Machine Learning Projects using scikit-learn

This is a curated collection of Guided Projects for aspiring Data Scientists, Data Analysts and Python and Machine Learning enthusiasts. The Guided Projects in this collection are designed to help you solve a series of real-world problems by applying popular machine learning algorithms using scikit-learn.

Using the scikit-learn library in Python, you will first tackle sentiment analysis, a natural language processing application. You will build a logistic regression model to classify the sentiments of movie reviews as either positive or negative. In the next Guided Project, you will grow decision trees and random forest models to help organizations to predict employee turnover. Having covered logistic regression and tree-based methods, the remaining Guided Projects cover k-means clustering applied to compression and linear regression models to predict sales revenue.

This collection is suitable even if you have never used scikit-learn before. Prior Python programming experience and an interest in applied machine learning is highly recommended.

Machine Learning Projects using scikit-learn

This is a curated collection of Guided Projects for aspiring Data Scientists, Data Analysts and Python and Machine Learning enthusiasts. The Guided Projects in this collection are designed to help you solve a series of real-world problems by applying popular machine learning algorithms using scikit-learn.

Using the scikit-learn library in Python, you will first tackle sentiment analysis, a natural language processing application. You will build a logistic regression model to classify the sentiments of movie reviews as either positive or negative. In the next Guided Project, you will grow decision trees and random forest models to help organizations to predict employee turnover. Having covered logistic regression and tree-based methods, the remaining Guided Projects cover k-means clustering applied to compression and linear regression models to predict sales revenue.

This collection is suitable even if you have never used scikit-learn before. Prior Python programming experience and an interest in applied machine learning is highly recommended.

Perform Sentiment Analysis with scikit-learn

Perform Sentiment Analysis with scikit-learn

Coursera Project Network

projeto guiado
Avaliado como 4.5 de 5 estrelas. 402 análises
Intermediate LevelIntermediate Level

Why use scikit-learn for machine learning?

Built on NumPy, SciPy, and matplotlib, scikit-learn is the prefered Python library by researchers, and seasoned data scientists to apply robust and easy-to-use implementations of popular machine learning algorithms. The diversity of simple and efficient tools for predictive modelling available through scikit-learn makes it the swiss army knife of applied machine learning.

A dedicated team of experts serve as the primary contributors to the scikit-learn codebase. As such, all of its APIs are well documented. What's more is that scikit-learn scales well to most problems, making it an excellent choice for big data analysis.

What is a Guided Project?

A Guided Project helps you learn a job-relevant skill in under 2 hours through an interactive experience with step-by-step instructions from a subject matter expert. Everything you need to complete a Guided Project is available right in your browser. No software or prior experience is required to get started.

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