This course on Natural Language Processing aims to teach learners about machine learning applications in NLP, supervised and unsupervised learning, text preprocessing, text vectorization, and various models like neural networks, LSTM networks, and transformers. The course utilizes Jupyter Notebooks on Sagemaker and covers topics such as model evaluation, class imbalance, and hyperparameter tuning. The intended audience for this course includes individuals interested in advancing their knowledge and skills in natural language processing and machine learning.
Overview
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
