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Do you have data and wonder what it can tell you? Need a deeper understanding of the top ways machine learning can improve your business? Want to be able to chat with specialists on anything from regression and classification to deep learning and recommender systems? In this course, you'll gain hands-on experience with machine learning through a series of practical case studies. By the end of the first course, you will have studied how to predict house prices based on
Do you have data and wonder what it can tell you? Need a deeper understanding of the top ways machine learning can improve your business? Want to be able to chat with specialists on anything from regression and classification to deep learning and recommender systems? In this course, you'll gain hands-on experience with machine learning through a series of practical case studies.
By the end of the first course, you will have studied how to predict home prices based on home-level features, analyze user sentiment, retrieve documents of interest, recommend products, and search for images.
Through hands-on practice with these use cases, you'll be able to apply machine learning methods across a wide range of domains.
This first course treats the machine learning method as a black box.
Using this abstraction, you will focus on understanding the tasks of interest, matching these tasks with machine learning tools, and evaluating the quality of the result.
In later courses, you will delve into the components of this black box by examining models and algorithms.
Together, these pieces form the machine learning pipeline, which you'll use in developing intelligent applications.
Learning Outcomes: At the end of this course, you will be able to: -Identify potential applications of machine learning in practice.
-Describe the central differences in analyzes enabled by regression, classification and clustering.
-Select the appropriate machine learning task for a potential application.
-Apply regression, classification, clustering, retrieval, recommendation systems, and deep learning.
-Represent your data as features to serve as input to machine learning models.
-Evaluate the quality of the model in terms of error metrics relevant to each task.
-Use a data set to fit a model to analyze new data.
-Build an end-to-end application that uses machine learning at its core.
-Implement these techniques in Python.
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Machine learning is everywhere, but it often works behind the scenes. This introduction to the specialization educates you on the power of machine learning and the multitude of intelligent applications that you can personally develop and deploy upon completion. We also discuss who we are, how we got here, and our vision for the future of smart apps.
This week, you'll create your first smart app that makes predictions from data. We'll explore this idea within the context of our first case study, Predicting House Prices, where you'll create models that predict a continuous value (price) from Input Characteristics (square footage, number of bedrooms and bathrooms, ...). This is just one of many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and energy use in high-performance computing, to analyzing which regulators are important for gene expression. You will also examine how to analyze the performance of your predictive model and implement regression in practice using a Jupyter notebook.
How do you tell if a person felt positively or negatively about an experience, just from a short review they wrote? In our second case study, analyzing sentiment, you will create models that predict a class (positive/negative sentiment) of input features (review text, user profile information, ...). This task is an example 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. You'll analyze the accuracy of your classifier, implement an actual classifier in a Jupyter notebook, and test for the first time a core piece of the smart app that you'll create and implement in your capstone.
A reader is interested in a specific news article and wants to find similar articles to recommend. What is the correct notion of similarity? How do I automatically search the documents to find the closest match? How do I quantitatively represent the documents in the first place? In this third case study, Retrieving Documents, you'll examine various document representations and an algorithm for retrieving the most similar subset. You'll also consider structured representations of documents that automatically group articles by similarity (for example, the topic of the document). In fact, it will create a smart document retrieval system for Wikipedia entries in a Jupyter notebook.
Ever wonder how Amazon forms its personalized product recommendations? How does Netflix suggest movies to watch? How does Pandora select the next song to stream? How does Facebook or LinkedIn find people you could connect with? Behind all these technologies for personalized content is something called collaborative filtering. You'll learn how to build such a recommendation system using a variety of techniques and explore its trade-offs. One method we examine is matrix factorization, which learns the characteristics of users and products to form recommendations. In a Jupyter notebook, you'll use these techniques to create an actual song recommendation system.
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Amazon Professor of Machine Learning
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Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the world's leading research universities.
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