Machine Learning for Research
Machine Learning for Research, available at Free, has an average rating of 4.4, with 29 lectures, based on 23 reviews, and has 1419 subscribers.
You will learn about Learn when it is appropriate to apply machine learning to scientific research Learn how supervised and unsupervised machine learning algorithms work How to take raw data and apply machine learning to develop conclusions How to conduct your own research project using machine learning This course is ideal for individuals who are Any motivated individual looking to empower themselves with the knowledge of machine learning and apply it to their own research work at no added cost It is particularly useful for Any motivated individual looking to empower themselves with the knowledge of machine learning and apply it to their own research work at no added cost.
Enroll now: Machine Learning for Research
Summary
Title: Machine Learning for Research
Price: Free
Average Rating: 4.4
Number of Lectures: 29
Number of Published Lectures: 29
Number of Curriculum Items: 29
Number of Published Curriculum Objects: 29
Original Price: Free
Quality Status: approved
Status: Live
What You Will Learn
- Learn when it is appropriate to apply machine learning to scientific research
- Learn how supervised and unsupervised machine learning algorithms work
- How to take raw data and apply machine learning to develop conclusions
- How to conduct your own research project using machine learning
Who Should Attend
- Any motivated individual looking to empower themselves with the knowledge of machine learning and apply it to their own research work at no added cost
Target Audiences
- Any motivated individual looking to empower themselves with the knowledge of machine learning and apply it to their own research work at no added cost
Get introduced to machine learning and become equipped with the knowledge of applying machine learning to your research for FREEfrom the comfort of your home.
Get access to valuable tutorials and lectures that will make you comfortable using state-of-the-art machine-learning techniques for your research. You will learn:
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Why ML is used, and where to apply ML to scientific research?
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Preprocessing Techniques: Data Augmentation, SMOTe, PCA
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Supervised Machine Learning: Convolutional Neural Networks
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Unsupervised Machine Learning: The Louvain Method
This course is for anyone regardless of experience with machine learning. In fact, you will learn the basics of applying machine learning to research from scratch.
The course begins by giving you an overview of where exactly machine learning can be applied to the scientific process. The course teaches you not only why machine learning is such a powerful tool, but also in what instances it is appropriate (and also when it is not appropriate) to apply machine learning to scientific research.
Armed with this knowledge, you will explore multiple data preprocessing techniques that are crucial for applying machine learning to your research. With these mastered, you will delve straight into applying machine learning techniques to effectively generate results for your research work, with interactive tutorials along the way. Jupyter Notebooks with detailed comments are available for every tutorial.
This course is sprinkled with advice on how to get the best results when applying machine learning. Throughout the course, you will see first-hand how to apply the knowledge you learned to real-world data to solve research problems such as early cancer detection, and analyzing gene expression data in patients with life-threatening ailments. Furthermore, you will get a primer on important topics such as finding quality datasets for your research and tips for sharing your work.
Upon completing this course, you will have the knowledge to effectively apply machine learning to your own research projects. You will also have a intuitive understanding of how your machine learning algorithms work, not only making your research more robust, but also easily interpretable for a general audience.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: Course Outline
Lecture 3: Section 1 Recap
Chapter 2: Machine Learning in Research
Lecture 1: The Scientific Process
Lecture 2: Why Use Machine Learning?
Lecture 3: Machine Learning in Action
Lecture 4: When to Use Machine Learning?
Lecture 5: Section 2 Recap
Chapter 3: Python Review
Lecture 1: Downloading Jupyter
Lecture 2: Python Review
Chapter 4: Preprocessing Data
Lecture 1: Introduction
Lecture 2: Data Augmentation
Lecture 3: Scenario #1
Lecture 4: SMOTe
Lecture 5: Scenario #2
Lecture 6: PCA
Lecture 7: Scenario #3
Lecture 8: Section 4 Recap
Chapter 5: Supervised Machine Learning
Lecture 1: CNNs in Research
Lecture 2: How do CNNs Work?
Lecture 3: Scenario #4 (Part 1)
Lecture 4: Scenario #4 (Part 2)
Lecture 5: Section 5 Recap
Chapter 6: Unsupervised Machine Learning
Lecture 1: The Louvain Method
Lecture 2: Scenario #5
Lecture 3: Section 6 Recap
Chapter 7: Conclusion
Lecture 1: Next Steps
Lecture 2: Course Conclusion
Lecture 3: Acknowledgements
Instructors
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Kush Parikh
Instructor at Udemy
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 2 votes
- 4 stars: 7 votes
- 5 stars: 14 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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