Udemy - Machine Learning in Bioinformatics - From Theory to Practical torrent

Machine Learning in Bioinformatics: From Theory to Practical

https://WebToolTip.com

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