Have we ever wondered about the inner workings of AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion? Dive deep into the foundations of these groundbreaking applications with our comprehensive course.**Machine Learning: Natural Language Processing in Python** offers a comprehensive 4-in-1 course covering:1) Vector models and text preprocessing methods2) Probability models and Markov models3) Machine learning methods4) Deep learning and neural network methodsIn Part 1, the significance of vectors in data science and artificial intelligence is uncovered. Learn about techniques like CountVectorizer and TF-IDF for text-to-vector conversion and delve into neural embedding processes such as word2vec and GloVe.

Apply these skills to tasks like text classification, document retrieval/search engine, and text summarization. Master essential text preprocessing steps like tokenization, stemming, and lemmatization, and a brief introduction to classic NLP tasks like parts-of-speech tagging.Part 2 delves into probability and Markov models, which have been foundational in data science and machine learning for decades.