Summary, Description
A new ALL TIME HIGH has just hit Bitcoin (BTC) price! #cryptocurrency #bitcoin
It seems pretty clear to you and me that this is positive Bitcoin news, isn’t it? But is it so easy for a machine to understand it?… Perhaps not… Well, this is precisely what this course is about: learning how to build a model of Machine Learning that can read and classify all this news for us!
Twitter has been an ever-growing source of intelligence since 2006, keeping us updated on anything and nothing. It is estimated that every second more than 6,000 tweets are shared on the site, making it an inexhaustible mine of data that it would be a shame not to utilise.
What are you going to learn during this course?
You will learn all the steps required to develop your own model of Tweet Sentiment prediction by taking this course. That said, when the course is divided into 4 distinct sections, connected together, but offering its share of expertise in a specific area, you will learn much more (Text Mining, NLP and Machine Learning).
Chapter 1: Text Mining Introduction
We will go through some general elements in this first segment, setting up the starting problem and the numerous problems to be solved with text data. This is also the segment in which, using libraries like Pandas and Matplotlib, we can discover our Twitter dataset.
SECTION 2: Normalizing Text
Data on Twitter is considered to be very sloppy. Using Text Mining techniques and some relevant libraries like NLTK, this section will try to clean up all our tweets in depth. If you are finished with this section, tokenization, stemming or lemmatization would not have any secrets for you.
SECTION 3: Representation of Text
We would need to learn how to interpret it the correct way before our cleansed data can be fed to our model. The goal of this section is to cover numerous approaches related to this function and also used in NLP (Bag-of-Words, TF-IDF, etc.). This will provide us with an extra chance to use NLTK.
SECTION 4: ML Modelling Modelling
Ultimately… The most enthralling move of all! In order to build our Sentiment prediction model, this segment will be about bringing together all that we have studied. Among all, it will be about getting an incentive in Machine Learning to use one of the most used libraries: Scikit-Learn (SKLEARN).
For whom this course is intended:
Anyone with a background in NLP and Artificial Intelligence
Anyone interested in understanding what Text Mining is and how it can be used
Anyone eager to learn how to easily predict any tweet’s feelings
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