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My Awesome YouTube Channel

Welcome to my YouTube channel! 🎉 In this channel, I share exciting content related to web development. Whether you're interested in Angular, Javascript, or Productivity tips, you'll find something valuable here. YouTube Channel

About Me

I'm Sam, and I'm passionate about product engineering and web development. I create videos that will help you learn web development.

What You'll Find Here

  • [ Angular ]: [Learn the fundamentals. I explained everything you need to get started with this JavaScript framework written in TypeScript. It is the web development framework for building the future. works at any scale. Loved by millions. Build for everyone. open-source framework for building single-page client applications using HTML and TypeScript.]
  • [ Javascript ]: [JavaScript is a powerful programming language that can add interactivity to a website. JavaScript is easy to learn. It's the foundation of frontend web development.]
  • [ Career Tips ]: [self-assessment, goal setting, action planning, implementation, and refinement. By following these steps, you can develop a road map for achieving your career goals. Tips to improve your career development. Cultivating a beginner's mindset is a critical part of career growth. critical part of your professional growth.]
  • [ Common Errors ]: [Mistakes to Avoid in Software Development Projects.]
  • [ Development Tools ]: [Top Software Development Tools List.]
  • [ Typescript ]: [TypeScript extends JavaScript by adding types to the language. TypeScript speeds up your development experience by catching errors. TypeScript can help enhance and improve your web development projects.]
  • [ Git & GitHub ]: [GitHub is where over 100 million developers shape the future of software together. Contribute to the open-source community and manage Git repositories. This practical guide gets you to jump right into using GitHub, learning the basics of Git. Git and GitHub are two of the most essential tools in the world of software development.]
  • [ Video Conferencing Tool ]: [Unlock the potential of video conferencing software development. Dive into our guide for insights on key features and cost factors. If you're looking to integrate video communication into your app or planning to build a video streaming/conference app from scratch, create a fully customized audio & video conferencing app.]

About the app

AI ML app

Q1. How do u differentiate between an Informed Search method and Uninformed search method? Discuss heuristic search as a method of solving problems. Q2. Explain forward chaining and backward chaining inference mechanism. Describe A* technique and prove that A* is optimal. Q3. What are levels of knowledge used in language understanding? What are the general approaches to natural language understanding? Q4. Represent the following by using existential and universal quantifiers Every student smiles. Every student except George smiles.

Q5. Explain: (a) Belief Network (b) Neural Networks (c) Probabilistic Reasoning Q6. How does emergence of agent technology bought paradigm shift in soft development. Q7. First order logic is also known as (i)……….., (ii)………, (iii)…………

Tokenization Techniques

A comprehensive guide to text tokenization methods and implementation in Python.

Table of Contents

Setup

Create and activate a virtual environment to isolate project dependencies:

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows
venv\Scripts\activate
# On Unix or MacOS
source venv/bin/activate

Installation

Install required packages and dependencies:

# Export current dependencies
pip freeze > requirements.txt

# Install specific package
pip install packagename

# Install all requirements
pip install -r requirements.txt

# Install spaCy language model
python -m spacy download en_core_web_sm

# Run the scraper
python scraper.py

Tokenization Methods

1. Unigram Language Model

  • Individual token probability modeling
  • Context-independent analysis
  • Suitable for basic text processing

2. Character Tokenization

  • Character-level text segmentation
  • Ideal for:
    • Noisy data processing
    • Morphologically rich languages
    • Character-based analysis

3. N-gram Tokenization

Sequential token analysis:

Types of N-grams

  • Unigram: Single token units
  • Bigram: Two consecutive tokens
  • Trigram: Three consecutive tokens

4. Sentence Tokenization

  • Text-to-sentence segmentation
  • Applications:
    • Sentence-level analysis
    • Document structuring
    • Content summarization

5. Word-Level and Subword-Level Hybrid Tokenization

Combined tokenization approach:

  • Word-level processing
  • Subword segmentation
  • Multi-language support

6. Morpheme-Based Tokenization

Morphological analysis:

  • Root word identification
  • Prefix/suffix handling
  • Compound word processing

7. Specialized Tokenizers

Treebank Tokenizer

  • Syntax-preserving tokenization
  • Parse tree optimization
  • Linguistic structure maintenance

Tweet Tokenizer

Social media text processing:

  • Hashtag handling
  • Emoji recognition
  • URL processing
  • @mention parsing

Multi-Word Expression Tokenizer (MWETokenizer)

Phrase-level tokenization:

  • Entity recognition
  • Idiomatic expression handling
  • Compound term processing

Usage

Example implementation:

# Import required libraries
from your_tokenizer import Tokenizer

# Initialize tokenizer
tokenizer = Tokenizer()

# Process text
text = "Your input text here"
tokens = tokenizer.tokenize(text)

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Commit changes
  4. Push to the branch
  5. Create a Pull Request

Role of Tokenization in Machine Learning/AI: Tokenization is a crucial step in natural language processing (NLP) that involves breaking down text into smaller units, called tokens. These tokens can be words, phrases, or even characters. This process allows machine learning models to understand and manipulate text data more effectively. By converting text into a structured format, tokenization enables models to learn patterns, understand context, and generate coherent responses.

Roadmap to Proficiency in AI: AI -> ML -> NLP -> DL -> GEN AI -> LLM

  1. Fundamentals of Programming:

    • Learn Python or R for data manipulation and analysis.

  2. Mathematics & Statistics:

    • Study linear algebra, calculus, probability, and statistics.

  3. Data Handling:

    • Familiarize yourself with data preprocessing, cleaning, and exploratory data analysis (EDA).

  4. Machine Learning Basics:

    • Understand supervised and unsupervised learning, model evaluation, and key algorithms (e.g., regression, decision trees).

  5. Deep Learning:

    • Explore neural networks, particularly for NLP tasks (e.g., recurrent neural networks, transformers).

  6. NLP Techniques:

    • Study tokenization, embeddings (like Word2Vec or BERT), and advanced NLP models.

  7. Projects & Practical Experience:

    • Build projects, participate in competitions (like Kaggle), and collaborate on open-source projects.

  8. Stay Updated:

    • Follow AI research, blogs, and communities to keep abreast of new developments.

By following this roadmap and understanding the role of tokenization, you can build a strong foundation in AI and machine learning!

Packages used

Useful commands

Subscribe and Stay Updated!

Don't miss out on new videos! Subscribe to my channel and hit the notification bell 🔔 to receive updates whenever I upload fresh content. Let's learn, laugh, and explore together!

!Subscribe to My Channel

Connect with Me

Feel free to reach out, comment on videos, and share your thoughts. I appreciate your support! 🙌

License

This project is licensed under the MIT License.

If you have any questions or need further assistance, feel free to ask! 🚀

Steps to contribute and generate PR(pull request)

clone the repository

clone the forked repository to your system. Go to your GitHub account, open the forked repository, click on the code button and then clone the repository. If you want to use the terminal, use the following commands after you fork the repository, open the terminal type the given command

git clone repo url

create a branch

create a branch on your local repository to solve a problem.

Terminal commands

git checkout -b your_new_branch_name

add & commit

add your changes(folder) to that branch. Make necessary changes and commit those changes. Terminal commands

git add .
git commit -m "your-commit-message"

push changes to github

finally, push your local repository to the remote repository compare & submit a pull request

terminal commands

git push origin

Go to your repository on GitHub, you'll see a compare & pull request button. Click on that button.

Now submit the pull request.

For quick approval of the pull request, reach out to me on the mentioned social media channels.

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______      _               _   _               _ 
| ___ \    (_)             | | | |             | |
| |_/ / ___ _ _ __   __ _  | |_| | ___ _ __ ___| |
| ___ \/ _ \ | '_ \ / _` | |  _  |/ _ \ '__/ _ \ |
| |_/ /  __/ | | | | (_| | | | | |  __/ | |  __/_|
\____/ \___|_|_| |_|\__, | \_| |_/\___|_|  \___(_)
                     __/ |                        
                    |___/                         

 


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happy coding fellas!!💕✨