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Olatunji Ayodele Abidemi
Olatunji Ayodele Abidemi

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Generative AI roadmap a comprehensive guide, mixing learning, projects, and career goals

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Generative AI Roadmap

  1. New Skills to Learn:
  2. Programming Languages:
    • Python: Focus on libraries like TensorFlow, PyTorch, and Keras.
    • SQL: Essential for data management and retrieval.
  3. Machine Learning & Deep Learning:
    • Algorithms: Understand supervised and unsupervised learning algorithms.
    • Neural Networks: Dive into architectures like CNN, RNN, and GANs.
  4. NLP (Natural Language Processing):
    • Study language models like BERT, GPT-3/4.
    • Learn about tokenization, transformers, and embeddings.
  5. Data Science:
    • Data Cleaning and Preprocessing: Techniques to handle large datasets.
    • Exploratory Data Analysis (EDA): Tools to visualize and understand data.
  6. Cloud Computing:
    • Platforms like AWS, Google Cloud, and Azure for deploying AI models.
  7. Ethics in AI:

    • Understand the ethical implications and biases in AI systems.
  8. Side Projects to Pursue:

  9. Chatbot Development:

    • Build a conversational agent using Rasa or Microsoft Bot Framework.
  10. Text Generation:

    • Create a text summarization tool or a poetry generator using GPT-3/4.
  11. Image Generation:

    • Experiment with GANs to generate art or enhance images.
  12. Personal Assistant:

    • Develop an AI assistant that can handle tasks like scheduling, reminders, etc.
  13. Open-Source Contributions:

    • Contribute to open-source AI projects on platforms like GitHub.
  14. Career Aspirations:

  15. Short-Term Goals:

    • Gain practical experience through internships or freelance projects.
    • Participate in AI competitions on platforms like Kaggle.
  16. Mid-Term Goals:

    • Obtain certifications in AI and ML from reputable institutions.
    • Aim for a role as a Machine Learning Engineer or Data Scientist.
  17. Long-Term Goals:

    • Aspire to be an AI Research Scientist or lead a team of AI developers.
    • Pursue advanced degrees like a Master’s or Ph.D. in AI-related fields.
  18. Networking and Professional Development:

  19. Join AI Communities:

    • Engage with online communities on Reddit, Discord, or LinkedIn.
  20. Attend Conferences and Workshops:

    • Participate in events like NeurIPS, CVPR, and ACL.
  21. Collaborate with Peers:

    • Work on group projects or research papers with other AI enthusiasts.

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