Machine Learning, Deep Learning, and LLMs: The Same Foundation at Different Scales

1. 1Explore the basics of Machine Learning using scikit-learn to understand the foundation
2. 2Run Deep Learning models using TensorFlow or PyTorch to see how they build upon ML
3. 3Configure and fine-tune LLMs using Hugging Face Transformers to apply them to NLP tasks
4. 4Compare the performance of ML, DL, and LLMs on a benchmark dataset to understand their differences
5. 5Apply the knowledge of ML, DL, and LLMs to a real-world project, such as text classification or image recognition

Data scientists and machine learning engineers can benefit from understanding the similarities and differences between ML, DL, and LLMs to apply the right techniques to their projects

💡 Machine Learning, Deep Learning, and LLMs share the same foundation but differ in scale and application

Discover the foundation of #MachineLearning, #DeepLearning, and #LLMs and how they differ in scale!