Natural Language Processing

Overview

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Dive into the world of Natural Language Processing (NLP) with this comprehensive 3.5-hour course. Explore fundamental concepts of machine learning, including supervised and unsupervised learning, class imbalance, and model evaluation. Learn essential NLP techniques such as text preprocessing and vectorization. Master various machine learning models like K-Nearest Neighbors, tree-based models, and regression models. Delve into advanced topics including neural networks, word vectors, recurrent neural networks, and transformers. Gain hands-on experience using Jupyter Notebooks on SageMaker. By the end of this accelerated course, acquire a solid foundation in NLP and its applications in modern machine learning.

Syllabus

Accelerated Natural Language Processing 1.1 - Course Introduction.
Accelerated Natural Language Processing 1.2 - Introduction to Machine Learning.
Accelerated Natural Language Processing 1.3 - ML Applications.
Accelerated Natural Language Processing 1.4 - Supervised and Unsupervised Learning.
Accelerated Natural Language Processing 1.5 - Class Imbalance.
Accelerated Natural Language Processing 1.6 - Missing Values.
Accelerated Natural Language Processing 1.7 - Model Evaluation.
Accelerated Natural Language Processing 1.8 - Introduction to NLP.
Accelerated Natural Language Processing 1.9 - Machine Learning and Text.
Accelerated Natural Language Processing 1.10 - Text Preprocessing.
Accelerated Natural Language Processing 1.11 - Text Vectorization.
Accelerated Natural Language Processing 1.12 - K Nearest Neighbors.
Using Jupyter Notebooks on Sagemaker.
Accelerated Natural Language Processing 2.1 - Tree-based Models.
Accelerated Natural Language Processing 2.2 - Regression Models.
Accelerated Natural Language Processing 2.3 - Optimization.
Accelerated Natural Language Processing 2.4 - Regularization.
Accelerated Natural Language Processing 2.5 - Hyperparameter Tuning.
Accelerated Natural Language Processing 3.1 - Neural Networks.
Accelerated Natural Language Processing 3.2 - Word Vectors.
Accelerated Natural Language Processing 3.3 - Recurrent Neural Networks.
Accelerated Natural Language Processing 3.4 - Gated Recurrent Units (GRUs).
Accelerated Natural Language Processing 3.5 - Long Short Term Memory (LSTM) Networks.
Accelerated Natural Language Processing 3.6 - Transformers.
Accelerated Natural Language Processing 3.7 - Single Headed Attention.
Accelerated Natural Language Processing 3.8 - Multi Headed Attention.
MLU Channel Introduction.