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Natural Language Processing

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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.

Taught by

Machine Learning University

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