Machine Learning in Genomics
Course highlight title
- Analyse whole genome sequence data
- Determine the prevalence of antimicrobial resistance
- Track pathogen evolution
- Epidemiological Reporting
Price: $ 50 $ 20
Our alumni work at
“
I had a great learning experience and importantly, I was super excited today because I got the opportunity to put my learning to use and I was able to do just that!
Aanuoluwa E.A.
Now in University of Oklahoma
About the Course
About the Course
"Machine Learning in Genomics" is designed to equip students with essential skills to apply machine learning techniques in biological research. The course covers the basics of machine learning, from data preprocessing and cleaning to building and training advanced models for classifying biological data such as genomics and methylation profiles. Students will also learn how to interpret model outputs, gaining the ability to extract valuable insights from complex data. By the end of the course, participants will be prepared to tackle real-world genomic challenges using machine learning.
Course Syllabus
Welcome to HackBio
- Welcome to HackBio
- Testing the Platform
- Getting Help
Machine Learning in Omics
- What is Machine Learning
- Core Concepts to Know in ML
- How is ML relevant for Biology and healthcare
- Introduction to Machine Learning in Omics
- Why Machine Learning
- How Humans Learn
- ML001
How Machines Learn!
- Machines (Computer) can also learn 😀
- How Machines Learn
- ML Tasks we will focus on in this course
- Primer on Distances
- Distance in Action!
- ML002: Test
The Machine Learning Pipeline (Hands On)
- The Machine Learning Pipeline
- Dataset
- Let's Preview our Data Quickly
Data Preprocessing and Processing
- Section Overview
- Importing the Dataset Properly
- Data Preprocessing 1
- Adding Metadata Information
- Data Preprocessing 2
- ML003
Training your Model!
- Splitting into Training and Testing Sets
- Finally! Train the Model
- ML004
Testing and Interpretation
- Explaining the ML model
- So what have we done?
- Which of the features (genes) helped our prediction better
- Task
Classification with Random Forest
- From Trees to Forest
- Throw your data into a Random Forest for Classification
- Task
Final Projects
- Classifying Cancer Subtypes Using Methylation Data
Technologies you will use
Machine Learning in Genomics
100% Practice Oriented
Mentorship
Immediately applicable skills
High quality portfolio projects
Price: $ 50 $ 20
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Prerequisites
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Real world projects
From industry and academia
Self-paced, flexible learning
Control your learning time and pace
Support for computing
You don't have to buy a new computer to learn
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