Text Analysis and Natural Language Processing With Python

Text Analysis and Natural Language Processing With Python

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Use Python and Google CoLab For Social Media Mining and Text Analysis and Natural Language Processing (NLP) What you’ll learn:
Students will be able to read in data from different sources- including websites and social media
Social media mining from Twitter
Extract information relating to tweets and posts
Analyze text data for emotions
Carry out Sentiment analysis
Implement natural language processing (NLP) on different types of text data
Introduction to some of the most common Python text analysis packages

Requirements
Should have prior experience of Python data science
Prior experience of statistical and machine learning techniques will be beneficial
Should have an interest in extracting unstructured text data from social media and websites
Should have an interest in extracting qinsights from text analysis
Should have an interest in applying machine learning models on text data

Description
ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT PYTHON SOCIAL MEDIA & NATURAL LANGUAGE PROCESSING (NLP)

Do you want to harness the power of social media to make financial decisions?

Are you looking to gain an edge in the fields of retail, online selling, real estate and geolocation services?

Do you want to turn unstructured data from social media and web pages into real insights?

Do you want to develop cutting edge analytics and visualisations to take advantage of the millions of Twitter posts that appear each day?

Gaining proficiency in social media mining can help you harness the power of the freely available data and information on the world wide web (including popular social media sites such as Twitter) and turn it into actionable insights

MY COURSE IS A HANDS-ON TRAINING WITH REAL PYTHON SOCIAL MEDIA MINING- You will learn to carry out text analysis and natural language processing (NLP) to gain insights from unstructured text data, including tweets

My course provides a foundation to carry out PRACTICAL, real-life social media mining. By taking this course, you are taking an important step forward in your data science journey to become an expert in harnessing the power of social media for deriving insights and identifying trends.

Why Should You Take My Course?

I have an MPhil (Geography and Environment) from the University of Oxford, UK. I also completed a data science intense PhD at Cambridge University (Tropical Ecology and Conservation).

I have several years of experience in analyzing real-life data from different sources and producing publications for international peer-reviewed journals.

This course will help you gain fluency both in the different aspects of text analysis and NLP working through a real-life example of cryptocurrency tweets and financial news using a powerful clouded based python environment called GoogleColab. Specifically, you will

Gain proficiency in setting up and using Google CoLab for Python Data Science tasks

Carry out common social media mining tasks such as obtaining tweets (e.g. tweets relating to bitcoins)

Work with complicated web pages and extract information

Process the extracted textual information in a usable form via preprocessing techniques implemented via powerful Python packages such as NTLK

A thorough grounding in text analysis and NLP related Python packages such as NTLK, Snscrape among others

Carry out common text analytics tasks such as Sentiment Analysis

Implement machine learning and artificial intelligence techniques on text data

You will work on practical mini case studies relating to (a) extracting and pre-processing tweets from certain users and topics relating to cryptocurrencies (b) identify the sentiments of cryptocurrency tweets(c) classify your tweets using machine learning models

In addition to all the above, you’ll have MY CONTINUOUS SUPPORT to make sure you get the most value out of your investment!

Who this course is for
People who wish to learn practical text mining and natural language processing
People who wish to derive insights from textual and social media data
People wanting to understand the impact of human sentiments on financial markets

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