Download Applied Machine Learning - Feature Engineering (2024) torrent

Applied Machine Learning - Feature Engineering (2024)

Torrent:	Applied Machine Learning - Feature Engineering (2024)
Description:	Applied Machine Learning: Feature Engineering (2024) https://CourseWikia.com Released 4/2024 MP4 \| Video: h264, 1280x720 \| Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Intermediate \| Genre: eLearning \| Language: English + srt \| Duration: 1h 41m \| Size: 255 MB Machine learning is not magic. The quality of the predictions coming out of your model is a direct reflection of the data you feed it during training. This course with instructor Matt Harrison guides you through the nuances of feature engineering techniques for numeric data so you can take a dataset, tease out the signal, and throw out the noise in order to optimize your machine learning model. Matt teaches you techniques like imputation, binning, log transformations, and scaling for numeric data. He covers methods for other types of data, like as one hot encoding, mean targeting coding, principal component analysis, feature aggregation, and text processing techniques like TFIDF and embeddings. The tools you learn in this course will generalize to nearly any kind of machine learning algorithm/problem, so join Matt in this course to learn how you can extract the maximum value from your data using feature engineering.
Downloads:	106
Category:	Other /Tutorials
Size:	254.6 MB
Show Files »	[ CourseWikia.com ] Applied Machine Learning - Feature Engineering (2024) Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 01 - Introduction 01 - Applied ML Feature engineering.mp4 (4.2 MB) 01 - Applied ML Feature engineering.srt (1.5 KB) 02 - What you should know.mp4 (5.3 MB) 02 - What you should know.srt (2.1 KB) 02 - 1. Basic Techniques 01 - Imputation.mp4 (11.6 MB) 01 - Imputation.srt (8.1 KB) 02 - Filling in missing values.mp4 (12.3 MB) 02 - Filling in missing values.srt (9.4 KB) 03 - Binning.mp4 (13.9 MB) 03 - Binning.srt (10.8 KB) 04 - Log transform.mp4 (13.6 MB) 04 - Log transform.srt (10.9 KB) 05 - Scaling.mp4 (5.2 MB) 05 - Scaling.srt (3.8 KB) 06 - Challenge Basic techniques.mp4 (2.9 MB) 06 - Challenge Basic techniques.srt (2.6 KB) 07 - Solution Basic techniques.mp4 (11.5 MB) 07 - Solution Basic techniques.srt (9.6 KB) 03 - 2. Categorical Encoding 01 - One hot encoding.mp4 (20.5 MB) 01 - One hot encoding.srt (12.0 KB) 02 - Hashing encoder.mp4 (5.6 MB) 02 - Hashing encoder.srt (4.6 KB) 03 - Mean target encoding.mp4 (7.0 MB) 03 - Mean target encoding.srt (3.9 KB) 04 - Challenge Categorical.mp4 (775.5 KB) 04 - Challenge Categorical.srt (0.7 KB) 05 - Solution Categorical.mp4 (13.7 MB) 05 - Solution Categorical.srt (8.4 KB) 04 - 3. Feature Extraction 01 - PCA.mp4 (7.0 MB) 01 - PCA.srt (4.4 KB) 02 - Feature aggregation.mp4 (5.7 MB) 02 - Feature aggregation.srt (3.4 KB) 03 - TFIDF.mp4 (16.1 MB) 03 - TFIDF.srt (7.7 KB) 04 - Text embeddings.mp4 (10.3 MB) 04 - Text embeddings.srt (6.3 KB) 05 - Challenge Feature extraction.mp4 (499.0 KB) 05 - Challenge Feature extraction.srt (0.4 KB) 06 - Solution Feature extraction.mp4 (4.5 MB) 06 - Solution Feature extraction.srt (3.0 KB) 05 - 4. Temporal Features 01 - Extracting date components.mp4 (2.0 MB) 01 - Extracting date components.srt (1.4 KB) 02 - Seasonality and trend decomposition.mp4 (14.9 MB) 02 - Seasonality and trend decomposition.srt (10.0 KB) 03 - Challenge Temporal features.mp4 (3.4 MB) 03 - Challenge Temporal features.srt (2.5 KB) 04 - Solution Temporal features.mp4 (21.3 MB) 04 - Solution Temporal features.srt (12.3 KB) 06 - 5. Feature Evaluation 01 - Importance and weights.mp4 (19.9 MB) 01 - Importance and weights.srt (14.1 KB) 02 - Recursive feature elimination.mp4 (6.0 MB) 02 - Recursive feature elimination.srt (3.8 KB) 03 - Adding a random column.mp4 (3.3 MB) 03 - Adding a random column.srt (2.5 KB) 04 - Challenge Feature selection.mp4 (602.0 KB) 04 - Challenge Feature selection.srt (0.5 KB) 05 - Solution Feature selection.mp4 (9.9 MB) 05 - Solution Feature selection.srt (4.6 KB) 07 - Conclusion 01 - Next steps.mp4 (1.2 MB) 01 - Next steps.srt (1.0 KB) Bonus Resources.txt (0.4 KB) files
Added:	2024-04-25 13:07:02
Language:	English
Peers:	Seeders : 25 , Leechers : 9
Release name:	Applied Machine Learning - Feature Engineering (2024)
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