Pluralsight | Creating Machine Learning Models [FCO]

  • CategoryOther
  • TypeTutorials
  • LanguageEnglish
  • Total size390.9 MB
  • Uploaded BySunRiseZone
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  • Last checkedMar. 26th '20
  • Date uploadedMar. 25th '20
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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.



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[FreeCoursesOnline.Me] Pluralsight - Creating Machine Learning Models 0. Websites you may like
  • 0. (1Hack.Us) Premium Tutorials-Guides-Articles & Community based Forum.url (0.4 KB)
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01.Course Overview
  • 01.01.Course Overview.mp4 (9.9 MB)
02.Understanding Approaches to Machine Learning
  • 02.01.Module Overview.mp4 (6.5 MB)
  • 02.02.Prerequisites and Course Outline.mp4 (4.8 MB)
  • 02.03.Rule-based vs. ML-based Learning.mp4 (13.6 MB)
  • 02.04.Traditional ML vs. Representation ML.mp4 (7.0 MB)
  • 02.05.The Machine Learning Workflow.mp4 (6.0 MB)
  • 02.06.Choosing the Right Model Based on Data.mp4 (10.5 MB)
  • 02.07.Supervised vs. Unsupervised Learning.mp4 (9.3 MB)
  • 02.08.Transfer Learning, Cold Start ML and Warm Start ML.mp4 (10.1 MB)
  • 02.09.Popular Machine Learning Frameworks.mp4 (6.5 MB)
  • 02.10.Demo Getting Started with scikit-learn.mp4 (4.4 MB)
  • 02.11.Module Summary.mp4 (2.7 MB)
03.Understanding and Implementing Regression Models
  • 03.01.Module Overview.mp4 (2.4 MB)
  • 03.02.Building and Evaluating Regression Models.mp4 (9.3 MB)
  • 03.03.Demo Linear Regression Using Numeric Features.mp4 (17.6 MB)
  • 03.04.Demo Exploring Regression Data.mp4 (9.7 MB)
  • 03.05.Demo Preprocessing Numeric and Categorical Data and Fitting a Regression Model.mp4 (10.5 MB)
  • 03.06.Choosing Regression Algorithms.mp4 (4.9 MB)
  • 03.07.Regularized Regression Models Lasso, Ridge, and Elastic Net.mp4 (7.4 MB)
  • 03.08.Stochastic Gradient Descent.mp4 (4.4 MB)
  • 03.09.Demo Multiple Types of Regression.mp4 (12.0 MB)
  • 03.10.Module Summary.mp4 (2.6 MB)
04.Understanding and Implementing Classification Models
  • 04.01.Module Overview.mp4 (2.3 MB)
  • 04.02.Types of Classifiers.mp4 (8.0 MB)
  • 04.03.Understanding Logistic Regression Intuitively.mp4 (10.0 MB)
  • 04.04.Demo Building and Training a Binary Classification Model.mp4 (13.8 MB)
  • 04.05.Understanding Support Vector and Nearest Neighbors Classification.mp4 (7.5 MB)
  • 04.06.Understanding Decision Tree and Naive Bayes Classification.mp4 (9.9 MB)
  • 04.07.Demo Building Classification Models Using Multiple Techniques.mp4 (15.5 MB)
  • 04.08.Demo Using Warm Start with an Ensemble Classifier.mp4 (6.6 MB)
  • 04.09.Demo Performing Multiclass Classification on Text Data.mp4 (14.9 MB)
  • 04.10.Module Summary.mp4 (2.0 MB)
05.Understanding and Implementing Clustering Model
  • 05.01.Module Overview.mp4 (2.2 MB)
  • 05.02.Clustering as an Unsupervised Learning Technique.mp4 (7.6 MB)
  • 05.03.Choosing Clustering Algorithms.mp4 (7.4 MB)
  • 05.04.Categorizing Clustering Algorithms.mp4 (5.8 MB)
  • 05.05.K-means Clustering.mp4 (5.0 MB)
  • 05.06.Hierarchical Clustering.mp4 (6.8 MB)
  • 05.07.Demo Performing K-means Clustering on Unlabeled Data.mp4 (11.9 MB)
  • 05.08.Demo Clustering Using Labeled Data.mp4 (18.2 MB)
  • 05.09.Demo Agglomerative Clustering.mp4 (49.2 MB)
  • 05.10.Summary and Further Study.mp4 (6.6 MB)
  • Exercise_file.zip (8.1 MB)

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