Free Download Udemy - Machine Learning in Bioinformatics From Theory to Practical
Published: 4/2025
Created by: Rafiq Ur Rehman
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 35 Lectures ( 6h 4m ) | Size: 2.25 GB
Machine Learning for Bioinformatics: Analyze Genomic Data, Predict Disease, and Apply AI to Life Sciences
What you'll learn
Understand key machine learning concepts, including supervised and unsupervised learning.
Learn the differences between classification, regression, clustering, and deep learning in bioinformatics.
Process and analyze different types of biological data, such as genomic sequences, transcriptomics, and proteomics data.
Understand feature engineering and data preprocessing techniques specific to bioinformatics datasets.
Implement essential machine learning algorithms like Random Forest, SVM, k-means clustering, and neural networks in bioinformatics.
Learn dimensionality reduction techniques (e.g., PCA, t-SNE) for high-dimensional biological data.
Work with Scikit-learn, TensorFlow, Biopython, and Pandas to apply ML techniques in bioinformatics.
Develop and optimize machine learning models for gene expression analysis, protein structure prediction, and variant classification.
Apply machine learning to genomic variant classification, drug discovery, personalized medicine, and disease prediction.
Build a machine learning pipeline for predicting gene function and protein interactions.
Evaluate model performance using cross-validation, confusion matrices, ROC curves, and precision-recall metrics.
Fine-tune models using hyperparameter optimization and feature selection.
Understand deep learning architectures like CNNs and RNNs for biological sequence analysis.
Implement deep learning models for protein structure prediction and genome annotation.
Develop machine learning models for bioinformatics research and real-world applications.
Learn how to interpret ML results for biological insights and scientific publications.
Requirements
No Prior Machine Learning Experience Needed!
Familiarity with biological concepts such as DNA, RNA, proteins, and gene expression.
Basic knowledge of bioinformatics file formats (FASTA, FASTQ, CSV, etc.).
Basic understanding of Python syntax, loops, functions, and data structures.
Experience with libraries like NumPy, Pandas, or Matplotlib is a plus, but not required.
Understanding of basic concepts like mean, median, standard deviation, probability, and correlation.
Some familiarity with linear algebra and calculus
Description
Machine Learning for Bioinformatics: Analyze Genomic Data, Predict Disease, and Apply AI to Life SciencesUnlock the Power of Machine Learning in Bioinformatics & Computational BiologyMachine learning (ML) is transforming the field of bioinformatics, enabling researchers to analyze massive biological datasets, predict gene functions, classify diseases, and accelerate drug discovery. If you're a bioinformatics student, researcher, life scientist, or data scientist looking to apply machine learning techniques to biological data, this course is designed for you!In this comprehensive hands-on course, you will learn how to apply machine learning models to various bioinformatics applications, from analyzing DNA sequences to classifying diseases using genomic data. Whether you are new to machine learning or have some prior experience, this course will take you from the fundamentals to real-world applications step by step.Why Should You Take This Course?No Prior Machine Learning Experience Required - We start from the basics and gradually build up to advanced techniques.Bioinformatics-Focused Curriculum - Unlike general ML courses, this course is tailored for biological and biomedical datasets.Hands-on Python Coding - Learn Scikit-learn, Biopython, NumPy, Pandas, and TensorFlow to implement machine learning models.Real-World Applications - Work on projects involving genomics, transcriptomics, proteomics, and disease prediction. Machine Learning Algorithms Explained Clearly - Understand how models like Random Forest, SVM, Neural Networks, and Deep Learning are applied in bioinformatics. What You Will Learn in This Course?By the end of this course, you will be able to: 1. Introduction to Machine Learning in BioinformaticsWhat is machine learning, and why is it important in bioinformatics?Overview of Supervised vs. Unsupervised LearningKey challenges in biological data analysis and how ML helps 2. Working with Biological DatasetsIntroduction to genomic, transcriptomic, and proteomic datasetsUnderstanding biological file formats: FASTA, FASTQ, CSV, and moreData preprocessing & cleaning: Handling missing values and noisy data 3. Supervised Learning for BioinformaticsUnderstanding classification & regression algorithmsImplementing Logistic Regression, Decision Trees, and Random ForestCase Study: Predicting disease from gene expression data 4. Unsupervised Learning & Clustering in BioinformaticsIntroduction to clustering techniquesApplying K-means and Hierarchical Clustering to gene expression analysisDimensionality Reduction: PCA, t-SNE, and their role in biological data visualization 5. Deep Learning & Neural Networks for BioinformaticsBasics of Deep Learning (DL) and Neural NetworksHow CNNs and RNNs are used for protein structure prediction & genome annotationCase Study: Using deep learning to classify cancer subtypes 6. Hands-on Machine Learning with Python for BioinformaticsSetting up the Python environment for ML applicationsWorking with Scikit-learn, Pandas, Biopython, and TensorFlowStep-by-step implementation of ML models on Synthetic biological data 7. Machine Learning Applications in Bioinformatics & Life SciencesGenomic Variant Classification using MLDrug Discovery & Personalized MedicineDisease Prediction Models for precision medicinePredicting protein-protein interactions (PPIs) using ML 8. Model Evaluation & Optimization in BioinformaticsEvaluating ML models with confusion matrices, ROC curves, and precision-recall analysisHyperparameter tuning for improved performanceAvoiding overfitting and improving model generalization 9. Building and Deploying Bioinformatics ML ModelsCreating end-to-end ML pipelines for bioinformaticsDeploying ML models in biomedical research & clinical settingsEthical considerations in AI-driven bioinformatics research Who Should Take This Course?This course is perfect for: Bioinformatics Students & Researchers - Learn how to integrate ML into your bioinformatics research. Life Science Professionals - Biologists, geneticists, and biotechnologists wanting to explore ML applications in genomics & drug discovery. Data Scientists - Looking to specialize in bioinformatics and apply ML to biological problems. Healthcare & Biomedical Professionals - Interested in AI-driven personalized medicine & disease prediction. Beginners in Machine Learning - No prior experience needed! This course teaches ML from scratch, specifically for bioinformatics applications.Course Requirements & PrerequisitesYou don't need prior experience in machine learning, but the following will be helpful:Basic biology and bioinformatics knowledge (DNA, RNA, proteins, gene expression)Some Python programming experience (loops, functions, data structures)Basic understanding of statistics and probabilityIf you're completely new to programming, we'll guide you step-by-step through the coding exercises! Tools & Technologies CoveredPython for Machine Learning (NumPy, Pandas, Matplotlib)Scikit-learn (for classical ML algorithms)TensorFlow/Keras (for deep learning applications)Biopython (for working with biological datasets)Jupyter Notebooks (for hands-on coding)What Makes This Course Unique?Hands-on Learning: Work with synthetic biological datasets and apply ML techniques step by step.Bioinformatics-Focused Curriculum: Unlike generic ML courses, we focus only on bioinformatics & life sciences applications.Comprehensive Yet Beginner-Friendly: We explain everything from basic ML to advanced deep learning models in an easy-to-understand way. Industry & Research Applications: Learn how ML is used in biotech, healthcare, and drug discovery.Course Projects & Real-World ApplicationsThroughout the course, you'll work on practical projects such as: Gene Expression Analysis Using ML Protein Sequence Classification with Deep Learning Cancer Subtype Prediction Using Genomic Data Building a Bioinformatics ML Pipeline for Variant ClassificationBy the end, you'll have portfolio-ready projects that showcase your ML & bioinformatics skills! Ready to Start Your Machine Learning Journey in Bioinformatics?Join now and take your bioinformatics skills to the next level with machine learning!Let's analyze genomes, predict diseases, and accelerate discoveries using AI! Enroll today and start applying machine learning to real-world biological problems!
Who this course is for
Undergraduate, postgraduate, and PhD students looking to integrate machine learning into their bioinformatics research.
Biologists, geneticists, and biotechnologists who want to learn how machine learning can be applied to genomics, proteomics, and drug discovery.
Data scientists looking to specialize in bioinformatics by applying machine learning algorithms to biological datasets.
Learners with little or no prior experience in machine learning but with an interest in biology and data analysis.
Researchers working on personalized medicine, biomarker discovery, and disease prediction who want to leverage machine learning for data analysis.
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redicting disease from gene expression data 