ai - Java Programmatic Universe

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Topic 1:- 2 steps of Introduction and Setup of Python

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Topic 4- Python Functions

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Topic 7 – Modules and Packages in Python

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Topic 8- File Handling in Python

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Topic 12 – 4 steps for Python Web Development

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Topic 14- 3 steps of Unit Testing in Python

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Topic 15 – 4 points about Python Data Science and Machine Learning

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Topic 16 – 3 points of Python Best Practices

9 (C)- LLM benchmarks and leaderboards

9 (B)- Human evaluation and user studies

9 (A)- Automatic evaluation metrics (BLEU, ROUGE, METEOR)

8 (C)- AI governance and ethical frameworks

8 (B)- Privacy and security considerations

8 (A)- Bias and fairness in LLMs

7 (E)- Code generation and programming assistance

7 (D)- Content creation and creative writing

7 (C)- Question answering and information retrieval

7 (B)- Text generation and summarization

7 (A)- Chatbots and conversational AI

6 (A)- Model compression and quantization

5 (C)- Prompt engineering and few-shot learning

5 (B)- Fine-tuning LLMs for specific tasks (text generation, summarization, question answering)

5 (A)- Pre-training techniques (self-supervised learning, masked language modeling)

4 (C)- TensorFlow and PyTorch for LLMs

4 (B)- Anthropic’s models and APIs

4 (A)- Hugging Face Transformers

3 (E)- LLaMA, Anthropic’s models, and other open-source LLMs

3 (D)- GPT-3 and its variants (GPT-J, GPT-Neo)

3 (C)- T5 (Text-to-Text Transfer Transformer)

3 (B)- Bidirectional Encoder Representations from Transformers (BERT)