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TORRENT DETAILS

[FreeCourseWeb] Udemy - Machine Learning Intro For Python Developers

TORRENT SUMMARY

	Status:	All the torrents in this section have been verified by our verification system
Category:	Other
Infohash	e5be9f8e23ef9b5d89be2fbd073b3301558d533b
Size:	366.3 MB in 43 files
Release Date(s):	Torrent Release: 13-02-2019 by user freecourseweb
Swarm:	0 Seeds & 0 Peers
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DESCRIPTION

[FreeCourseWeb] Udemy - Machine Learning Intro for Python Developers

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + .VTT | Duration: 44 mins | Size: 366 MB Get started with Machine Learning Algorithms What you'll learn Machine Learning Basics with Python How to apply ML algorithms to your own problems How some algorithms work internally (kmeans, decision tree) Classify data automatically Predict prices with algorithms Group data points automatically Requirements Experience with Python Programming Description Learn how to use Machine Learning with Python Scikit! This is a getting started course for Machine Learning. If you are new to Machine Learning, this course is for you! We'll teach you how to get started with Machine Learning including these topics: What is Machine Learning Automatically classify new data Predict prices with regression Group your data automatically with clustering Who this course is for: Python developers curious about Machine Learning If you like this torrent then Please, we repeat, Please, Support Authors. They Deserve it [/b]

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FILE LIST

Filename		Size
	1. Introduction/1. Introduction.mp4	8.2 MB
	1. Introduction/1. Introduction.vtt	910 B
	1. Introduction/2. What is Machine Learning.mp4	9 MB
	1. Introduction/2. What is Machine Learning.vtt	1.4 KB
	1. Introduction/3. Example Problems.mp4	9.7 MB
	1. Introduction/3. Example Problems.vtt	1.6 KB
	1. Introduction/4. Supervised Learning.mp4	15.1 MB
	1. Introduction/4. Supervised Learning.vtt	2.6 KB
	2. First Program (Classifier)/1. Installing Scikit and Numpy.html	1.2 KB
	2. First Program (Classifier)/2. Important Note.html	268 B
	2. First Program (Classifier)/3. First Program (Classifier).mp4	55.4 MB
	2. First Program (Classifier)/3. First Program (Classifier).vtt	5.6 KB
	2. First Program (Classifier)/3.1 ML-classification-example1.py.py	968 B
	2. First Program (Classifier)/4. Lecture Notes.html	420 B
	2. First Program (Classifier)/4.1 ML-classification-example1.py.py	968 B
	2. First Program (Classifier)/5. Exercise.html	285 B
	2. First Program (Classifier)/5.1 solution.py.py	261 B
	3. Iris Classifier/1. Iris Classifier.mp4	26.2 MB
	3. Iris Classifier/1. Iris Classifier.vtt	2.3 KB
	3. Iris Classifier/1.1 ML-classification-iris.py.py	865 B
	3. Iris Classifier/2. Accuracy Score.mp4	32.7 MB
	3. Iris Classifier/2. Accuracy Score.vtt	4.3 KB
	3. Iris Classifier/2.1 ML-accuracy.py.py	1020 B
	3. Iris Classifier/3. See a Decision Tree.mp4	18.5 MB
	3. Iris Classifier/3. See a Decision Tree.vtt	2.1 KB
	3. Iris Classifier/3.1 ML-draw-a-classification-tree.py.py	1.6 KB
	4. Regression/1. Linear Regression.mp4	32.4 MB
	4. Regression/1. Linear Regression.vtt	5.6 KB
	4. Regression/1.1 ML-regression1.py.py	560 B
	4. Regression/1.2 ML-regression2.py.py	908 B
	4. Regression/2. Lecture Notes.html	455 B
	4. Regression/3. Regression Example.mp4	51 MB
	4. Regression/3. Regression Example.vtt	5.7 KB
	4. Regression/3.1 ML-regression3.py.py	1.4 KB
	4. Regression/4. Polynomial Regression.mp4	28.3 MB
	4. Regression/4. Polynomial Regression.vtt	5.3 KB
	4. Regression/4.1 ML-nonlinear-prediction.py.py	831 B
	5. Clustering/1. Clustering.mp4	31.3 MB
	5. Clustering/1. Clustering.vtt	6.1 KB
	5. Clustering/1.1 ML-cluster.py.py	2.6 KB
	5. Clustering/2. Lecture Notes.html	708 B
	5. Clustering/3. Kmeans explained.mp4	48.5 MB
	5. Clustering/3. Kmeans explained.vtt	5.4 KB

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