Download Lynda - Supervised Learning Essential Training torrent

Lynda - Supervised Learning Essential Training

Torrent:	Lynda - Supervised Learning Essential Training
Description:	Description Data scientists and ML/AI students may need some practical experience with supervised learning algorithms. In this course, instructor Ayodele Odubela teaches you to apply models you’ve created to new data and to assess model performance. First, Ayodele outlines what supervised learning is and how to make predictions using labeled training data. She gives you an overview of the logistic regression algorithm, how to build a linear model in Python, and how to calculate model metrics. Next, Ayodele helps you create your first decision trees as well as k-nearest neighbors models using GridSearch. Ayodele covers how you can create artificial neural networks that are foundational for most deep learning work. She concludes with an ethical AI overview and asks you to consider the impact of your models. Released 1/21/2021
Downloads:	133
Category:	Other /Tutorials
Size:	268.3 MB
Show Files »	Supervised Learning Essential Training [TutsNode.com] - Supervised Learning Essential Training 01 Supervised machine learning and the technology boom.mp4 (22.8 MB) 01 Supervised machine learning and the technology boom.en.srt (2.1 KB) 02 Using the exercise files.en.srt (1.9 KB) 09 Checking your dataset for assumptions.en.srt (12.8 KB) 03 What you should know.en.srt (1.2 KB) 22 Building your own KNN.en.srt (11.3 KB) 08 Steps to prepare data for modeling.en.srt (9.6 KB) 17 Analyzing decision tree performance.en.srt (8.6 KB) 05 Python supervised learning packages.en.srt (1.8 KB) 11 Creating a logistic regression model.en.srt (8.0 KB) 14 Splitting data and limiting decision tree depth.en.srt (6.8 KB) 04 What is supervised learning.en.srt (6.1 KB) 19 Discovering your k-nearest neighbors.en.srt (6.0 KB) 25 Biological vs. artificial neural networks.en.srt (5.6 KB) 07 Defining logistic and linear regression.en.srt (5.5 KB) 10 Creating a linear regression model.en.srt (5.4 KB) 26 Preprocessing data for modeling.en.srt (5.3 KB) 23 Deciphering KNN model metrics.en.srt (5.3 KB) 30 Ethical overview.en.srt (5.3 KB) 24 Searching for the best model.en.srt (5.2 KB) 16 Creating your first decision trees.en.srt (4.8 KB) 28 Assembling your neural networks.en.srt (4.7 KB) 15 How to build a decision tree.en.srt (3.9 KB) 12 Evaluating regression model predictions.en.srt (4.5 KB) 20 What's the big deal about k.en.srt (3.9 KB) 27 How neural networks find patterns in data.en.srt (3.8 KB) 13 Identify common decision trees.en.srt (3.7 KB) 06 Predicting with supervised learning.en.srt (3.8 KB) 21 How to assemble a KNN model.en.srt (3.6 KB) 18 Exploring how ensemble methods create strong learners.en.srt (3.5 KB) 29 Comparing networks and selecting final models.en.srt (3.4 KB) 31 How can I keep developing my skills in supervised learning.en.srt (1.0 KB) 22 Building your own KNN.mp4 (19.7 MB) 09 Checking your dataset for assumptions.mp4 (18.0 MB) 17 Analyzing decision tree performance.mp4 (16.5 MB) 08 Steps to prepare data for modeling.mp4 (12.2 MB) 31 How can I keep developing my skills in supervised learning.mp4 (11.7 MB) 23 Deciphering KNN model metrics.mp4 (11.2 MB) 14 Splitting data and limiting decision tree depth.mp4 (10.7 MB) 11 Creating a logistic regression model.mp4 (10.4 MB) 24 Searching for the best model.mp4 (9.3 MB) 28 Assembling your neural networks.mp4 (8.9 MB) 25 Biological vs. artificial neural networks.mp4 (8.4 MB) 04 What is supervised learning.mp4 (7.9 MB) 30 Ethical overview.mp4 (7.5 MB) 07 Defining logistic and linear regression.mp4 (7.4 MB) 19 Discovering your k-nearest neighbors.mp4 (7.2 MB) 16 Creating your first decision trees.mp4 (7.2 MB) 10 Creating a linear regression model.mp4 (7.0 MB) 12 Evaluating regression model predictions.mp4 (6.3 MB) 20 What's the big deal about k.mp4 (5.6 MB) 15 How to build a decision tree.mp4 (5.6 MB) 06 Predicting with supervised learning.mp4 (5.5 MB) 26 Preprocessing data for modeling.mp4 (5.3 MB) 27 How neural networks find patterns in data.mp4 (5.2 MB) 21 How to assemble a KNN model.mp4 (4.9 MB) 18 Exploring how ensemble methods create strong learners.mp4 (4.9 MB) 13 Identify common decision trees.mp4 (4.8 MB) 29 Comparing networks and selecting final models.mp4 (4.0 MB) Ex_Files_Supervised_Learning.zip (3.4 MB) 05 Python supervised learning packages.mp4 (2.2 MB) 02 Using the exercise files.mp4 (1.9 MB) 03 What you should know.mp4 (1.1 MB) TutsNode.com.txt (0.1 KB) [TGx]Downloaded from torrentgalaxy.to .txt (0.6 KB) .pad 0 (66.3 KB) 1 (43.5 KB) 2 (222.7 KB) 3 (242.0 KB) 4 (75.4 KB) 5 (2.5 KB) 6 (74.5 KB) 7 (39.7 KB) 8 (97.7 KB) 9 (241.8 KB) 10 (92.8 KB) 11 (57.0 KB) 12 (112.8 KB) 13 (209.8 KB) 14 (62.7 KB) 15 (67.7 KB) 16 (90.5 KB) 17 (41.3 KB) 18 (182.8 KB) 19 (157.8 KB) 20 (197.5 KB) 21 (29.2 KB) 22 (214.3 KB) 23 (36.0 KB) 24 (92.8 KB) 25 (134.2 KB) 26 (178.7 KB) 27 (211.2 KB) 28 (52.7 KB) 29 (57.5 KB) 30 (60.8 KB) files
Added:	2021-01-25 11:04:52
Language:	English
Peers:	Seeders : 15 , Leechers : 3
Release name:	Lynda - Supervised Learning Essential Training
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