Les infos clés
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.
- Week 1 - Welcome
Machine learning is everywhere, but is often operating behind the scenes.
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 dev...
- Week 2 - Regression: Predicting House Prices
This week you will build your first intelligent application that makes predictions from data.
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) ...
- Week 3 - Classification: Analyzing Sentiment
How do you guess whether 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) from input feat...
- Week 4 - Clustering 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 repres...
- Week 5 - Recommending Products
Ever wonder how Amazon forms its personalized product recommendations? How Netflix suggests movies to watch? How Pandora selects the next song to stream? How Facebook or LinkedIn finds people you might connect with? Underlying all of these technologies for...
- Week 6 - Deep Learning: Searching for Images
You’ve probably heard that Deep Learning is making news across the world as one of the most promising techniques in machine learning. Every industry is dedicating resources to unlock the deep learning potential, including for tasks such as image tagging, objec...
- Week 6 - Closing Remarks
In the conclusion of the course, we will describe the final stage in turning our machine learning tools into a service: deployment.
We will also discuss some open challenges that the field of machine learning still faces, and where we think machine learning ...
Coursera est une entreprise numérique proposant des formation en ligne ouverte à tous fondée par les professeurs d'informatique Andrew Ng et Daphne Koller de l'université Stanford, située à Mountain View, Californie.
Ce qui la différencie le plus des autres plateformes MOOC, c'est qu'elle travaille qu'avec les meilleures universités et organisations mondiales et diffuse leurs contenus sur le web.