lectio-uninapoli-2021

Presentation

Proge-Software

Since 1985 Proge-Software produces high-technological content projects for Small and Medium Enterprises, providing them all the necessary activities for the design, development and maintenance of the corporate IT system: from IT architecture design to complex IT infrastructure management, from the realization of Custom Software Development to the implementation of mobile solutions, from the creation of application infrastructure and databases to the design of Cloud scenarios, up to the creation of portals for collaboration.

• GitHub: https://github.com/proge-software
• Site: https://www.progesoftware.it/
• LinkedIn: https://www.linkedin.com/company/proge-software/

Caterina Frollo

Data Scientist per Proge-Software

• GitHub: https://github.com/poessevero
• LinkedIn: https://www.linkedin.com/in/caterina-frollo/

Alberto Mollica Graziano

Software Engineer per Proge-Software

• GitHub: https://github.com/albertomollica
• LinkedIn: https://www.linkedin.com/in/alberto-mollica-graziano/

Agenda

1. Presentation (10:30)
   • Who Is Proge-Software
   • Who Am I
2. Introduction (10:45)
   • Python
     • Pandas
     • NumPy
     • Matplotlib
     • Seaborn
     • Scikit-learn
   • Jupyter Notebook
   • Anaconda
   • Machine Learning
     • Supervised Learning
     • Unsupervised Learning
     • Reinforcement learning
   • Preprocessing data
   • Forecast Analysis
     • ARIMA: Autoregressive integrated moving average
     • Prophet
3. Azure Machine Learning Studio (11:30)
   • What is Azure ML Studio
     • Differences with Classic version of the platform
   • Why do we need it
   • How does it work
     • Compute
       • Compute Instances
       • Compute Clusters
       • Inference Clusters
       • Attached Compute
     • Storage
     • Notebooks
     • Work with Computes
       • JupyterLab
       • Command Line
4. Demo (12:00)
   • Setup Azure ML Studio
   • Data
     • Preprocessing the Data
     • Code explanation
   • Spot and treat outliers
   • Train a Prophet model
   • Model evaluation
   • Hyperparameter tuning
5. Where to go next (12:20)
   • Artificial Neural Networks
   • Deep Learning
   • Frameworks for deep learning
     • Tensorflow
     • Keras
     • PyTorch
   • Types of Neural Network
   • NeuralProphet
6. Q&A (12:25)