Given your background as a backend software engineer and the requirements of your AI master's program, it's essential to build a strong foundation in machine learning (ML) and progress towards understanding and utilizing Large Language Models (LLMs). Below is a roadmap with resource recommendations:
Data Cleaning:
- Understanding Data Cleaning: Data Cleaning in Python: Advanced Techniques
- Handling Missing Data: Handling Missing Data with Pandas
- Outlier Detection: Detecting and Handling Outliers
Feature Selection:
- Introduction to Feature Selection: Feature Selection Techniques in Machine Learning
- Advanced Techniques: Feature Selection Using Python for Machine Learning
Modeling:
- Machine Learning Basics: Machine Learning Crash Course by Google Developers
- ML Projects from Scratch: Python Machine Learning Tutorial (Data Science)
Normalization and Regularization:
- Normalization Techniques: Feature Scaling with scikit-learn
- Understanding Regularization: Regularization in Machine Learning
Optimization and Neural Networks with Backpropagation:
- Neural Network Optimization: Neural Networks Demystified
- Backpropagation Explained: Backpropagation clearly explained
Seq2Seq and Vectors/Embeddings:
- Sequence to Sequence Learning: Sequence Models - Coursera
- Word Vectors and Embeddings: Natural Language Processing - Word Embeddings
Transformers and Attention Mechanism:
- Transformers and Attention: Illustrated Guide to Transformers
- 'Attention Is All You Need' Paper Explained: Attention Is All You Need - Paper Walkthrough
Fine-Tuning LLMs:
- Fine-Tuning LLMs: How to Fine-Tune BERT for Text Classification
- Hands-On Fine-Tuning: Hugging Face Course
Develop a Strong Foundation: Start with understanding the basics of machine learning. Google's Crash Course is excellent for this. Make sure you're comfortable with statistical concepts, as they're the building blocks of ML.
Hands-On Practice: Apply what you learn in small projects. Use datasets from places like Kaggle to practice your skills in data cleaning and building models.
Deepen Your Understanding: Once you've got the basics down, move on to more complex topics like neural networks and regularization. The resources provided are sequential and will build upon your foundational knowledge.
LLMs and Beyond: LLMs are advanced and build upon many of the concepts from earlier ML studies. Ensure you understand how neural networks work, especially the concepts of embeddings and attention, before diving into LLMs.
Stay Current: Follow AI research, read papers, and implement algorithms from scratch. Resources like arXiv.org for the latest papers and Papers with Code for implementations can be invaluable.
Community Engagement: Join ML communities and forums. Engage in discussions, attend webinars, and workshops to stay connected with the latest in the field.
Balance Theory and Practice: While it's important to understand the theory, practical application solidifies your understanding. Use platforms like Colab to experiment with code and model training.
Remember, learning machine learning is a marathon, not a sprint. Take the time to understand the fundamentals thoroughly, and build from there. With your background in software engineering, you have a strong foundation