traction in this field, as it allows for the development of more sophisticated and accurate models. The AWS AutoGluon for Machine Learning Classification course provides you with the necessary skills to apply machine learning techniques to financial data, preparing you for a successful career in quantitative finance.
Research Scientist
Research Scientist
Research Scientists conduct scientific research to advance knowledge in various fields. Machine learning classification plays a significant role in data-driven research, enabling the development of models for image recognition, natural language processing, and other complex tasks. The AWS AutoGluon for Machine Learning Classification course provides you with a strong foundation in this area, empowering you to contribute to cutting-edge research projects.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. Knowledge of machine learning classification is becoming increasingly important for building intelligent and autonomous software systems. The AWS AutoGluon for Machine Learning Classification course offers practical experience in developing classification models, preparing you to integrate machine learning capabilities into your software solutions.
Product Manager
Product Manager
Product Managers are responsible for planning, developing, and launching products that meet customer needs. Machine learning classification is becoming essential for personalized product recommendations, spam detection, and other product-related tasks. The AWS AutoGluon for Machine Learning Classification course equips you with the knowledge to understand and apply machine learning in product development, helping you drive innovation and create successful products.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines to ensure data quality and accessibility. Machine learning classification is becoming increasingly important for automating data cleaning, feature engineering, and other data engineering tasks. The AWS AutoGluon for Machine Learning Classification course provides you with practical experience in building classification models, preparing you to contribute to the development of robust and efficient data pipelines.
DevOps Engineer
DevOps Engineer
DevOps Engineers bridge the gap between development and operations teams to ensure seamless delivery of software systems. Machine learning classification is finding applications in DevOps, such as automating anomaly detection and monitoring system performance. The AWS AutoGluon for Machine Learning Classification course provides you with the foundational knowledge to understand and apply machine learning in DevOps practices, helping you contribute to efficient and reliable software delivery processes.
Technical Writer
Technical Writer
Technical Writers create documentation and other materials to explain technical concepts and products. Knowledge of machine learning classification can help you write clear and accurate documentation for machine learning systems. The AWS AutoGluon for Machine Learning Classification course provides you with a practical understanding of this field, enabling you to effectively communicate complex technical information to a variety of audiences.
Sales Engineer
Sales Engineer
Sales Engineers work with customers to understand their needs and provide technical solutions. Knowledge of machine learning classification can help you better explain how machine learning can solve business problems. The AWS AutoGluon for Machine Learning Classification course provides you with the foundational knowledge to effectively communicate the benefits and applications of machine learning to customers, helping you drive sales and build strong customer relationships.
Marketing Analyst
Marketing Analyst
Marketing Analysts gather and analyze data to understand customer behavior and market trends. Machine learning classification is gaining traction in marketing for tasks such as customer segmentation, lead scoring, and campaign optimization. The AWS AutoGluon for Machine Learning Classification course provides you with the practical experience to apply machine learning techniques to marketing data, helping you gain deeper insights and drive more effective marketing campaigns.
Risk Analyst
Risk Analyst
Risk Analysts assess and manage risks in various industries. Machine learning classification is becoming increasingly important for tasks such as fraud detection, credit scoring, and cyber threat analysis. The AWS AutoGluon for Machine Learning Classification course provides you with the foundational knowledge to build and evaluate classification models, preparing you to contribute to risk assessment and mitigation efforts in your field.
Consultant
Consultant
Consultants provide expert advice and guidance to organizations. Knowledge of machine learning classification can enhance your ability to provide solutions for business problems that involve data analysis. The AWS AutoGluon for Machine Learning Classification course equips you with practical knowledge and experience in building classification models, enabling you to offer valuable insights and recommendations to your clients.
For more career information including salaries, visit:
OpenCourser.com/course/tsd20h/aws
Reading list
We've selected 12 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
AWS AutoGluon for Machine Learning Classification.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers the fundamental concepts and techniques used in machine learning, with a focus on probabilistic models and Bayesian inference. It valuable resource for those seeking a deeper understanding of the theoretical foundations of machine learning.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and graphical models. It valuable resource for those seeking a deep understanding of the field.
Provides a comprehensive overview of sparse statistical learning methods, including the Lasso and its generalizations. It covers the theoretical foundations and practical applications of these methods, making it a valuable resource for researchers and practitioners working in high-dimensional data analysis.
Provides a comprehensive overview of reinforcement learning. It covers a wide range of topics, including value functions, policy gradients, and deep reinforcement learning. It valuable resource for those seeking a deep understanding of the field.
Provides a comprehensive overview of statistical learning methods. It covers a wide range of topics, including supervised learning, unsupervised learning, and resampling methods. It valuable resource for those seeking a broad understanding of statistical learning.
Provides a comprehensive overview of deep learning for natural language processing. It covers a wide range of topics, including word embeddings, sequence models, and attention mechanisms. It valuable resource for those seeking a deeper understanding of the field.
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Provides a comprehensive overview of computer vision. It covers a wide range of topics, including image formation, feature extraction, and object recognition. It valuable resource for those seeking a deep understanding of the field.
Offers a comprehensive guide to machine learning using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It provides hands-on examples and exercises to help learners apply machine learning techniques to real-world problems.
Provides a practical introduction to machine learning for those with a programming background. It covers a wide range of topics, including supervised learning, unsupervised learning, and natural language processing. It valuable resource for those looking to quickly gain a working knowledge of machine learning.
Provides a practical introduction to deep learning using Python. It covers the fundamental concepts, architectures, and algorithms used in deep learning, making it a valuable resource for beginners in the field.
Provides a concise overview of machine learning algorithms. It covers the fundamental concepts and techniques used in machine learning, making it a valuable resource for beginners looking to gain a solid foundation in the field.
Provides a gentle introduction to machine learning for those with no prior experience in the field. It covers the basic concepts and algorithms in a clear and accessible manner, making it a great starting point for beginners.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/tsd20h/aws
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Suitable for learners with no background in autogluon or ML
Leverages industry-leading AWS Autogluon library for hands-on experience
Develops skills in disease detection and classification through practical examples
Taught by experienced instructors with expertise in the field
Introduces the cutting-edge Autogluon library for automated machine learning tasks
Provides a comprehensive overview of ML classification techniques for disease analysis
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