Natural Language Processing for Stocks News Analysis

ofrecido por
Coursera Project Network
En este Proyecto guiado, tú:

Create a pipeline to remove stop-words, perform tokenization and padding

Understand the theory and intuition behind Recurrent Neural Networks and LSTM

Train the deep learning model and assess its performance

Clock2 hours
BeginnerPrincipiante
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

In this hands-on project, we will train a Long Short Term Memory (LSTM) deep learning model to perform stocks sentiment analysis. Natural language processing (NLP) works by converting words (text) into numbers, these numbers are then used to train an AI/ML model to make predictions. In this project, we will build a machine learning model to analyze thousands of Twitter tweets to predict people’s sentiment towards a particular company or stock. The algorithm could be used automatically understand the sentiment from public tweets, which could be used as a factor while making buy/sell decision of securities. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Habilidades que desarrollarás

Python ProgrammingMachine LearningDeep Learningcoding

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  1. Task #1: Understand the Problem Statement and business case 

  2. Task #2: Import libraries and datasets and Perform Exploratory Data Analysis

  3. Task #3: Perform Data Cleaning (Remove Punctuations)

  4. Task #4: Perform Data Cleaning (Remove Stopwords)

  5. Task #5: Plot WordCloud

  6. Task #6: Visualize Cleaned Datasets

  7. Task #7: Prepare the data by tokenizing and padding

  8. Task #8: Understand the theory and intuition behind LSTM

  9. Task #9: Build and train the model

  10. Task #10: Assess trained model performance

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

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