Machine Learning Natural Language Processing In Python
 
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 methods
2) Probability models and Markov models
3) Machine learning methods
4) Deep learning and neural network methods

In 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. Discover their applications in text classification, article spinning, and text generation (e.g., poetry). These models are crucial prerequisites for understanding advanced Transformer models like BERT and GPT.

Part 3 shifts to application-oriented learning with machine learning methods. Explore classic NLP tasks like spam detection, sentiment analysis, latent semantic analysis/indexing, and topic modeling. Work with algorithms like Naive Bayes, Logistic Regression, Principal Components Analysis (PCA)/Singular Value Decomposition (SVD), and Latent Dirichlet Allocation (LDA)—staples in NLP.

In Part 4, deep learning methods cover modern neural network architectures for NLP tasks. Explore Feedforward Artificial Neural Networks (ANNs), embeddings, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Delve into advanced RNN architectures like LSTM and GRU, which are crucial for understanding state-of-the-art transformers like BERT and GPT-3.

**Unique Features:**

- Every line of code is meticulously explained.
- Embrace university-level math to grasp essential algorithmic details often omitted in other courses.

**What you'll learn:**

- Techniques to convert text into vectors using CountVectorizer, TF-IDF, word2vec, and GloVe
- Implementation of a document retrieval system/search engine / similarity search/vector similarity
- Probability models, language models, and Markov models (essential for Transformers, BERT, and GPT-3)
- Implementation of spam detection, sentiment analysis, article spinning, and text summarization
- Latent semantic indexing and topic modeling with LDA, NMF, and SVD
- Machine learning algorithms (Naive Bayes, Logistic Regression, PCA, SVD, Latent Dirichlet Allocation)
- Deep learning architectures (ANNs, CNNs, RNNs, LSTM, GRU)
- Text preprocessing techniques and parts-of-speech tagging
- Foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion.

This course is a must for anyone eager to delve into the depths of AI and NLP, from beginners to advanced learners. It offers practical insights and hands-on experience with cutting-edge technologies.