this post was submitted on 25 May 2024
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AI

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Artificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen.

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How can I create an AI model from this dataset? (raw.githubusercontent.com)
submitted 5 months ago by kionite231@lemmy.ca to c/artificial_intel@lemmy.ml
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Hello,

I want to create an AI model to learn about AI/ML. so I have scraped some data from Threads and Instagram.now I am wondering how can I use this dataset to make an AI model or do something useful with it? (BTW I don't know anything about AI/ML. I have done internship as Data Analyst so I know a little bit about Linear regression etc. but don't know anything advance.)

I am really curious to explore this space :)

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[โ€“] lurch@sh.itjust.works -2 points 5 months ago (1 children)

Creating an AI Model: A Beginner's Guide

Introduction

Creating an AI model involves several steps, especially if you're new to the field. Let's break down the process into actionable steps:

  1. Data Preprocessing:

    • Clean and preprocess your dataset.
    • Handle missing values, duplicates, and format the data appropriately.

  2. Define Your Problem:

    • Decide what task your AI model should perform (classification, regression, etc.).
    • Collect labeled data if needed (e.g., sentiment analysis).

  3. Choose an AI/ML Approach:

    • Start with simpler models before diving into deep learning.
    • Common approaches:
      • Linear Regression: Predict continuous values.
      • Classification: Assign labels to data points.
      • Clustering: Group similar data points.
      • Decision Trees: Simple yet powerful.
      • Random Forests: Ensemble of decision trees.
      • Neural Networks: Deep learning models.

  4. Feature Engineering:

    • Extract relevant features from your data.
    • Use techniques like TF-IDF or word embeddings for text data.
    • For images, consider pre-trained CNNs.

  5. Split Your Data:

    • Divide your dataset into training and validation/test sets.

  6. Train Your Model:

    • Use libraries like Scikit-Learn (for traditional ML) or TensorFlow/Keras (for deep learning).
    • Start with a simple model and iterate.

  7. Evaluate and Tune:

    • Use appropriate evaluation metrics (accuracy, precision, recall, F1-score, etc.).
    • If performance is low, consider hyperparameter tuning.

  8. Deployment:

    • Deploy your model (web app, API, etc.).

  9. Learn Continuously:

    • AI/ML is evolving; keep learning and stay updated.

Remember, patience and persistence are key! Start small, learn, and gradually build your expertise. Good luck! ๐Ÿ˜Š

If you have any specific questions or need further guidance, feel free to ask! ๐Ÿš€

For additional resources, explore tutorials and videos on web scraping and AI model training. Happy learning! ๐ŸŒŸ

: Web scraping and AI model training: Microsoft Learn : Building custom models with AI Builder: Microsoft Learn : Web scraping for data models: Towards Data Science

[โ€“] datavoid@lemmy.ml 6 points 5 months ago

Copilot's use of emojis is mildly upsetting