Python for Non-Programmers Basics
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Questions and Answers

What is the purpose of data cleaning?

  • To organize data formats for easier analysis.
  • To add metadata to the data set.
  • To fix or remove errors in the data. (correct)
  • To collect new data samples.

Which method often produces the highest-quality labels in data annotation?

  • Manual Annotation (correct)
  • Automated Annotation
  • Annotation Tools
  • Training Data

What aspect does data bias affect in data analysis?

  • The speed of data collection.
  • The analytical methods used.
  • The storage solutions for data.
  • The accuracy of the analysis. (correct)

What is metadata?

<p>Data that describes other data. (D)</p> Signup and view all the answers

What do annotation rules help with in the data annotation process?

<p>Ensuring consistency in labeling. (C)</p> Signup and view all the answers

Which of the following is a result of automated annotation?

<p>Increased speed of data processing. (B)</p> Signup and view all the answers

What is the purpose of sampling in data analysis?

<p>To select a representative subset for study. (D)</p> Signup and view all the answers

Why is versioning important in data management?

<p>To monitor changes in annotated data over time. (B)</p> Signup and view all the answers

What is the primary purpose of variables in Python?

<p>To store data for later use (C)</p> Signup and view all the answers

Which of the following is NOT a common data type in Python?

<p>Matrix (C)</p> Signup and view all the answers

What does the operator '==' do in Python?

<p>Check if two values are equal (C)</p> Signup and view all the answers

What is the purpose of control structures in programming?

<p>To perform actions based on conditions (A)</p> Signup and view all the answers

How do functions contribute to a program's efficiency?

<p>By simplifying the coding process through reuse (A)</p> Signup and view all the answers

What process involves removing extra symbols from text to enhance its analyzability?

<p>Cleaning Text (D)</p> Signup and view all the answers

Why are lists particularly useful in Python?

<p>They store multiple items in a specific order (B)</p> Signup and view all the answers

Which characteristic is NOT typically used to identify fake news?

<p>Factual accuracy (B)</p> Signup and view all the answers

What is the primary purpose of language models in social media analysis?

<p>To understand the context behind text (D)</p> Signup and view all the answers

What is the function of the input() method in Python?

<p>To fetch user data (D)</p> Signup and view all the answers

Which of the following best describes 'emotion' in the context of fake news detection?

<p>The intensity of feelings expressed in the text (D)</p> Signup and view all the answers

What role do APIs play in data collection?

<p>They allow programs to retrieve data from external services (B)</p> Signup and view all the answers

Identifying what a user needs based on their queries is known as what?

<p>Understanding User Goals (D)</p> Signup and view all the answers

What is 'text sorting' in the context of fake news detection?

<p>Classifying articles as real or fake (D)</p> Signup and view all the answers

What does engagement measure in the context of social media?

<p>The amount of likes, comments, and shares (B)</p> Signup and view all the answers

What role do backup responses serve in chatbot functionality?

<p>They are predetermined answers for uncertain contexts (C)</p> Signup and view all the answers

What does feedback entail in the context of chatbot improvement?

<p>User ratings or comments to enhance performance (D)</p> Signup and view all the answers

What is the primary function of a language model like ChatGPT?

<p>To create dialogue based on input data (B)</p> Signup and view all the answers

What does 'keeping context' refer to in chatbot interactions?

<p>Recalling previous conversation elements to remain relevant (B)</p> Signup and view all the answers

Which of the following best illustrates the 'zero-shot vs. few-shot' concept?

<p>Providing no examples versus giving several examples (A)</p> Signup and view all the answers

What is a practical use of AI writing tools?

<p>Helping in customer service interactions (C)</p> Signup and view all the answers

What is meant by 'adjusting style' in the context of AI content generation?

<p>Modifying tone to match the content purpose (B)</p> Signup and view all the answers

In the process of prompt engineering, what is the significance of unbiased prompts?

<p>Prompts that maintain neutrality to avoid leading answers (D)</p> Signup and view all the answers

What are templates in prompt engineering?

<p>Standardized prompts for repetitive tasks (B)</p> Signup and view all the answers

Flashcards

Variables

Containers that store data, like numbers or text. Used to save and reuse data in a program.

Data Types

Categories of data in Python. Examples are numbers, text, and lists.

Operators

Symbols that perform actions on data (like addition, comparison).

Control Structures

Commands to control program flow (if conditionals, loops).

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Functions

Reusable code blocks to perform specific tasks.

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Lists

Ordered collections of items.

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Strings

Text data.

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Input() function

Gets data from the user.

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Print() function

Displays data on the screen.

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Errors

Mistakes in code that stop a program.

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Libraries

Collections of tools to perform tasks in Python.

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API

Application Programming Interface - allows programs to get or use data from external services.

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Web Scraping

Collecting data from websites using programs.

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Data Collection

Gathering information (posts, comments, etc) from social media platforms.

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ChatGPT

An AI that generates text based on input. It creates conversations.

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Language Model

Trained on a massive amount of text to answer questions and hold discussions.

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Personalized Replies

Customizing chatbot responses based on user information.

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Feedback

User ratings/comments improving chatbot responses.

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Privacy

Ensuring user data protection.

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Prompt

Question/statement guiding AI's answer.

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Prompt Format

Writing clear prompts for good AI responses.

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Giving Instructions

Detailed directions that help AI focus on the right answer.

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Randomness

Adjusting how creative or predictable AI responses are.

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Examples

Adding sample text to show the AI desired answers.

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AI Writing Tools

Tools helping create content, similar to ChatGPT.

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Adjusting Style

Changing tone (e.g., formal, casual) to fit content purpose.

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Data Privacy

Respecting people's privacy when collecting data, especially personal information.

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Annotations

Adding tags to data to categorize it, e.g., tagging tweets as positive or negative.

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Metadata

Extra data providing context, like date, time, and location of a social media post.

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Data Formats

Ways of organizing data (e.g., JSON, CSV) to make it easy to store, read, and analyze.

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Data Cleaning

Fixing or removing errors like typos and repeated entries in data.

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Sampling

Choosing a smaller portion of data to analyze when the full dataset is too large.

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Data Bias

Data that is unbalanced or unfairly represents one view or group, affecting analysis accuracy.

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Manual Annotation

Humans add tags to data by understanding its meaning; usually produces the best quality labels.

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Automated Annotation

Using software to automatically tag data, faster but may miss details.

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Consistency (annotation)

Ensuring labeling is done the same way by all annotators to keep data accurate.

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Annotation Rules

Clear guidelines to help annotators tag data correctly and consistently.

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Training Data

Labeled data used to teach AI how to recognize patterns, e.g., teaching it what positive/negative tweets look like.

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Annotation Tools

Software that helps annotators label data faster.

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Data Storage

Keeping data safe and organized for easy access during analysis.

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Versioning

Tracking changes in data as it's annotated/cleaned to always know the latest version.

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Dataset Checks

Checking data for accuracy and completeness before analysis.

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Social Media Data Privacy

Ensuring permission to use social data according to rules and privacy laws.

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Sentiment (analysis)

Analyzing social media posts to determine if they express positive, negative, or neutral sentiment.

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Topics (analysis)

Identifying main themes or ideas discussed across social media posts.

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Recognizing Names

Finding mentions of people, places, or brands in text.

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Cleaning Text

Removing extra symbols from text for easier analysis.

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Breaking Text

Dividing sentences into individual words for processing.

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Slang

Informal words, acronyms, or emojis on social media.

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Language Models

Programs understanding the meaning behind social media text.

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Important Words

Key words or phrases summarizing the main message.

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Finding Trends

Tracking how popular topics change over time.

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Engagement

Measuring likes, comments, and shares to understand responses.

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Fake News

Untrue information disguised as real to mislead.

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Fact-Checking

Verifying information accuracy by comparing to reliable sources.

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Features (Fake News)

Characteristics helping spot fake news (e.g., suspicious headlines).

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Text Sorting

Categorising articles or posts as real or fake.

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Misleading Language

Words used to confuse or deceive readers.

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Source Trust

Checking if an information source is reliable.

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Simple Models (Fake News)

Basic AI programs for fake news detection.

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Emotion (Fake News)

Analyzing emotions in text (e.g., anger, fear) in fake news.

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Spread of News

Observing how fake news spreads across social networks.

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Evaluating Success

Measuring the performance of fake news detection tools.

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Chatbot Basics

Programs that talk to people, using rules or AI.

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Understanding User Goals

Identifying user needs from their requests or statements.

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Dialogue Flow

The order of responses maintaining a smooth conversation.

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Understanding Language

Chatbots processing messages to respond appropriately.

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Response Creation

Generating replies suited to questions for useful answers.

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Backup Responses

Generic responses when the chatbot isn't sure how to reply.

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Study Notes

Python for Non-Programmers - Basics

  • Variables are containers for data like numbers or text (e.g., name = "John")
  • Data Types categorize data: numbers, text, and lists (collections)
  • Operators perform actions (+, ==) in code
  • Control Structures manage program flow (e.g., if statements, loops)
  • Functions are shortcuts for repeating code
  • Lists hold items in order (e.g., ["red", "blue", "green"])
  • Strings store text: combined and modified (e.g., "Hello, world")
  • Input/Output interacts with users (input(), print())
  • Errors are mistakes (typos) managed with error handling
  • Libraries are collections of tools for various tasks

Social Media Data Collection and Annotation Pipeline

  • Data Collection gathers posts, comments, and messages from social media platforms
  • APIs directly access data from websites (e.g., Twitter)
  • Web Scraping collects data from web pages
  • Data Privacy is important when collecting data
  • Annotations (e.g., "positive" or "negative") tag data for analysis
  • Metadata (date, time, location) provides context
  • Data Formats (JSON, CSV) organize collected data
  • Data Cleaning fixes errors (typos, repeated posts)
  • Sampling chooses a smaller dataset for analysis
  • Data Bias means unbalanced or unfair representation in data

NLP for Social Media Listening and Analysis

  • Sentiment analysis identifies positive, negative, or neutral sentiment
  • Topics in social media posts are identified (e.g., common themes, events)
  • Recognizing Names (people, places, brands) in conversations
  • Cleaning text removes extra symbols (e.g., hashtags)
  • Breaking text into words for separate analysis
  • Slang, informal words, and acronyms are considered for analysis
  • Language Models interpret the meaning behind social media text
  • Important words summarize post messages
  • Identifying trends in data over time
  • Measuring engagement (likes, comments, shares)

Case Studies: NLP and Media Analytics - Fake News Detection

  • Fake news is false information that looks genuine
  • Fact-checking verifies information accuracy
  • Features of fake news (e.g., suspicious headlines) are identified
  • Text sorting groups posts as real or fake
  • Misleading language aims to confuse readers
  • Source Trust verifies information reliability
  • Simple models analyze text features for fake news detection
  • Emotion analysis in text identifies strong emotions used in fake news
  • Spread of news tracks social network sharing
  • Evaluation measures the success of fake news detection tools

Conversational Al in Practice: A Deep Dive into Chatbots

  • Basics of chatbot programs and how they respond to user input
  • Understanding user goals in conversations
  • Dialogue flow, or the order of conversation turns
  • Understanding language from user input
  • Response Creation, generic/backup responses, data-learning to improve responses
  • User feedback for chatbot improvement
  • Protecting user privacy

Exploring ChatGPT: Introduction to ChatGPT and its Capabilities

  • ChatGPT is an AI that generates text based on input
  • Language Model trained on vast text data for generating text
  • Creating conversations (back and forths)
  • Customizing ChatGPT for specific topics
  • Keeping context within conversation
  • Good prompt writing guiding ChatGPT responses
  • Practical uses of ChatGPT, e.g., customer service or text assistance
  • ChatGPT limitations including mistakes or incomplete responses
  • Safety considerations with potentially harmful responses
  • Interactivity provides conversational dialogue

Prompt Engineering Basics: Introduction

  • Prompts, questions, or statements that guide AI responses
  • Prompt format should be clear for desired response
  • Giving instructions aids to get the right response
  • Adjusting randomness in prompts' predictability
  • Examples improve AI's understanding of needed response
  • Avoiding bias in prompts to avoid a specific answer
  • Testing different prompts to see which works best
  • Using templates for routine tasks as prompts
  • Understanding prompt limitations and that not all will be perfect

Creative Writing and Media Content Generation with Al

  • AI writing tools for content creation (e.g., ChatGPT)
  • Adjusting writing style, e.g., formal or casual
  • Story writing or generating story ideas
  • Summarizing information from longer content
  • Rewriting for clarity or variety
  • Brainstorming ideas for content
  • Improving grammar, editing errors
  • Setting boundaries for AI creativity
  • Creating different content types
  • Originality in generated content to avoid plagiarism

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