Все публикации

1:24:52

Lab 25 - Natural Language Processing and Sentiment Analysis in Python

1:38:29

Lab 24 - NLP and Sentiment Analysis of Amazon's Baby Products with Logistic Regression in Python

1:16:11

Lab 23 - Kernel Regression using Python

1:05:47

Lab 22 - Polynomial Regression using Python

1:15:39

Lab 21: Steam Optimization with XGBoost in R (a Xeek Competition)

0:55:53

Lab 20: XGB Lithology Classification Lessons Learned from Force 2020 ML Competition

1:09:17

Lab 19 - Neural networks from scratch in Python with Geoscience examples

0:51:52

Lab 18 - Long-Short Term Memory (LSTM) as a Machine Learning Algorithm for Seismic Inversion

1:02:02

Lab 17: Deep Learning with U-Net to Perform Seismic Inversion

0:42:05

Lab 16: Seismic Data Engineering for Machine Learning Inversion

0:40:22

Lab 15: Data Imputation of Well Logs using Regression Models

0:58:48

Lab 14: Clustering Models Applied to the Energy Sector - Part 3

1:13:36

Lab 13: Clustering Models Applied to the Energy Sector - Part 2

0:59:48

Lab 12: Clustering Models Applied to the Energy Sector - Part 1

1:10:46

Lab 11: Using Hybrid Machine Learning Models

0:53:51

Lab 10: An Overview of Machine Learning Applications on the Energy Sector

0:55:31

Lab 09: Impact analysis in R - the effects of the COVID-19 pandemic to the oil industry

1:05:40

Lab 08: Time Series Forecasting with SARIMA -Application to COVID-19 Pandemic Data

0:47:38

Lab 06: Salt Identification in Seismic Sessions using Tensorflow for Deep Learning Solutions

1:00:57

Lab 07: Unsupervised Seismic Facies Classification using Python (Dr. Brian Russell)

1:03:31

Lab 05: Machine Learning for Lithology Classification from Wireline Logs (Facies Classification)

1:14:27

Lab 04: Natural Language Processing and Machine Learning to Classify Severe Injuries (Oil and Gas)

0:53:29

Lab 03: Introduction to HTML, CSS, and Chrome DevTools for Shiny Apps Layouts

1:22:37

Lab 02 - Fundamentals of R, Flexdashboard, and Shiny for Data Science