Machine Learning: Natural Language Processing in Python (V2)

Welcome to Machine Learning: Natural Language Processing in Python (Version 2). This course covers the use of Markov Models, NLTK, Artificial Intelligence, Deep Learning, Machine Learning, and Data Science in Python.

Course Overview

This is 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

Part 1: Vector Models and Text Preprocessing

Discover why vectors are essential in data science and artificial intelligence. Learn techniques for converting text into vectors like CountVectorizer and TF-IDF, as well as neural embedding methods such as word2vec and GloVe.

Applications

• Text classification
• Document retrieval / search engine
• Text summarization

Additionally, master important text preprocessing steps like tokenization, stemming, and lemmatization. Briefly explore classic NLP tasks like parts-of-speech tagging.

Part 2: Probability Models and Markov Models

Learn about a pivotal model used in finance, bioinformatics, and reinforcement learning. Explore how probability models can assist in:

• Building a text classifier
• Article spinning
• Text generation (such as poetry)

Grasp the essentials needed to understand advanced Transformer models like BERT and GPT-3.

Part 3: Machine Learning Methods

Focus on applying machine learning methods to classic NLP tasks such as:

• Spam detection
• Sentiment analysis
• Latent semantic analysis (LSA)
• Topic modeling

Learn to apply Naive Bayes, Logistic Regression, PCA/SVD, and LDA algorithms significant in NLP.

Part 4: Deep Learning Methods

Enhance your capabilities with modern neural network architectures applicable to NLP tasks. Understand:

• Feedforward Artificial Neural Networks (ANNs)
• Embeddings
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)

Explore RNN architectures like LSTM and GRU, widespread in language processing by leading tech companies. Gain insights into Transformers (BERT, GPT-3) as part of deep neural networks.