Скачать или смотреть Data Mining with Orange - Complete Expert Tech Tutorial For Beginners

Learn #DataMining and predict future trends in businesses. Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. In other words is the process of finding anomalies, patterns, and correlations within large data sets to predict outcomes. It is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use. #DataMining is the analysis step of the "knowledge discovery in databases" process or KDD.

Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for explorative rapid qualitative data analysis and interactive data visualization.

⭐️ Download Orange: https://orangedatamining.com/download/



⭐️ Tutorial Timestamps ⭐️
⌨️ 0:00:05 Welcome to Orange Data mining
⌨️ 0:02:08 Data Workflows
⌨️ 0:04:31 Orange Widgets and channels
⌨️ 0:07:32 Loading your data
⌨️ 0:11:34 Hierarchical clustering
⌨️ 0:15:25 Making prediction
⌨️ 0:18:58 Model evaluation and scoring
⌨️ 0:22:36 Add ons
⌨️ 0:25:17 Principal component analysis
⌨️ 0:28:08 Feature scoring and ranking
⌨️ 0:31:36 K-means
⌨️ 0:35:49 K-means clustering
⌨️ 0:45:10 Image analytics - Clustering
⌨️ 0:48:16 Text preprocessing
⌨️ 0:52:12 Text clustering
⌨️ 0:55:50 Text classification
⌨️ 0:59:45 Importing text documents
⌨️ 1:02:52 Multivariate projection - freeviz
⌨️ 1:06:24 Text processing - documents embeddings
⌨️ 1:10:07 Text mining - document to network
⌨️ 1:13:15 Sentiment analysis
⌨️ 1:17:38 Text mining - twitter data analysis







⭐️ Credit ⭐️
This course is created by: Orange Data Mining
YouTube: https://www.youtube.com/channel/UClKK...
License: Creative Commons Attribution license (reuse allowed)

You can write us @ [email protected]


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