This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.
About this 전문분야
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59,757귀하가 습득할 기술
Data Clustering AlgorithmsMachine LearningClassification AlgorithmsDecision Tree
How the 전문분야 Works
강좌 수강
Coursera 전문 분야는 기술을 완벽하게 습득하는 데 도움이 되는 일련의 강좌입니다. 시작하려면 전문 분야에 직접 등록하거나 강좌를 둘러보고 원하는 강좌를 선택하세요. 하나의 전문 분야에 속하는 강좌에 등록하면 해당 전문 분야 전체에 자동으로 등록됩니다. 단 하나의 강좌만 수료해도 됩니다. — 학습을 일시 중지하거나 언제든 구독을 종료할 수 있습니다. 학습자 대시보드를 방문하여 강좌 등록 상태와 진도를 추적해 보세요.
실습 프로젝트
모든 전문 분야에는 실습 프로젝트가 포함되어 있습니다. 전문 분야를 완료하고 수료증을 받으려면 프로젝트를 성공적으로 마쳐야 합니다. 전문 분야에 별도의 실습 프로젝트 강좌가 포함되어 있는 경우 각 강좌를 완료해야 프로젝트를 시작할 수 있습니다.
수료증 취득
모든 강좌를 마치고 실습 프로젝트를 완료하면 취업할 때나 전문가 네트워크에 진입할 때 제시할 수 있는 수료증을 취득할 수 있습니다.
이 전문분야에는 4개의 강좌가 있습니다.
강좌1
Machine Learning Foundations: A Case Study Approach
4.6
8,479개의 평가
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?
In this course, you will get hands-on experience with machine learning from a series of practical case-studies. 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. Through hands-on practice with these use cases, you will be able to apply machine learning methods in 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 tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent 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 will use in developing intelligent applications.
Learning Outcomes: By the end of this course, you will be able to:
-Identify potential applications of machine learning in practice.
-Describe the core differences in analyses enabled by regression, classification, and clustering.
-Select the appropriate machine learning task for a potential application.
-Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
-Represent your data as features to serve as input to machine learning models.
-Assess the model quality in terms of relevant error metrics for each task.
-Utilize a dataset 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.
강좌2
Machine Learning: Regression
4.8
4,163개의 평가
Case Study - Predicting Housing Prices
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,...). This is just one of the many places where regression can be applied. 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.
In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets.
Learning Outcomes: By the end of this course, you will be able to:
-Describe the input and output of a regression model.
-Compare and contrast bias and variance when modeling data.
-Estimate model parameters using optimization algorithms.
-Tune parameters with cross validation.
-Analyze the performance of the model.
-Describe the notion of sparsity and how LASSO leads to sparse solutions.
-Deploy methods to select between models.
-Exploit the model to form predictions.
-Build a regression model to predict prices using a housing dataset.
-Implement these techniques in Python.
강좌3
Machine Learning: Classification
4.7
2,715개의 평가
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).
강좌4
Machine Learning: Clustering & Retrieval
4.6
1,657개의 평가
Case Studies: Finding Similar Documents
A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover?
In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce.
Learning Outcomes: By the end of this course, you will be able to:
-Create a document retrieval system using k-nearest neighbors.
-Identify various similarity metrics for text data.
-Reduce computations in k-nearest neighbor search by using KD-trees.
-Produce approximate nearest neighbors using locality sensitive hashing.
-Compare and contrast supervised and unsupervised learning tasks.
-Cluster documents by topic using k-means.
-Describe how to parallelize k-means using MapReduce.
-Examine probabilistic clustering approaches using mixtures models.
-Fit a mixture of Gaussian model using expectation maximization (EM).
-Perform mixed membership modeling using latent Dirichlet allocation (LDA).
-Describe the steps of a Gibbs sampler and how to use its output to draw inferences.
-Compare and contrast initialization techniques for non-convex optimization objectives.
-Implement these techniques in Python.
강사
Carlos Guestrin
Amazon Professor of Machine LearningComputer Science and Engineering
Emily Fox
Amazon Professor of Machine LearningStatistics
산업 파트너:
워싱턴 대학교 정보
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 preeminent research universities in the world.
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전문 분야를 완료하면 대학 학점을 받을 수 있나요?
이 전문 분야는 대학 학점을 제공하지 않지만, 일부 대학에서 선택적으로 전문 분야 인증서를 학점으로 인정할 수도 있습니다. 자세한 내용은 해당 기관에 문의하세요.
How long does it take to complete the Machine Learning Specialization?
Time to completion can vary based on your schedule, but most learners are able to complete the Specialization in about 8 months.
얼마나 자주 전문 분야의 강좌가 제공되나요?
Each course in the Specialization is offered on a regular schedule, with sessions starting about once per month. If you don't complete a course on the first try, you can easily transfer to the next session, and your completed work and grades will carry over.
What background knowledge is necessary?
You should have some experience with computer programming; most assignments in this Specialization will use the Python programming language. This Specialization is designed specifically for scientists and software developers who want to expand their skills into data science and machine learning, but is appropriate for anyone with basic math and programming skills and an interest in deriving intelligence from data.
Do I have to take the courses in this Specialization in a specific order?
We recommend taking the courses in the order presented, as each subsequent course will build on material from previous courses.
Will I earn university credit for completing the Machine Learning Specialization?
Coursera courses and certificates don't carry university credit, though some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.
What will I be able to do upon completing the Machine Learning Specialization?
You will be able to use machine learning techniques to solve complex real-world problems, by identifying the right method for your task, implementing an algorithm, assessing and improving the algorithm’s performance, and deploying your solution as a service.
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