05-19-2022, 04:39 PM
Looks very interesting and good value at $2K. It's a 15-week program that tackles highly in-demand topics. Admission requires a prior masters degree (MBA/MS/MA/MPA/MPH/etc) and "basic knowledge of statistics and previous experience working with a statistical software package".
https://brownschool.wustl.edu/Resources-...-Data.aspx
This is what their post-masters certificates look like:
https://archive.ph/YCBwl
Coursework includes:
Weeks 1-2 - Participants will receive an overview of artificial intelligence, learn to code in Python, use NumPy and Pandas to do data wrangling, and use Matplotlib to do data visualization.
Weeks 3-7 – Class content will focus on machine learning techniques. We will start with an end-to-end machine learning project using Scikit-Learn. From there, we will move on to topics including classification and regression, model training and validation, support vector machines, decision trees, ensemble methods, dimensionality reduction, and unsupervised learning.
Weeks 8-15 - The final portion of the program considers deep learning techniques (i.e., neural networks). Topics covered include: neural networks for regression and classification; computer vision using Keras and TensorFlow2; advanced computer vision using fastai2, PyTorch, and IceVision; time series forecasting and recommender system using Keras, TensorFlow2, and fastai2; natural language processing using Keras and TensorFlow2; advanced natural language processing using Hugging Face; and generative deep learning for image modification and generation.
https://brownschool.wustl.edu/Resources-...-Data.aspx
This is what their post-masters certificates look like:
https://archive.ph/YCBwl
Coursework includes:
Weeks 1-2 - Participants will receive an overview of artificial intelligence, learn to code in Python, use NumPy and Pandas to do data wrangling, and use Matplotlib to do data visualization.
Weeks 3-7 – Class content will focus on machine learning techniques. We will start with an end-to-end machine learning project using Scikit-Learn. From there, we will move on to topics including classification and regression, model training and validation, support vector machines, decision trees, ensemble methods, dimensionality reduction, and unsupervised learning.
Weeks 8-15 - The final portion of the program considers deep learning techniques (i.e., neural networks). Topics covered include: neural networks for regression and classification; computer vision using Keras and TensorFlow2; advanced computer vision using fastai2, PyTorch, and IceVision; time series forecasting and recommender system using Keras, TensorFlow2, and fastai2; natural language processing using Keras and TensorFlow2; advanced natural language processing using Hugging Face; and generative deep learning for image modification and generation.