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  • Provider Coursera
  • Subject Machine Learning
  • $ Cost Free Online Course (Audit)
  • Session In progress
  • Language English
  • Certificate Paid Certificate Available
  • Start Date
  • Duration 7 weeks long

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Overview

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Case Studies: Analyzing Sentiment & Loan Default Prediction

In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification.

In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. We've also included optional content in every module, covering advanced topics for those who want to go even deeper!

Learning Objectives: By the end of this course, you will be able to:
-Describe the input and output of a classification model.
-Tackle both binary and multiclass classification problems.
-Implement a logistic regression model for large-scale classification.
-Create a non-linear model using decision trees.
-Improve the performance of any model using boosting.
-Scale your methods with stochastic gradient ascent.
-Describe the underlying decision boundaries.
-Build a classification model to predict sentiment in a product review dataset.
-Analyze financial data to predict loan defaults.
-Use techniques for handling missing data.
-Evaluate your models using precision-recall metrics.
-Implement these techniques in Python (or in the language of your choice, though Python is highly recommended).

Taught by

Carlos Guestrin and Emily Fox

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Reviews for Coursera's Machine Learning: Classification
4.8 Based on 8 reviews

  • 5 stars 75%
  • 4 stars 25%
  • 3 star 0%
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  • 1
Gregory S
4.0 3 years ago
by Gregory completed this course.


Machine Learning: Classification is the third course in the 6-part machine learning specialization offered by the University of Washington on the Coursera MOOC platform. The first two weeks of the 7-week course discuss classification in general, logistic regression and controlling overfitting with regularization. Weeks 3 and 4 cover decision trees, methods to control overfitting in tree models and handling missing data. Week 5 discusses boosting as an ensemble learning method in the context of decision trees. Weeks 6 and 7 cover precision and recall as alternatives to accuracy fo…
4 people found
this review helpful
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Zelphir K
4.0 2 years ago
by Zelphir completed this course, spending 10 hours a week on it and found the course difficulty to be medium.
This course can really teach you about classification approaches and problems you might encounter. The quiz felt like challenges, but they were all doable and made me feel good about completing them. Not too easy and not too difficult. I spent a lot of time each week of the course, but then again I am a perfectionist and the amount of time I spend on a course also contains taking my own notes, making them digitally online available, doing revision to be sure I understood everything, looking up and learning about formulas and the why behind them, etc.

If you take this course seriou…
Was this review helpful to you? Yes
Jason C
5.0 3 years ago
by Jason completed this course, spending 5 hours a week on it and found the course difficulty to be hard.
This continues UWash's outstanding Machine Learning series of classes, and is equally as impressive, if not moreso, then the Regression class it follows. I'm super-excited for the next class!
1 person found
this review helpful
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Daniel R
5.0 3 years ago
by Daniel completed this course, spending 10 hours a week on it and found the course difficulty to be medium.
The course is great, as the others in this specializations, they really make it simple, but they are not going deeper in the algorithms, however it is really great!
Was this review helpful to you? Yes
Pankaj K
5.0 3 years ago
by Pankaj completed this course.
0 person found
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Colin K
5.0 3 years ago
by Colin completed this course.
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Abhilash V
5.0 2 years ago
by Abhilash completed this course.
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Farmy R
5.0 2 years ago
by Farmy completed this course.
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  • 1

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