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TORRENT DETAILS

[FTUForum.com] [UDEMY] Python Machine Learning Basics Shallow Learning Algorithms [FTU]

TORRENT SUMMARY

	Status:	All the torrents in this section have been verified by our verification system
Category:	Other
Infohash	aa5bf9d6c42e0f2d108b89824cb50920bf21d9cd
Size:	451.3 MB in 34 files
Release Date(s):	Torrent Release: 18-05-2019 by user
Swarm:	0 Seeds & 0 Peers
Vote:	No votes yet.

DESCRIPTION

Become an expert in Python Machine Learning recommendation algorithms

Created by: Mark Nielsen

Last updated: 9/2018

Language: English

Caption (CC): Included

Torrent Contains: 39 Files, 5 Folders

Course Source: https://www.udemy.com/basic-machine-learning-shallow-learning/

What you'll learn

• Understand basic machine learning principles

• Understand the different between Shallow learning and Deep learning

• Implement shallow learning algorithms

Requirements

• Computer

• Internet

• No prior coding knowledge

Description

Start your Machine Learning career with this basic Machine learning course. Together we will look into the "shallow" learning algorithms KNN and SVD. The course consist of both assignments, follow along programming and theory lectures to ensure that you as a student have the best possible foundation to make real world applications using KNN or SVD.

Who this course is for:

• Anyone who wish to learn machine learning

• Anyone who wish to develop recommender systems.

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FILE LIST

Filename		Size
	0. Websites you may like/How you can help Team-FTU.txt	237 B
	1. Introduction/1. What is Machine Learning.mp4	28.7 MB
	1. Introduction/1. What is Machine Learning.vtt	5.8 KB
	1. Introduction/2. Shallow vs Deep learning.mp4	13.3 MB
	1. Introduction/2. Shallow vs Deep learning.vtt	3.4 KB
	1. Introduction/3. Theory vs Practice.mp4	11.4 MB
	1. Introduction/3. Theory vs Practice.vtt	3.6 KB
	2. Machine Learning Framework/1. Scikit Surprise.mp4	38.6 MB
	2. Machine Learning Framework/1. Scikit Surprise.vtt	4.4 KB
	2. Machine Learning Framework/2. Evaluation.mp4	21.5 MB
	2. Machine Learning Framework/2. Evaluation.vtt	8 KB
	2. Machine Learning Framework/3. Install Python 3.5+.mp4	7.3 MB
	2. Machine Learning Framework/3. Install Python 3.5+.vtt	1017 B
	2. Machine Learning Framework/4. MI Python skeleton.mp4	58.7 MB
	2. Machine Learning Framework/4. MI Python skeleton.vtt	8.9 KB
	3. SVD/1. SVD implementation.mp4	53.1 MB
	3. SVD/1. SVD implementation.vtt	6.9 KB
	3. SVD/2. SVD Theory.mp4	37 MB
	3. SVD/2. SVD Theory.vtt	4.5 KB
	3. SVD/2.1 Singular Value Decomposition Stanford University.html	104 B
	3. SVD/3. Implement SVD().html	150 B
	4. KNN/1. KNN implementation.mp4	37.7 MB
	4. KNN/1. KNN implementation.vtt	4.2 KB
	4. KNN/2. Knn theory.mp4	22.6 MB
	4. KNN/2. Knn theory.vtt	5.6 KB
	4. KNN/3. User vs Item.mp4	18.6 MB
	4. KNN/3. User vs Item.vtt	2.3 KB
	4. KNN/4. Implementing KNN.html	150 B
	5. Going Live/1. Display KNN neighbors.mp4	31.7 MB
	5. Going Live/1. Display KNN neighbors.vtt	4.1 KB
	5. Going Live/2. Display SVD Item Predictions.mp4	22.6 MB
	5. Going Live/2. Display SVD Item Predictions.vtt	3.9 KB
	5. Going Live/3. Surpriselib and Flask.mp4	48.5 MB
	5. Going Live/3. Surpriselib and Flask.vtt	6.1 KB

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