机器学习基础：案例研究

你将学到什么

课程概况

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.

课程大纲

Week 1 Welcome

Why you should learn machine learning with us
Who this specialization is for and what you will be able to do
Getting started with the tools for the course
Getting started with Python and the IPython Notebook
Getting started with SFrames for data engineering and analysis

Week 2 Regression: Predicting House Prices

Linear regression modeling
Evaluating regression models
Summary of regression
Predicting house prices: IPython Notebook
Programming assignment

Quiz: Regression
Quiz: Predicting house prices

Week 3 Classification: Analyzing Sentiment

Classification modeling
Evaluating classification models
Summary of classification
Analyzing sentiment: IPython Notebook
Programming assignment

Quiz: Classification
Quiz: Analyzing product sentiment

Week 4 Clustering and Similarity: Retrieving Documents

Algorithms for retrieval and measuring similarity of documents
Clustering models and algorithms
Summary of clustering and similarity
Document retrieval: IPython Notebook
Programming assignment

Quiz: Clustering and Similarity
Quiz: Retrieving Wikipedia articles

Week 5 Recommending Products

Recommender systems
Co-occurrence matrices for collaborative filtering
Matrix factorization
Performance metrics for recommender systems
Summary of recommender systems
Song recommender: IPython Notebook
Programming assignment

Quiz: Recommender Systems
Quiz: Recommending songs

Week 6 Deep Learning: Searching for Images

Closing Remarks
Neural networks: Learning very non-linear features
Deep learning & deep features
Summary of deep learning
Deep features for image classification: IPython Notebook
Deep features for image retrieval: IPython Notebook
Programming assignment
Deploying machine learning as a service
Machine learning challenges and future directions

Quiz: Deep Learning
Quiz: Deep features for image retrieval