BOOKS - PROGRAMMING - Supervised Machine Learning Optimization Framework and Applicat...
Supervised Machine Learning Optimization Framework and Applications with SAS and R - Tanya Kolosova, Samuel Berestizhevsky 2021 PDF | EPUB Chapman and Hall/CRC BOOKS PROGRAMMING
ECO~12 kg CO²

1 TON

Views
12391

Telegram
 
Supervised Machine Learning Optimization Framework and Applications with SAS and R
Author: Tanya Kolosova, Samuel Berestizhevsky
Year: 2021
Pages: 176
Format: PDF | EPUB
File size: 10 MB
Language: ENG



Pay with Telegram STARS
''

You may also be interested in:

Supervised Machine Learning Optimization Framework and Applications with SAS and R
Hands-on Supervised Learning with Python Learn How to Solve Machine Learning Problems with Supervised Learning Algorithms
Supervised Machine Learning with Python A Comprehensive guide to Supervised Learning for 2024
Supervised Machine Learning with Python: A Comprehensive guide to Supervised Learning for 2024
Supervised Machine Learning with Python A Comprehensive guide to Supervised Learning for 2024
Linear Algebra And Optimization With Applications To Machine Learning - Volume II Fundamentals of Optimization Theory with Applications to Machine Learning
Hands-On Machine Learning with Scikit-Learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
Machine Learning Master Supervised and Unsupervised Learning Algorithms with Real Examples
Machine Learning: Master Supervised and Unsupervised Learning Algorithms with Real Examples (English Edition)
Machine Learning. Supervised Learning Techniques and Tools Nonlinear Models Exercises with R, SAS, STATA, EVIEWS and SPSS
Machine Learning: Fundamental Algorithms for Supervised and Unsupervised Learning With Real-World Applications (Advanced Data Analytics Book 1)
Supervised Machine Learning for Text Analysis in R
The Mathematics of Machine Learning Lectures on Supervised Methods and Beyond
The Mathematics of Machine Learning Lectures on Supervised Methods and Beyond
The Mathematics of Machine Learning Lectures on Supervised Methods and Beyond
Fundamentals of Supervised Machine Learning With Applications in Python, R, and Stata
Fundamentals of Supervised Machine Learning With Applications in Python, R, and Stata
Machine Learning For Beginners Guide Algorithms Supervised & Unsupervsied Learning. Decision Tree & Random Forest Introduction
Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction
The Definitive Guide to Machine Learning Operations in AWS Machine Learning Scalability and Optimization with AWS
Fundamentals of Supervised Machine Learning: With Applications in Python, R, and Stata (Statistics and Computing)
Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions
Machine Learning and Optimization for Engineering Design (Engineering Optimization: Methods and Applications)
Supervised and Unsupervised Learning for Data Science (Unsupervised and Semi-Supervised Learning)
Statistical Machine Learning: A Unified Framework
Machine Learning and Optimization for Engineering Design
Machine Learning A Bayesian and Optimization Perspective
Machine Learning and Optimization for Engineering Design
Text as Data: A New Framework for Machine Learning and the Social Sciences
Advanced Techniques in Optimization for Machine Learning and Imaging
Stochastic Optimization for Large-scale Machine Learning
Machine Learning Hybridization and Optimization for Intelligent Applications
Machine Learning for Radio Resource Management and Optimization in 5G and Beyond
Advanced Techniques in Optimization for Machine Learning and Imaging
Machine Learning Hybridization and Optimization for Intelligent Applications
Fundamentals of Optimization Theory With Applications to Machine Learning
Metaheuristic and Machine Learning Optimization Strategies for Complex Systems
Financial Data Analytics with Machine Learning, Optimization and Statistics
Metaheuristic and Machine Learning Optimization Strategies for Complex Systems
An Introduction to Optimization With Applications to Machine Learning, 5th Edition