The virtual course "Machine Learning: Classification - Virtual Course - Coursera", is a course with different contents and that offers video classes of Approx. 21 hours to complete. Explore its essential features, and click the orange button to get detailed information on the Coursera e-Learning platform
Case Studies: Sentiment Analysis and Loan Default Prediction In our case study on sentiment analysis, you'll create models that predict a class (positive/negative sentiment) from input characteristics (text of reviews, profile information of Username,.
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In our second case study for this course, Loan Default Prediction, you'll tackle financial data and predict when a loan is likely to be risky or safe for the bank.
These tasks are examples of classification, one of the most widely used areas of machine learning, with a wide range of applications, including ad targeting, spam detection, medical diagnosis, and image classification.
In this course, you will create classifiers that provide cutting-edge performance on a variety of tasks.
You will become familiar with the most successful techniques, which are the most used in practice, including logistic regression, decision trees and boosting.
Additionally, you will be able to design and implement the underlying algorithms that can learn these scale models, using stochastic gradient ascent.
You will implement these techniques in real-world, large-scale machine learning tasks.
You'll also address important tasks you'll face in real-world applications of ML, including handling missing data and measuring accuracy 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 each module, covering advanced topics for those who want to dig even deeper! Learning Objectives: By the end of this course, you will be able to: -Describe the input and output of a classification model.
-Address both binary and multiclass classification problems.
-Implement a logistic regression model for large-scale classification.
-Create a nonlinear model using decision trees.
-Improve the performance of any model using boosting.
-Scale your methods with stochastic gradient ascent.
-Describe the underlying decision boundaries.
-Create a ranking model to predict sentiment on 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 the language of your choice, although Python is highly recommended).
-Implement a logistic regression model for large-scale classification.
-Create a nonlinear model using decision trees.
-Improve the performance of any model using boosting.
-Scale your methods with stochastic gradient ascent.
-Describe the underlying decision boundaries.
-Create a ranking model to predict sentiment on 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 the language of your choice, although Python is highly recommended).
-Implement a logistic regression model for large-scale classification.
-Create a nonlinear model using decision trees.
-Improve the performance of any model using boosting.
-Scale your methods with stochastic gradient ascent.
-Describe the underlying decision boundaries.
-Create a ranking model to predict sentiment on 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 the language of your choice, although Python is highly recommended).
-Create a ranking model to predict sentiment on 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 the language of your choice, although Python is highly recommended).
-Create a ranking model to predict sentiment on 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 the language of your choice, although Python is highly recommended).
Prepare yourself from home with the most prestigious universities in the world.
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Data science, business and personal development. You can enroll in multiple courses at once, earn unlimited certificates, and learn in-demand job skills to start, grow, and even change careers.
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*You save up to USD$500 in 12 months, when you go from paying USD$59 for a monthly subscription, to an annual subscription with the promotion. The normal annual subscription is USD $399. With the promotion you will only pay USD $299. Find out everything by clicking the yellow button.
University of Washington
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