
Quick tips before to start to be more efficient in your learning.
Source: https://www.youtube.com/watch?v=2-VKC8g2u1Y
Youtube channel: SparroW
Learn how to gather seven days of data with the Twitter API. You can find a month of TSLA data in the lecture materials.
Welcome to named entity recognition (NER) in Python!
Introduction to named entity recognition (NER) with spaCy.
How to get access to the Reddit API and authenticate.
How to make a GET request for new posts on a subreddit.
Pulling large batches of data from Reddit (the /r/investing data is attached in the resources).
We will cover applying NER to a set of data and extract the most commonly mentioned organizations on the /r/investing subreddit (data can be found attached in the resources).
Here we will combine what we have learned so far in the course and calculate the average sentiment of organizations extracted from /r/investing data using NER.
Hi there, we are James and Sajid. Both of us are working as data scientists for various banks here in London, and we have both gone a long way before arriving at our current position in the market.
Do you wish to become a data scientist and build yourself a strong portfolio? This course will allow you to develop your Python skills tutored by professionals. You will be able to add Natural Language Processing and Deep Learning to your CV and start getting paid for your skills.
In this course, you will learn how to apply the newest methods in machine learning and natural language processing to predictive analysis of the stock market and cryptocurrency.
Use the latest technologies available such as TensorFlow, PlotLy, HuggingFace's Transformers, Flair, spaCy, and many of the essential classics like Pandas, RegEx, Numpy, and more!
We will cover:
- TensorFlow
- Sentiment Analysis
- Transformers (including Google AI's BERT)
- APIs (including Twitter and Reddit)
- Trading for cryptocurrencies
- Named Entity Recognition (NER)
Take this course if you are learning Python and/or Machine Learning and looking to apply these skills to the stock market. We can't promise to 'fix' on the stock market, but we can promise that you will learn many priceless skills that when applied correctly, can translate to a real benefit both in the job market, and the stock market.
The course is taught by two data scientists from the finance sector. Sajid of Trading 707, who works in Banking and Capital Markets. And James of Aurelio, who specializes in Natural Language Processing (NLP).