![]() ![]() 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.). Build a regression model to predict prices using a housing dataset.Ĭase Studies: Analyzing Sentiment & Loan Default Prediction Deploy methods to select between models. Describe the notion of sparsity and how LASSO leads to sparse solutions. Estimate model parameters using optimization algorithms. Compare and contrast bias and variance when modeling data. ![]() Describe the input and output of a regression model. To fit these models, you will implement optimization algorithms that scale to large datasets. You will also analyze the impact of aspects of your data - such as outliers - on your selected models and predictions. You will be able to handle very large sets of features and select between models of various complexity. ![]() In this course, you will explore regularized linear regression models for the task of prediction and feature selection. 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. This is just one of the many places where regression can be applied. 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.). Build an end-to-end application that uses machine learning at its core. Utilize a dataset to fit a model to analyze new data. Assess the model quality in terms of relevant error metrics for each task. Represent your data as features to serve as input to machine learning models. Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. Select the appropriate machine learning task for a potential application. Describe the core differences in analyses enabled by regression, classification, and clustering. Identify potential applications of machine learning in practice. Learning Outcomes: By the end of this course, you will be able to: Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. This first course treats the machine learning method as a black box. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. In this course, you will get hands-on experience with machine learning from a series of practical case-studies. Commercial reproduction, distribution or transmission of any part or parts of this website or any information contained therein by any means whatsoever without the prior written permission of the Club is not permitted.Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? This website is the only official website of the New England Patriots and is © Copyright New England Patriots (the "Club"). ![]()
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