Этот курс входит в специализацию ''Специализация Machine Learning'
Machine Learning Foundations: A Case Study Approach
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Об этом курсе
4.6
Оценки: 7,691
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.
Приобретаемые навыки
Python ProgrammingMachine Learning ConceptsMachine LearningDeep Learning
Программа курса: что вы изучите
Week
1Welcome
Machine learning is everywhere, but is often operating behind the scenes. <p>This introduction to the specialization provides you with insights into the power of machine learning, and the multitude of intelligent applications you personally will be able to develop and deploy upon completion.</p>We also discuss who we are, how we got here, and our view of the future of intelligent applications.
18 видео (всего 84 мин.), 6 материалов для самостоятельного изучения
18 видео
Who we are5мин
Machine learning is changing the world3мин
Why a case study approach?7мин
Specialization overview6мин
How we got into ML3мин
Who is this specialization for?4мин
What you'll be able to doмин
The capstone and an example intelligent application6мин
The future of intelligent applications2мин
Starting an IPython Notebook5мин
Creating variables in Python7мин
Conditional statements and loops in Python8мин
Creating functions and lambdas in Python3мин
Starting GraphLab Create & loading an SFrame4мин
Canvas for data visualization4мин
Interacting with columns of an SFrame4мин
Using .apply() for data transformation5мин
6 материала для самостоятельного изучения
Important Update regarding the Machine Learning Specialization10мин
Slides presented in this module10мин
Reading: Getting started with Python, IPython Notebook & GraphLab Create10мин
Reading: where should my files go?10мин
Download the IPython Notebook used in this lesson to follow along10мин
Download the IPython Notebook used in this lesson to follow along10мин
Week
2Regression: Predicting House Prices
This week you will build your first intelligent application that makes predictions from data.<p>We will explore this idea within the context of our first case study, predicting house prices, where you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). <p>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.</p>You will also examine how to analyze the performance of your predictive model and implement regression in practice using an iPython notebook.
19 видео (всего 82 мин.), 3 материалов для самостоятельного изучения, 2 тестов
19 видео
What is the goal and how might you naively address it?3мин
Linear Regression: A Model-Based Approach5мин
Adding higher order effects4мин
Evaluating overfitting via training/test split6мин
Training/test curves4мин
Adding other features2мин
Other regression examples3мин
Regression ML block diagram5мин
Loading & exploring house sale data7мин
Splitting the data into training and test sets2мин
Learning a simple regression model to predict house prices from house size3мин
Evaluating error (RMSE) of the simple model2мин
Visualizing predictions of simple model with Matplotlib4мин
Inspecting the model coefficients learned1мин
Exploring other features of the data6мин
Learning a model to predict house prices from more features3мин
Applying learned models to predict price of an average house5мин
Applying learned models to predict price of two fancy houses7мин
3 материала для самостоятельного изучения
Slides presented in this module10мин
Download the IPython Notebook used in this lesson to follow along10мин
Reading: Predicting house prices assignment10мин
2 практического упражнения
Regression18мин
Predicting house prices6мин
Week
3Classification: Analyzing Sentiment
How do you guess whether a person felt positively or negatively about an experience, just from a short review they wrote?<p>In our second case study, analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...).This task is an example 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.</p>You will analyze the accuracy of your classifier, implement an actual classifier in an iPython notebook, and take a first stab at a core piece of the intelligent application you will build and deploy in your capstone.
19 видео (всего 75 мин.), 3 материалов для самостоятельного изучения, 2 тестов
19 видео
What is an intelligent restaurant review system?4мин
Examples of classification tasks4мин
Linear classifiers5мин
Decision boundaries3мин
Training and evaluating a classifier4мин
What's a good accuracy?3мин
False positives, false negatives, and confusion matrices6мин
Learning curves5мин
Class probabilities1мин
Classification ML block diagram3мин
Loading & exploring product review data2мин
Creating the word count vector2мин
Exploring the most popular product4мин
Defining which reviews have positive or negative sentiment4мин
Training a sentiment classifier3мин
Evaluating a classifier & the ROC curve4мин
Applying model to find most positive & negative reviews for a product4мин
Exploring the most positive & negative aspects of a product4мин
3 материала для самостоятельного изучения
Slides presented in this module10мин
Download the IPython Notebook used in this lesson to follow along10мин
Reading: Analyzing product sentiment assignment10мин
2 практического упражнения
Classification14мин
Analyzing product sentiment22мин
Week
4Clustering and Similarity: Retrieving Documents
A reader is interested in a specific news article and you want to find a similar articles to recommend. What is the right notion of similarity? How do I automatically search over documents to find the one that is most similar? How do I quantitatively represent the documents in the first place?<p>In this third case study, retrieving documents, you will examine various document representations and an algorithm to retrieve the most similar subset. You will also consider structured representations of the documents that automatically group articles by similarity (e.g., document topic).</p>You will actually build an intelligent document retrieval system for Wikipedia entries in an iPython notebook.
17 видео (всего 76 мин.), 3 материалов для самостоятельного изучения, 2 тестов
17 видео
What is the document retrieval task?1мин
Word count representation for measuring similarity6мин
Prioritizing important words with tf-idf3мин
Calculating tf-idf vectors5мин
Retrieving similar documents using nearest neighbor search2мин
Clustering documents task overview2мин
Clustering documents: An unsupervised learning task4мин
k-means: A clustering algorithm3мин
Other examples of clustering6мин
Clustering and similarity ML block diagram7мин
Loading & exploring Wikipedia data5мин
Exploring word counts5мин
Computing & exploring TF-IDFs7мин
Computing distances between Wikipedia articles5мин
Building & exploring a nearest neighbors model for Wikipedia articles3мин
Examples of document retrieval in action4мин
3 материала для самостоятельного изучения
Slides presented in this module10мин
Download the IPython Notebook used in this lesson to follow along10мин
Reading: Retrieving Wikipedia articles assignment10мин
2 практического упражнения
Clustering and Similarity12мин
Retrieving Wikipedia articles18мин
4.6
31%
83%
Лучшие рецензии
автор: BL•Oct 17th 2016
Very good overview of ML. The GraphLab api wasn't that bad, and also it was very wise of the instructors to allow the use of other ML packages. Overall i enjoyed it very much and also leaned very much
автор: DS•Sep 28th 2015
Excellent course, with really good lectures, material and assignment. Plus the professors are really amazing and their enthusiasm is really refreshing and makes the class more interesting. Loved it!
Преподавателя
Carlos Guestrin
Amazon Professor of Machine LearningComputer Science and Engineering
Emily Fox
Amazon Professor of Machine LearningStatistics
О University of Washington
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.
О специализации ''Machine Learning'
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.
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