Data Science : Machine Learning and Predictions

Durée : Plus de 6 heures

Catégories :
Niveau des connaissances préalables requises en IA : Aucune connaissance préalable, Connaissances de bases
Niveau des connaissances préalables requises en santé : Aucune connaissance préalable, Connaissances de bases

Langue : Anglais

Accèssibilité : Accès gratuit avec certification payante

Durée : Plus de 6 heures

Sujet : Algorithme (Algorithm), Apprentissage automatique (Machine Learning), Science des données et Intelligence artificielle (Data science & AI)

Description

One of the principal responsibilities of a data scientist is to make reliable predictions based on data. When the amount of data available is enormous, it helps if some of the analysis can be automated. Machine learning is a way of identifying patterns in data and using them to automatically make predictions or decisions. In this data science course, you will learn basic concepts and elements of machine learning.

The two main methods of machine learning you will focus on are regression and classification. Regression is used when you seek to predict a numerical quantity. Classification is used when you try to predict a category (e.g., given information about a financial transaction, predict whether it is fraudulent or legitimate).

For regression, you will learn how to measure the correlation between two variables and compute a best-fit line for making predictions when the underlying relationship is linear. The course will also teach you how to quantify the uncertainty in your prediction using the bootstrap method. These techniques will be motivated by a wide range of examples.

For classification, you will learn the k-nearest neighbour classification algorithm, learn how to measure the effectiveness of your classifier, and apply it to real-world tasks including medical diagnoses and predicting genres of movies.

The course will highlight the assumptions underlying the techniques, and will provide ways to assess whether those assumptions are good. It will also point out pitfalls that lead to overly optimistic or inaccurate predictions.

https://www.edx.org/course/foundations-of-data-science-prediction-and-machine