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Download Torrent "Pluralsight | Creating Machine Learning Models [FCO]"

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Title:Pluralsight | Creating Machine Learning Models [FCO]
Description:
Lynda and other Courses >>> https://www.freecoursesonline.me/
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Created by: Janani Ravi
Language: English
Updated: Oct 29, 2019
Duration: 2h 44m
Course Source: https://www.pluralsight.com/courses/creating-machine-learning-models

About

This course covers the important types of machine learning algorithms, solution techniques based on the specifics of the problem you are trying to solve, as well as the classic machine learning workflow.

Description

As Machine Learning explodes in popularity, it is becoming ever more important to know precisely how to frame a machine learning model in a manner appropriate to the problem we are trying to solve, and the data that we have available.

In this course, Creating Machine Learning Models you will gain the ability to choose the right type of model for your problem, then build that model, and evaluate its performance.

First, you will learn how rule-based and ML-based systems differ and their strengths and weaknesses and how supervised and unsupervised learning models differ from each other.

Next, you will discover how to implement a range of techniques to solve the supervised learning problems of classification and regression. You will gain an intuitive understanding of the the model algorithms you can use for classification and regression. Finally, you will round out your knowledge by building clustering models using a couple of different algorithms, and validating the results.

When you’re finished with this course, you will have the skills and knowledge to identify the correct machine learning problem setup, and the appropriate solution and evaluation techniques for your use-case.

Level

• Intermediate

About Author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.




Category:Tutorials > Tutorials
Lang:English  English
Total Size:391 MB
Info Hash:85a166d9dde8bd9d31faf588af0cf89a0e110fc3
Added By:Prom3th3uS
Date Added:24-03-2020 16:33:44

  

Files

File List: 
 File Size
0. Websites you may like/0. (1Hack.Us) Premium Tutorials-Guides-Articles & Community based Forum.url377.00 B
0. Websites you may like/1. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url286.00 B
0. Websites you may like/2. (NulledPremium.com) Download E-Learning, E-Books, Audio-Books, & more.etc.url163.00 B
0. Websites you may like/3. (FTUApps.com) Download Cracked Developers Applications For Free.url239.00 B
0. Websites you may like/How you can help our Group!.txt208.00 B
01.Course Overview/01.01.Course Overview.mp410 MB
02.Understanding Approaches to Machine Learning/02.01.Module Overview.mp47 MB
02.Understanding Approaches to Machine Learning/02.02.Prerequisites and Course Outline.mp45 MB
02.Understanding Approaches to Machine Learning/02.03.Rule-based vs. ML-based Learning.mp414 MB
02.Understanding Approaches to Machine Learning/02.04.Traditional ML vs. Representation ML.mp47 MB
02.Understanding Approaches to Machine Learning/02.05.The Machine Learning Workflow.mp46 MB
02.Understanding Approaches to Machine Learning/02.06.Choosing the Right Model Based on Data.mp410 MB
02.Understanding Approaches to Machine Learning/02.07.Supervised vs. Unsupervised Learning.mp49 MB
02.Understanding Approaches to Machine Learning/02.08.Transfer Learning, Cold Start ML and Warm Start ML.mp410 MB
02.Understanding Approaches to Machine Learning/02.09.Popular Machine Learning Frameworks.mp47 MB
02.Understanding Approaches to Machine Learning/02.10.Demo Getting Started with scikit-learn.mp44 MB
02.Understanding Approaches to Machine Learning/02.11.Module Summary.mp43 MB
03.Understanding and Implementing Regression Models/03.01.Module Overview.mp42 MB
03.Understanding and Implementing Regression Models/03.02.Building and Evaluating Regression Models.mp49 MB
03.Understanding and Implementing Regression Models/03.03.Demo Linear Regression Using Numeric Features.mp418 MB
03.Understanding and Implementing Regression Models/03.04.Demo Exploring Regression Data.mp410 MB
03.Understanding and Implementing Regression Models/03.05.Demo Preprocessing Numeric and Categorical Data and Fitting a Regression Model.mp410 MB
03.Understanding and Implementing Regression Models/03.06.Choosing Regression Algorithms.mp45 MB
03.Understanding and Implementing Regression Models/03.07.Regularized Regression Models Lasso, Ridge, and Elastic Net.mp47 MB
03.Understanding and Implementing Regression Models/03.08.Stochastic Gradient Descent.mp44 MB
03.Understanding and Implementing Regression Models/03.09.Demo Multiple Types of Regression.mp412 MB
03.Understanding and Implementing Regression Models/03.10.Module Summary.mp43 MB
04.Understanding and Implementing Classification Models/04.01.Module Overview.mp42 MB
04.Understanding and Implementing Classification Models/04.02.Types of Classifiers.mp48 MB
04.Understanding and Implementing Classification Models/04.03.Understanding Logistic Regression Intuitively.mp410 MB
04.Understanding and Implementing Classification Models/04.04.Demo Building and Training a Binary Classification Model.mp414 MB
04.Understanding and Implementing Classification Models/04.05.Understanding Support Vector and Nearest Neighbors Classification.mp47 MB
04.Understanding and Implementing Classification Models/04.06.Understanding Decision Tree and Naive Bayes Classification.mp410 MB
04.Understanding and Implementing Classification Models/04.07.Demo Building Classification Models Using Multiple Techniques.mp415 MB
04.Understanding and Implementing Classification Models/04.08.Demo Using Warm Start with an Ensemble Classifier.mp47 MB
04.Understanding and Implementing Classification Models/04.09.Demo Performing Multiclass Classification on Text Data.mp415 MB
04.Understanding and Implementing Classification Models/04.10.Module Summary.mp42 MB
05.Understanding and Implementing Clustering Model/05.01.Module Overview.mp42 MB
05.Understanding and Implementing Clustering Model/05.02.Clustering as an Unsupervised Learning Technique.mp48 MB
05.Understanding and Implementing Clustering Model/05.03.Choosing Clustering Algorithms.mp47 MB
05.Understanding and Implementing Clustering Model/05.04.Categorizing Clustering Algorithms.mp46 MB
05.Understanding and Implementing Clustering Model/05.05.K-means Clustering.mp45 MB
05.Understanding and Implementing Clustering Model/05.06.Hierarchical Clustering.mp47 MB
05.Understanding and Implementing Clustering Model/05.07.Demo Performing K-means Clustering on Unlabeled Data.mp412 MB
05.Understanding and Implementing Clustering Model/05.08.Demo Clustering Using Labeled Data.mp418 MB
05.Understanding and Implementing Clustering Model/05.09.Demo Agglomerative Clustering.mp449 MB
05.Understanding and Implementing Clustering Model/05.10.Summary and Further Study.mp47 MB
Exercise_file.zip8 MB



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