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Interpreting Data with Advanced Statistical Models

Interpreting Data with Advanced Statistical Models

1425/month
Start anytime
Self paced
English
Course by
PluralsightCourses from Pluralsight
Certificate awarded
Advanced
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Not sure which job profiles this course will open for you? Check out our AI based tool to get a complete personalized career map.

Checklist

Certification

You will get a certificate on completing this course.

University

This course is not affiliated with any university.

Price

This course costs very less.

Edvicer's Rewards

You can get a cashback of ₹ 100 on buying this course.

Why should you choose this course?

Description

Machine Learning is changing the world and at the very core of machine learning are advanced statistical models. With this course, you will know how to create an ML application for problems that appear at your work and understand the basis behind it

Syllabus

Course Overview
Demo
Getting Started with Machine Learning?
Unsupervised Learning: I Don’t Wanna Miss a Thing!
Summary
Supervised Learning: When We Learn from History
How It All Fits: Going Back to the Model!
Summary
How to Learn in Machine Learning? Cost Functions!
Demo: Implementing GD and SGD
Making Things Faster: Feature Scaling and Learning Rates
Finding the Minima: GD and SGD
What if I Want More Variables? Multiple Regression to the Rescue!
What No One Talks About: Assumptions
Demo: Perform Multiple Linear Regression
Back to the Basics: Linear Regression Again
Hyperparameter Optimization: Train/Dev/Test Sets
Demo: Evaluate a Regression Model
Summary
Demo: Perform Simple Linear Regression
Summary
Overfitting: A Great Responsibility Conveys a Great Regularization
Demo: Perform a Spline Regression
Demo: Linear Regression with Regularization
Demo: Perform Polynomial Regression
Model Selection: Let the Simplest Model Win
Non-linear Regression: Polynomial Features
Outliers Strike Again: Spline Regression as Local Regressor
Demo: Comparing Models
What Does the Boundary Look Like?
Classify Multiple Categories: One vs. All Classification
If You Need to Classify, Try the Star: Logistic Regression in Depth!
Demo: Classify with Logistic Regression
Demo: Multiclass Classification with Multinomial Logistic Regression
Summary
Demo: Classify Iris with SVM
Demo: Multilabel Intent Classification with Naive Bayes
Maximizing the Actual Information: Bayesian Attack!
Large Margin Classification: Outliers!
Summary
Passing from Linear Boundaries to Nonlinear Ones: Kernel Trick
Demo: Breast Cancer at a Glance
Summary
Distance and Covariance Matrices
Demo: Perform PCA
Compression: PCA and CA
Clustering: Hierarchical and Non-hierarchical

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