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The course is from one of the top universities of the world - University of Washington.

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This course is costly - Rs. 4439/-.

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60% of the students have found this course difficult.

Content

84% of the students have liked the content of this course.

Assignments

80% of the students have liked the assignments of this course.

Teaching

86% of the students have liked how the instructor has taught this course.

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86% of the students are overall satisfied with this course.

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Machine Learning: Regression

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36 hours
English
University of Washington
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For Rs. 4439
Intermediate
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Checklist

Certification

You will get a certificate on completing this course.

University

The course is from one of the top universities of the world - University of Washington.

Price

This course is costly - Rs. 4439/-.

Difficulty

60% of the students have found this course difficult.

Content

84% of the students have liked the content of this course.

Assignments

80% of the students have liked the assignments of this course.

Teaching

86% of the students have liked how the instructor has taught this course.

Satisfaction

86% of the students are overall satisfied with this course.

Edvicer's Rewards

You can get a cashback of ₹ 300 on buying this course.

Why should you choose this course?

Description

Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms.). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data - such as outliers - on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Analyze the performance of the model. -Describe the notion of sparsity and how LASSO leads to sparse solutions. -Deploy methods to select between models. -Exploit the model to form predictions. -Build a regression model to predict prices using a housing dataset. -Implement these techniques in Python.

Syllabus

Welcome
Simple Linear Regression
Multiple Regression
Assessing Performance
Ridge Regression
Feature Selection & Lasso
Nearest Neighbors & Kernel Regression
Closing Remarks

What others say about this course

Reviews from Coursera

I enrolled in this specialization to learn machine learning using GraphLab Create. Half way into the specialization the creators sold Turi, GrapLab's parent company, making it non available to the gene  Read More ...

This is an excellent course. The presentation is clear, the graphs are very informative, the homework is well-structured and it does not beat around the bush with unnecessary theoretical tangents.

I leave 2 stars as I learned a lot of new information and methods, and the theory and math behind them.You will learn about Data Science and Machine Learning, but not much about Python.The course is pr  Read More ...

Be aware that this course is from 2015. The videos are a good foundation, but they are old. The homework assignments use a proprietary python library (graphlabcreate/Turicreate) that is not useful ou  Read More ...

I really like the top-down approach of this specialization. The iPython code assignments are very well structured. They are presented in a step-by-step manner while still being challenging and fun!

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