Natural Language Processing for Stocks News Analysis

提供方
Coursera Project Network
在此指导项目中,您将:

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
Beginner初级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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.

您要培养的技能

  • Python Programming
  • Machine Learning
  • Deep Learning
  • coding

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  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

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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