Creating Machine Learning Models

Focused View

Janani Ravi

2:43:57

8 View

01.Course Overview

01.01.Course Overview.mp4

01:40

02.Understanding Approaches to Machine Learning

02.01.Module Overview.mp4

01:23

02.02.Prerequisites and Course Outline.mp4

01:15

02.03.Rule-based vs. ML-based Learning.mp4

07:47

02.04.Traditional ML vs. Representation ML.mp4

03:48

02.05.The Machine Learning Workflow.mp4

03:05

02.06.Choosing the Right Model Based on Data.mp4

05:52

02.07.Supervised vs. Unsupervised Learning.mp4

05:02

02.08.Transfer Learning, Cold Start ML and Warm Start ML.mp4

05:29

02.09.Popular Machine Learning Frameworks.mp4

03:33

02.10.Demo Getting Started with scikit-learn.mp4

01:58

02.11.Module Summary.mp4

01:34

03.Understanding and Implementing Regression Models

03.01.Module Overview.mp4

01:15

03.02.Building and Evaluating Regression Models.mp4

05:17

03.03.Demo Linear Regression Using Numeric Features.mp4

07:29

03.04.Demo Exploring Regression Data.mp4

04:14

03.05.Demo Preprocessing Numeric and Categorical Data and Fitting a Regression Model.mp4

04:17

03.06.Choosing Regression Algorithms.mp4

02:41

03.07.Regularized Regression Models Lasso, Ridge, and Elastic Net.mp4

04:26

03.08.Stochastic Gradient Descent.mp4

02:35

03.09.Demo Multiple Types of Regression.mp4

05:13

03.10.Module Summary.mp4

01:26

04.Understanding and Implementing Classification Models

04.01.Module Overview.mp4

01:16

04.02.Types of Classifiers.mp4

04:30

04.03.Understanding Logistic Regression Intuitively.mp4

05:30

04.04.Demo Building and Training a Binary Classification Model.mp4

05:11

04.05.Understanding Support Vector and Nearest Neighbors Classification.mp4

04:25

04.06.Understanding Decision Tree and Naive Bayes Classification.mp4

05:42

04.07.Demo Building Classification Models Using Multiple Techniques.mp4

06:32

04.08.Demo Using Warm Start with an Ensemble Classifier.mp4

02:44

04.09.Demo Performing Multiclass Classification on Text Data.mp4

05:53

04.10.Module Summary.mp4

01:11

05.Understanding and Implementing Clustering Model

05.01.Module Overview.mp4

01:16

05.02.Clustering as an Unsupervised Learning Technique.mp4

02:57

05.03.Choosing Clustering Algorithms.mp4

03:49

05.04.Categorizing Clustering Algorithms.mp4

03:10

05.05.K-means Clustering.mp4

02:50

05.06.Hierarchical Clustering.mp4

03:46

05.07.Demo Performing K-means Clustering on Unlabeled Data.mp4

05:07

05.08.Demo Clustering Using Labeled Data.mp4

07:34

05.09.Demo Agglomerative Clustering.mp4

07:46

05.10.Summary and Further Study.mp4

01:29

Description

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.

What You'll Learn?

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.

More details

User Reviews

Rating

average 0

Total votes0

Focused display

Machine Learning

Janani Ravi

Instructor's Courses

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. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing high-quality content for technical skill development. Loonycorn is working on developing an engine (patent filed) to automate animations for presentations and educational content.

Pluralsight

View courses Pluralsight

Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.