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Use techniques for handling missing data. Analyze financial data to predict loan defaults. Build a classification model to predict sentiment in a product review dataset. Describe the underlying decision boundaries. Scale your methods with stochastic gradient ascent. Improve the performance of any model using boosting. Create a non-linear model using decision trees.
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Implement a logistic regression model for large-scale classification. Tackle both binary and multiclass classification problems. Describe the input and output of a classification model. Learning Objectives: By the end of this course, you will be able to: We've also included optional content in every module, covering advanced topics for those who want to go even deeper!
USNG MACHINE LEARNING TO CONVERT HANDWRITING TO WORD FULL
This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. 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. You will implement these technique on real-world, large-scale machine learning tasks. 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 become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. 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 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. 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.). Case Studies: Analyzing Sentiment & Loan Default Prediction