Text Analysis and Natural Language Processing With Python
Text Analysis and Natural Language Processing With Python, available at $69.99, has an average rating of 4.43, with 70 lectures, based on 321 reviews, and has 2143 subscribers.
You will learn about 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 This course is ideal for individuals who are People who wish to learn practical text mining and natural language processing or People who wish to derive insights from textual and social media data or People wanting to understand the impact of human sentiments on financial markets It is particularly useful for People who wish to learn practical text mining and natural language processing or People who wish to derive insights from textual and social media data or People wanting to understand the impact of human sentiments on financial markets.
Enroll now: Text Analysis and Natural Language Processing With Python
Summary
Title: Text Analysis and Natural Language Processing With Python
Price: $69.99
Average Rating: 4.43
Number of Lectures: 70
Number of Published Lectures: 70
Number of Curriculum Items: 70
Number of Published Curriculum Objects: 70
Original Price: $199.99
Quality Status: approved
Status: Live
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
Who Should Attend
- 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
Target Audiences
- 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
ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT PYTHON SOCIAL MEDIA & NATURAL LANGUAGE PROCESSING (NLP)
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Do you want to harness the power of social media to make financial decisions?
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Are you looking to gain an edge in the fields of retail, online selling, real estate and geolocation services?
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Do you want to turn unstructured data from social media and web pages into real insights?
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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
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Gain proficiency in setting up and using Google CoLab for Python Data Science tasks
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Carry out common social media mining tasks such as obtaining tweets (e.g. tweets relating to bitcoins)
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Work with complicated web pages and extract information
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Process the extracted textual information in a usable form via preprocessing techniques implemented via powerful Python packages such as NTLK
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A thorough grounding in text analysis and NLP related Python packages such as NTLK, Snscrape among others
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Carry out common text analytics tasks such as Sentiment Analysis
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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 🙂
Course Curriculum
Chapter 1: Introduction To Social Media Mining With Python
Lecture 1: Welcome to the Course
Lecture 2: Data and Code
Lecture 3: Python Installation
Lecture 4: What Is Google CoLab?
Lecture 5: Google Colabs and GPU
Lecture 6: Google Colab Packages
Chapter 2: Basic Data Preprocessing
Lecture 1: What Is Pandas?
Lecture 2: Basic Data Cleaning With Pandas
Lecture 3: Basics of Data Visualization
Chapter 3: Welcome To Social Media
Lecture 1: Can Social Media Be Useful?: The Case of Twitter
Chapter 4: Extracting Tweets (Without An API)
Lecture 1: Obtaining Tweets Without A Twitter Account
Lecture 2: Lets Dip Our Toes Into Twitter
Lecture 3: Get Elon Musk's Tweet
Lecture 4: Obtain The Most Popular Tweets of a User
Lecture 5: Obtain Tweets For A User Between A Certain Date
Lecture 6: Look With For With a Specific Term
Lecture 7: Elon Musk's Bitcoin Tweets
Lecture 8: Tweets From a Location
Lecture 9: Tweets From Multiple Locations
Lecture 10: Tweets From Multiple Locations and Multiple Terms
Lecture 11: Another Way of Obtaining Tweets
Lecture 12: More Snscrape Tweets
Chapter 5: Other Ways of Obtaining Textual Data
Lecture 1: What is API?
Lecture 2: Using APIs: Singapore MRT Stations
Lecture 3: Obtain Financial News Headlines
Lecture 4: Obtaining Textual Data From Reddit
Chapter 6: Basic Textual Data Preprocessing
Lecture 1: Introduction to Theory
Lecture 2: Lets Start Cleaning The Text
Lecture 3: Final Cleaned Text
Lecture 4: A Function For Text Cleaning
Lecture 5: More Text Cleaning
Lecture 6: Another NTLK-Based Workflow
Chapter 7: Exploring Text Data
Lecture 1: Tweet Lengths
Lecture 2: How People Interact With Tweets
Lecture 3: Of Mentions and Hashtags
Lecture 4: Identify The Most Popular Hashtags
Lecture 5: Identify the Most Common Usernames
Lecture 6: What Are Wordclouds?
Lecture 7: Basic Wordcloud-Install
Lecture 8: A Basic Wordcloud
Lecture 9: Word Count of Common Words
Lecture 10: N-Grams
Lecture 11: Network of Bigrams
Lecture 12: Topic Modelling With Gensim
Chapter 8: Exploring Sentiments
Lecture 1: Identify the Polarity of Text
Lecture 2: Polarity: Positive or Negative
Lecture 3: Dealing With Dates
Lecture 4: Introduction to VADER Sentiment Analysis
Lecture 5: Dealing With Dates
Lecture 6: VADER Sentiment For Financial News
Lecture 7: Visualise the Sentiments
Chapter 9: Machine Learning and Deep learning For Text Data
Lecture 1: What Is Machine Learning?
Lecture 2: Preprocessing-Toy Example
Lecture 3: A Simple Machine Learning Model on Textual Data
Lecture 4: Predicting Stock Price Movements Based On Newspaper Headlines
Lecture 5: Unsupervised Learning With K-Means Algorithm
Lecture 6: Identifying Textual Clusters With K-means
Lecture 7: DBSCAN Based Textual Clustering
Lecture 8: Classify the Tweet Sentiment-GBM
Lecture 9: Keras Installation-Windows
Lecture 10: Keras Installation-Mac
Lecture 11: Long short-term memory (LSTM): Theory
Lecture 12: Brief Lowdown on Word Embeddings
Lecture 13: LSTM For Classifying Tweet Sentiment-1
Chapter 10: Miscellaneous Information
Lecture 1: Lets Do Dictionaries
Lecture 2: Set up the FourSquare App
Lecture 3: NTLK Cleaning
Lecture 4: Text Summarisation With AI-Case Study
Lecture 5: Of NLP and ChatGPT
Lecture 6: Posit On POSIT
Instructors
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Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)
Rating Distribution
- 1 stars: 9 votes
- 2 stars: 13 votes
- 3 stars: 21 votes
- 4 stars: 79 votes
- 5 stars: 199 votes
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