Virtual Practice Labs Virtual CodeX CodeX
Architecting Advanced ML/DL Apps with Python (pit_mladj4)
Perform advanced ML/DL app architecture tasks using Python, such as loading a data set to train a simple multilayer perceptron (MLP), a Convolutional Neural Network (CNN) and an LSTM model. Then, test your skills by answering assessment questions after performing image and text classification using CNN.
This lab provides access to several tools commonly used in machine learning, including:
Microsoft Excel 2016
Visual Studio Code
Anaconda
Jupyter Notebook + JupyterHub
Pandas, NumPy, SiPy
Seaborn Library
Spyder IDE
This lab is part of the ML/DL Architect Master track of the Aspire Machine Learning Architect Journey.
This lab provides access to several tools commonly used in machine learning, including:
Microsoft Excel 2016
Visual Studio Code
Anaconda
Jupyter Notebook + JupyterHub
Pandas, NumPy, SiPy
Seaborn Library
Spyder IDE
This lab is part of the DL Engineer track of the Aspire Machine Learning Architect Journey.
Perform advanced ML/DL app architecture tasks using Python, such as loading a data set to train a simple multilayer perceptron (MLP), a Convolutional Neural Network (CNN) and an LSTM model. Then, test your skills by answering assessment questions after performing image and text classification using CNN.
This lab provides access to several tools commonly used in machine learning, including:
Microsoft Excel 2016
Visual Studio Code
Anaconda
Jupyter Notebook + JupyterHub
Pandas, NumPy, SiPy
Seaborn Library
Spyder IDE
This lab is part of the ML/DL Architect Master track of the Aspire Machine Learning Architect Journey.
This lab provides access to several tools commonly used in machine learning, including:
Microsoft Excel 2016
Visual Studio Code
Anaconda
Jupyter Notebook + JupyterHub
Pandas, NumPy, SiPy
Seaborn Library
Spyder IDE
This lab is part of the DL Engineer track of the Aspire Machine Learning Architect Journey.