Machine Learning for Genomic Data Analysis
( 24 Modules )

Module #1
Introduction to Genomics and Genomic Data
Overview of genomics, types of genomic data, and importance of analysis

Module #2
Introduction to Machine Learning
Basics of machine learning, types of learning, and key concepts

Module #3
Why Machine Learning in Genomics?
Motivation and applications of machine learning in genomics

Module #4
Overview of Course and Expectations
Course objectives, outline, and prerequisites

Module #5
Genomic Data Formats and Quality Control
Types of genomic data formats, quality control metrics, and tools

Module #6
Data Preprocessing for Genomics
Normalizing, transforming, and filtering genomic data

Module #7
Handling Missing Values and Noisy Data
Methods for dealing with missing values and noisy data in genomics

Module #8
Data Visualization for Genomics
Visualizing genomic data for exploration and quality control

Module #9
Introduction to Supervised Learning
Basics of supervised learning, classification, and regression

Module #10
Classification in Genomics
Applying classification algorithms to genomic data (e.g. disease prediction)

Module #11
Regression in Genomics
Applying regression algorithms to genomic data (e.g. gene expression prediction)

Module #12
Hyperparameter Tuning for Genomic Data
Tuning hyperparameters for optimal performance in genomic data

Module #13
Introduction to Unsupervised Learning
Basics of unsupervised learning, clustering, and dimensionality reduction

Module #14
Clustering in Genomics
Applying clustering algorithms to genomic data (e.g. identifying gene modules)

Module #15
Dimensionality Reduction in Genomics
Applying dimensionality reduction techniques to genomic data

Module #16
Unsupervised Learning for Novel Biological Insights
Using unsupervised learning to discover new biological patterns in genomic data

Module #17
Deep Learning for Genomics
Applying deep learning techniques to genomic data (e.g. convolutional neural networks)

Module #18
Genomic Data Integration and Multi-Task Learning
Integrating multiple types of genomic data and multi-task learning

Module #19
Explainable AI in Genomics
Interpreting machine learning models for genomics and understanding feature importance

Module #20
Current Challenges and Future Directions
Discussion of current challenges and future directions in machine learning for genomics

Module #21
Case Study:Cancer Genomics
Applying machine learning to cancer genomics data

Module #22
Case Study:Genetic Variation and Disease Risk
Applying machine learning to genetic variation data for disease risk prediction

Module #23
Developing a Machine Learning Project for Genomics
Guided project development for applying machine learning to genomic data

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Machine Learning for Genomic Data Analysis career

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Machine Learning for Genomic Data Analysis ( 24 Modules )

Machine Learning for Genomic Data Analysis
( 24 Modules )