Natural Language Processing for Stocks News Analysis
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
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.
Skills you will develop
- Python Programming
- Machine Learning
- Deep Learning
- coding
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Task #1: Understand the Problem Statement and business case
Task #2: Import libraries and datasets and Perform Exploratory Data Analysis
Task #3: Perform Data Cleaning (Remove Punctuations)
Task #4: Perform Data Cleaning (Remove Stopwords)
Task #5: Plot WordCloud
Task #6: Visualize Cleaned Datasets
Task #7: Prepare the data by tokenizing and padding
Task #8: Understand the theory and intuition behind LSTM
Task #9: Build and train the model
Task #10: Assess trained model performance
How Guided Projects work
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
Reviews
TOP REVIEWS FROM NATURAL LANGUAGE PROCESSING FOR STOCKS NEWS ANALYSIS
Excellent facilitation,usecase and environment.Looking forward to see many many more use case projects in Financial area.
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