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Text Analysis and Natural Language Processing With Python (Udemy.com)

Use Python and Google CoLab For Social Media Mining and Text Analysis and Natural Language Processing (NLP)

Created by: Minerva Singh

Last updated November 2024

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What you will 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

How it compares

Against the other 126 rated Udemy courses in Deep Learning on CourseDuck:

  • Rating4.3 out of 5higher than 18% of them
  • Reviews550#63 of 127 by number of reviews
  • Length5 hoursshorter than 76% of them
  • Last updatedNovember 2024more recently than 53% of them

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Course 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!

ENROLL NOW :)

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Instructor Details

Minerva Singh

I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics.

I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly

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Reviews

4.3

550 ratings on Udemy

Select a bar to show only those reviews.Select the bar again to show every rating.

By Jaylord L Ybanez on 11/14/2025

This course gave me a solid foundation in Natural Language Processing. I loved how practical the lessons were — I could follow along and build my own text analysis projects easily.

By Marco Finardi on 7/31/2024

This course is a confused patchwork of materials that are not helpful to actually learn something. The value is to have a first introduction about a set of topics, with many doubts remaining unexplored. Many lines of coding, functions and packages are not explained and the logic is hard to follow. Also, the materials provided are not adequate and partially missing.

By Alison Lanski on 6/26/2024

Section 2 is great, with discussion of basic concepts and translation into code. Section 4/5 code doesn't work anymore because terms of service on the sites have been updated and the data is unavailable or paywalled. Section 6/7/8 are ok (the code works) but cleaning processes of the exact same data changes from video to video with no explanation of why. The videos start turning into recitations of the code without much explanation of what/why you are writing it in this way. Section 9 is only useful if you are previously familiar with the models presented and want to see a quick example of their application to text. There is little explanation of choosing good parameters or why one version of a model (or cleaning before a model) might be more effective than another. The code is also inconsistent (a 3-cluster model looks best, but the code is run for 5 clusters), and the videos about Keras are so blurry that they are functionally useless. Section 10 is very random, with only one remotely helpful video (Summarization) which again is mostly reading code off the screen. One of the videos has no sound or transcript (Cleaning), and another (ChatGPT) is only 11 seconds with no content. So yes, you can learn some things about text cleaning and basic analysis (counting, ngrams, plotting, sentiment, clustering) --- but you may not understand what you are doing, why, or how to adjust the code to meet your needs without extensive additional effort.

By Jabir al Mursalin on 1/13/2022

As a beginner, I found this course very interesting. It is triggering my interest in learning more about text analysis. However, some videos in the later section (specifically section 9) need some revision. It will be great if the instructor takes some more time to explain important steps rather than reading out the codes.

By Raveena Sarkar on 11/15/2021

The course is an embodiment of excellence in respect of social media mining, text analysis and natural language processing using Python. The concept can be very useful and effective for practical applications. The instructor's knowledge of the subject is impressive and her delivery is interesting and engaging.

By Ravi Srivastava on 11/13/2021

This is an excellent course bringing out the importance and utility of Python for social media mining, text analysis and natural language processing. The tools can be of immense importance in the practical use of the concept. The instructor is brilliant through her lectures in explaining details of the concept.

By Rishilal Yadav on 8/17/2021

The course discussing usage of Python in text analysis, data mining and natural language processing is beyond my expectations. Using the concept for practical work will make the task so very easy with accurate results. I hope to significantly get benefitted using this concept for my work. The instructor's knowledge of the concept is impressive and her delivery effective.

By Raj Kumar Majumdar on 8/10/2021

The use of Python for social media text mining and natural language processing is an important and valuable concept. The concept has tremendous potential for practical use. The instructor has brilliantly explained the concept in her lectures. Her knowledge of the subject is deep and profound.

By Rajesh Kumar on 8/2/2021

The course contains vital and important tools for text mining and natural language processing.The course will significantly enhance the quality of my work. The instructor has been superb both in the spectrum of her knowledge and her explanation of the concept in her lectures.

By RKM singh on 7/26/2021

Text analysis and natural language processing with Python is highly valuable course which will enhance my knowledge base several notches. It has wide practical usage as well. The instructor has complete mastery over the subject and her delivery is clear and confident.

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Overall Score : 86 / 100