Proxmox Full Course
Comprehensive guide to Proxmox virtualization platform: installation, management, networking, storage, clustering, and high availability for efficient server virtualization and containerization.
STAT115
Comprehensive lecture series on statistical methods for analyzing genomic data, covering microarrays, RNA-seq, epigenetics, cancer genomics, and more. Focuses on practical applications in bioinformatics and computational biology.
Deep Learning Intuition - Lecture 2
Explore deep learning intuition with Stanford experts, covering logistic regression, face verification, neural architectures, and advanced applications in computer vision and image processing.
Machine Learning for Genomics Fall 2020
Fall 2020 Prof. Manolis KellisComputational Biology: Genomes, Networks, Evolution, HealthMachine Learning in Genomics: Dissecting the circuitry of Human Disease
Machine Learning with Python
Comprehensive Python tutorial covering machine learning concepts, algorithms, and deep learning with TensorFlow. Includes hands-on projects and real-world applications.
Pattern Recognition and Application
This course covers feature extraction techniques and the representation of patterns in feature space. Measure of similarity between two patterns. Statistical, nonparametric and neural network techniques for pattern recognition have been discussed in this…
Random Forests
Learn to build, evaluate, and implement Random Forests, including handling missing data and clustering. Gain practical skills using R for this powerful machine learning technique.
Fairness in Networks - Information Access, Disadvantage, and Clustering
Explore fairness in social networks, examining information access, disadvantage, and clustering. Learn about interventions for equal access and the implications of network-based information flow.
Data Thinning to Avoid Double Dipping in Statistical Analysis - Lecture 1
Explore data thinning techniques to prevent double dipping in statistical analysis, with applications in single-cell RNA sequencing and unsupervised learning problems.
The Emergence of Clusters in Self-Attention Dynamics
Explore the formation of clusters in self-attention dynamics, examining their impact on transformer models and computational processes.
Scaling ML Embedding Models to Serve a Billion Queries
Learn to scale ML embedding models for visual search, exploring eBay's architecture, workflows, and solutions to handle billions of queries efficiently.
SciPy in Python - Scientific Python Tutorial
Comprehensive tutorial on SciPy library for scientific computing in Python, covering installation, sub-packages, clusters, constants, FFTPack, interpolation, linear algebra, and image processing.
Unsupervised Learning
Explore key unsupervised learning techniques including clustering, dimensionality reduction, and generative models. Gain insights into real-world applications like recommendation systems and image compression.
Statistics Tutorials
Comprehensive tutorials covering key statistical concepts, data visualization, clustering, probability, and regression for aspiring data scientists.
Machine Learning 1 - 2020
Comprehensive introduction to machine learning fundamentals, covering probability theory, regression, classification, neural networks, clustering, and advanced topics like kernels and Gaussian processes.
Data Science Projects in Python
This video teaches these projects by carrying out simultaneous demonstrations and examples to help you get started on the right foot.
CMDB and Discovery Tutorial Series - ServiceNow Tutorial for Beginners
Comprehensive tutorial on ServiceNow CMDB and Discovery, covering installation, configuration, and advanced topics for IT professionals seeking in-depth knowledge of these essential tools.
The Fun and Easy Guide to Machine Learning - Tutorials
Comprehensive introduction to machine learning algorithms, from regression to neural networks, with practical Python implementations and engaging whiteboard explanations for beginners and enthusiasts.
Customer Segmentation Using Clustering
Learn customer segmentation using four clustering techniques: KMeans, DBSCAN, MeanShift, and Agglomerative. Apply these methods to real-world data and compare their effectiveness in dividing customers into meaningful groups.
Read CSV Files, Extract Dataset Scikit Learn & Simulate Data Using Python - Machine Learning
Learn to extract data from CSV files and databases, use scikit-learn for dataset manipulation, and simulate various types of machine learning data in Python.
Complete Machine Learning in 6 Hours - Krish Naik
Comprehensive machine learning tutorial covering key algorithms, techniques, and practical implementations for regression, classification, clustering, and ensemble methods.