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Sherif A. Tawfik Abbas
In this 1-hour long project-based course, you will learn what ensemble learning is and how to implement is using python. You will create deep convolutional neural networks using the Keras library to predict the malaria parasite. You will learn various ways of assessing classification models. You will create an ensemble of deep convolutional neural networks and apply voting in order to combine the best predictions of your models.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/wpvxr3/malaria
What's inside
Syllabus
Project Overview
Ensemble learning lets you achieve higher predictive performance by combining a group of machine learning models and picking the best predictions of the group, or the "ensemble". In this project you will learn what ensemble learning is and how to implement is using python. You will create deep convolutional neural networks using the Keras library to predict the malaria parasite. You will learn various ways of assessing classification models. You will create an ensemble of deep convolutional neural networks and apply voting in order to combine the best predictions of your models.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches deep convolutional neural networks using the Keras library, which is highly relevant to data science
Focuses on practical implementation of ensemble learning, providing hands-on experience
Course duration is only 1 hour, making it suitable for learners with limited time
May require prior knowledge in deep learning and python programming
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Save Malaria parasite detection using ensemble learning in Keras to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Malaria parasite detection using ensemble learning in Keras with these
activities:
Review machine learning fundamentals
Show steps
Refreshes knowledge of machine learning concepts that are foundational to understanding ensemble learning.
Browse courses on
Machine Learning
Show steps
-
Review notes or textbooks on machine learning.
-
Practice solving machine learning problems using supervised learning algorithms.
Review python programming basics
Show steps
Reinforces knowledge of basic Python programming concepts, which are essential for the course.
Browse courses on
Python Programming
Show steps
-
Read a Python tutorial or refresher course.
-
Practice writing simple Python programs.
Follow Keras tutorials on deep learning
Show steps
Provides hands-on experience with the Keras library used in the course.
Browse courses on
Keras
Show steps
-
Find Keras tutorials on building deep convolutional neural networks.
-
Follow the tutorials step-by-step.
-
Experiment with different parameters and architectures.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group to discuss course concepts
Show steps
Fosters collaboration and deepens understanding through peer discussions.
Show steps
-
Find or create a study group with other students taking the course.
-
Meet regularly to discuss course material and share insights.
-
Work together on assignments and projects.
Read 'Ensemble Machine Learning' by Jason Brownlee
Show steps
Provides a comprehensive overview of ensemble learning techniques, complementing the course material.
Show steps
-
Obtain a copy of the book.
-
Read and understand the concepts and algorithms presented in the book.
-
Complete the exercises and examples in the book.
Solve coding challenges related to ensemble learning
Show steps
Enhances understanding of ensemble learning algorithms by applying them to practical problems.
Browse courses on
Ensemble Learning
Show steps
-
Find coding challenges or exercises on ensemble learning.
-
Attempt to solve the challenges using different ensemble methods.
-
Evaluate the performance of your solutions.
Create a blog post or video tutorial on ensemble learning
Show steps
Enhances understanding by requiring the learner to explain and present the concepts of ensemble learning.
Browse courses on
Ensemble Learning
Show steps
-
Choose a specific aspect or application of ensemble learning.
-
Research and gather information on the topic.
-
Write a blog post or create a video tutorial explaining the topic clearly.
Contribute to an open-source project related to ensemble learning
Show steps
Provides practical experience in applying ensemble learning techniques and contributing to a collaborative project.
Browse courses on
Open-Source
Show steps
-
Find an open-source project related to ensemble learning.
-
Identify a bug or feature to work on.
-
Implement the bug fix or feature and submit a pull request.
Review machine learning fundamentals
Show steps
Refreshes knowledge of machine learning concepts that are foundational to understanding ensemble learning.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review notes or textbooks on machine learning.
- Practice solving machine learning problems using supervised learning algorithms.
Review python programming basics
Show steps
Reinforces knowledge of basic Python programming concepts, which are essential for the course.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Read a Python tutorial or refresher course.
- Practice writing simple Python programs.
Follow Keras tutorials on deep learning
Show steps
Provides hands-on experience with the Keras library used in the course.
Browse courses on
Keras
Show steps
Show steps
Show steps
- Find Keras tutorials on building deep convolutional neural networks.
- Follow the tutorials step-by-step.
- Experiment with different parameters and architectures.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group to discuss course concepts
Show steps
Fosters collaboration and deepens understanding through peer discussions.
Show steps
Show steps
Show steps
- Find or create a study group with other students taking the course.
- Meet regularly to discuss course material and share insights.
- Work together on assignments and projects.
Read 'Ensemble Machine Learning' by Jason Brownlee
Show steps
Provides a comprehensive overview of ensemble learning techniques, complementing the course material.
Show steps
Show steps
Show steps
- Obtain a copy of the book.
- Read and understand the concepts and algorithms presented in the book.
- Complete the exercises and examples in the book.
Solve coding challenges related to ensemble learning
Show steps
Enhances understanding of ensemble learning algorithms by applying them to practical problems.
Browse courses on
Ensemble Learning
Show steps
Show steps
Show steps
- Find coding challenges or exercises on ensemble learning.
- Attempt to solve the challenges using different ensemble methods.
- Evaluate the performance of your solutions.
Create a blog post or video tutorial on ensemble learning
Show steps
Enhances understanding by requiring the learner to explain and present the concepts of ensemble learning.
Browse courses on
Ensemble Learning
Show steps
Show steps
Show steps
- Choose a specific aspect or application of ensemble learning.
- Research and gather information on the topic.
- Write a blog post or create a video tutorial explaining the topic clearly.
Contribute to an open-source project related to ensemble learning
Show steps
Provides practical experience in applying ensemble learning techniques and contributing to a collaborative project.
Browse courses on
Open-Source
Show steps
Show steps
Show steps
- Find an open-source project related to ensemble learning.
- Identify a bug or feature to work on.
- Implement the bug fix or feature and submit a pull request.
Career center
Learners who complete Malaria parasite detection using ensemble learning in Keras will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists apply machine learning techniques, including ensemble methods, to solve business problems. This course provides a strong foundation in ensemble learning and Keras, which are essential skills for Data Scientists. The hands-on project experience will give you the practical skills you need to succeed in this role.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. This course will teach you how to use ensemble learning to improve the accuracy and performance of your models. The Keras library is widely used in industry, and this course will give you the skills you need to use it effectively.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including machine learning. This course will provide you with a strong foundation in ensemble learning, which is a powerful technique for improving the accuracy of machine learning models. The hands-on project experience will give you the skills you need to conduct research in this field.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course will teach you how to use ensemble learning to improve the accuracy and performance of your software applications. The Keras library is widely used in industry, and this course will give you the skills you need to use it effectively.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make better decisions. This course will teach you how to use ensemble learning to improve the accuracy and performance of your data analysis models. The Keras library is widely used in industry, and this course will give you the skills you need to use it effectively.
Business Analyst
Business Analyst
Business Analysts help businesses identify and solve problems. This course will teach you how to use ensemble learning to improve the accuracy and performance of your business analysis models. The Keras library is widely used in industry, and this course will give you the skills you need to use it effectively.
Product Manager
Product Manager
Product Managers develop and manage products. This course will teach you how to use ensemble learning to improve the accuracy and performance of your product development models. The Keras library is widely used in industry, and this course will give you the skills you need to use it effectively.
Marketing Manager
Marketing Manager
Marketing Managers develop and execute marketing campaigns. This course will teach you how to use ensemble learning to improve the accuracy and performance of your marketing campaigns. The Keras library is widely used in industry, and this course will give you the skills you need to use it effectively.
Sales Manager
Sales Manager
Sales Managers develop and execute sales strategies. This course will teach you how to use ensemble learning to improve the accuracy and performance of your sales models. The Keras library is widely used in industry, and this course will give you the skills you need to use it effectively.
Customer Success Manager
Customer Success Manager
Customer Success Managers help customers achieve their goals. This course will teach you how to use ensemble learning to improve the accuracy and performance of your customer success models. The Keras library is widely used in industry, and this course will give you the skills you need to use it effectively.
Project Manager
Project Manager
Project Managers plan and execute projects. This course will teach you how to use ensemble learning to improve the accuracy and performance of your project management models. The Keras library is widely used in industry, and this course will give you the skills you need to use it effectively.
Financial Analyst
Financial Analyst
Financial Analysts evaluate investments. This course will teach you how to use ensemble learning to improve the accuracy and performance of your financial analysis models. The Keras library is widely used in industry, and this course will give you the skills you need to use it effectively.
Actuary
Actuary
Actuaries assess risk. This course will teach you how to use ensemble learning to improve the accuracy and performance of your actuarial models. The Keras library is widely used in industry, and this course will give you the skills you need to use it effectively.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. This course will teach you how to use ensemble learning to improve the accuracy and performance of your statistical models. The Keras library is widely used in industry, and this course will give you the skills you need to use it effectively.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes of disease. This course will provide you with a strong foundation in ensemble learning, which is a powerful technique for identifying the factors that contribute to disease. The hands-on project experience will give you the skills you need to conduct research in this field.
For more career information including salaries, visit:
OpenCourser.com/course/wpvxr3/malaria
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
Malaria parasite detection using ensemble learning in Keras.
Provides comprehensive coverage of deep learning concepts and techniques using Python and the Keras library. It is valuable as a reference for implementing the models discussed in the course.
Focuses on practical machine learning techniques using popular Python libraries. It provides detailed explanations and code examples for implementing various models, including deep learning models.
Covers the principles and techniques of deep learning for computer vision tasks. It includes topics such as image classification, object detection, and image segmentation.
Save
Explores the potential of AI in transforming healthcare. It discusses topics such as disease diagnosis, personalized medicine, and remote patient monitoring.
Provides a gentle introduction to machine learning concepts and algorithms. It good starting point for those new to the field.
This classic textbook provides a comprehensive overview of statistical learning methods. It covers topics such as regression, classification, and clustering.
Save
Offers a comprehensive introduction to computer vision algorithms and techniques. It covers topics such as image formation, feature extraction, and object recognition.
Provides a rigorous mathematical treatment of pattern recognition and machine learning methods. It valuable reference for those interested in the theoretical underpinnings of these fields.
Presents the theoretical foundations of statistical learning theory. It valuable resource for those interested in the mathematical underpinnings of machine learning.
Provides a practical introduction to machine learning algorithms using Python. It covers topics such as supervised and unsupervised learning, model selection, and overfitting.
Offers a beginner-friendly introduction to machine learning concepts and algorithms. It good starting point for those with little to no prior knowledge in the field.
Explores the principles of genetic programming, a powerful technique for solving complex problems. It provides a comprehensive overview of the field and its applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/wpvxr3/malaria
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Sherif A. Tawfik Abbas
Language
English
Good to know
Teaches deep convolutional neural networks using the Keras library, which is highly relevant to data science
Focuses on practical implementation of ensemble learning, providing hands-on experience
Course duration is only 1 hour, making it suitable for learners with limited time
May require prior knowledge in deep learning and python programming
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