Applied Data Science Lab ,WQU
Earners of this badge have completed eight end-to-end, applied data science projects. In each project, they accessed data from files, SQL and NoSQL databases and APIs. They have demonstrated their ability to explore and clean data, create functions and ETL pipelines to prepare training sets. They have built machine learning models for supervised and unsupervised learning tasks, and have created visualizations to explain data characteristics and model predictions for non-technical audiences.
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Deep Learning Specialization ,Coursera
Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications.
Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow.
Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data.
Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering.