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README.md

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 ### 1. Mathematics for Machine Learning
 
 Before mastering machine learning, it is important to understand the fundamental mathematical concepts that power these algorithms.
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 - [Khan Academy - Calculus](https://www.khanacademy.org/math/calculus-1): An interactive course that covers all the basics of calculus.
 - [Khan Academy - Probability and Statistics](https://www.khanacademy.org/math/statistics-probability): Delivers the material in an easy-to-understand format.
 
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 ### 2. Python for Machine Learning
 
 Python is a powerful and flexible programming language that's particularly good for machine learning, thanks to its readability, consistency, and robust ecosystem of data science libraries.
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 - [freeCodeCamp - Machine Learning for Everybody](https://youtu.be/i_LwzRVP7bg): Practical introduction to different machine learning algorithms for beginners.
 - [Udacity - Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120): Free course that covers PCA and several other machine learning concepts.
 
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 ### 3. Neural Networks
 
 Neural networks are a fundamental part of many machine learning models, particularly in the realm of deep learning. To utilize them effectively, a comprehensive understanding of their design and mechanics is essential.
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 - [Fast.ai - Practical Deep Learning](https://course.fast.ai/): Free course designed for people with coding experience who want to learn about deep learning.
 - [Patrick Loeber - PyTorch Tutorials](https://www.youtube.com/playlist?list=PLqnslRFeH2UrcDBWF5mfPGpqQDSta6VK4): Series of videos for complete beginners to learn about PyTorch.
 
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 ### 4. Natural Language Processing (NLP)
 
 NLP is a fascinating branch of artificial intelligence that bridges the gap between human language and machine understanding. From simple text processing to understanding linguistic nuances, NLP plays a crucial role in many applications like translation, sentiment analysis, chatbots, and much more.
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 - [Jake Tae - PyTorch RNN from Scratch](https://jaketae.github.io/study/pytorch-rnn/): Practical and simple implementation of RNN, LSTM, and GRU models in PyTorch.
 - [colah's blog - Understanding LSTM Networks](https://colah.github.io/posts/2015-08-Understanding-LSTMs/): A more theoretical article about the LSTM network.
 
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 ### 5. The Transformer Architecture
 
 The Transformer model, introduced in the "Attention is All You Need" paper, is a type of neural network architecture at the core of large language models.
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 - [Introduction to the Transformer by Rachel Thomas](https://www.youtube.com/watch?v=AFkGPmU16QA): Provides a good intuition behind the main ideas of the Transformer architecture.
 - [Stanford CS224N - Transformers](https://www.youtube.com/watch?v=ptuGllU5SQQ): A more academic presentation of this architecture.
 
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 ### 6. Pre-trained Language Models
 
 Pre-trained models like BERT, GPT-2, and T5 are powerful tools that can handle tasks like sequence classification, text generation, text summarization, and question answering.
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 - [Hugging Face - Transformers Notebooks](https://huggingface.co/docs/transformers/notebooks): List of official notebooks provided by Hugging Face.
 - [Hugging Face - Metrics](https://huggingface.co/metrics): All metrics on the Hugging Face hub.
 
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 ### 7. Advanced Language Modeling
 
 To fine-tune your skills, learn how to create embeddings with sentence transformers, store them in a vector database, and use parameter-efficient supervised learning or RLHF to fine-tune LLMs.
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 - [Hugging Face - PEFT](https://huggingface.co/blog/peft): Another library from Hugging Face implementing different techniques, such as LoRA.
 - [Efficient LLM training by Phil Schmid](https://www.philschmid.de/fine-tune-flan-t5-peft): Implementation of LoRA to fine-tune a Flan-T5 model.
 
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 ### 8. LMOps
 
 Finally, dive into Language Model Operations (LMOps), learning how to handle prompt engineering, build frameworks with Langchain and Llamaindex, and optimize inference with weight quantization, pruning, distillation, and more.
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 - [Pinecone - LangChain AI Handbook](https://www.pinecone.io/learn/langchain-intro/): Excellent free book on how to master the LangChain library.
 - [A Primer to using LlamaIndex](https://gpt-index.readthedocs.io/en/latest/guides/primer.html): Official guides to learn more about LlamaIndex.
 
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 *Disclaimer: I am not affiliated with any sources listed here. This roadmap was inspired by the excellent [DevOps Roadmap](https://github.com/milanm/DevOps-Roadmap) from Milan Milanović and Romano Roth.*