uninapoli-csml

Presentation

Me

Name: Francesco Venturini

• Role: Solution Architect / Senior DevOps Software Engineer for Proge-Software
  • Web Solutions
  • Mobile Solutions
  • Software Architectures
  • Cloud (Azure)
  • Containers (Docker)
  • Kubernetes
• LinkedIn: https://www.linkedin.com/in/francesco-venturini-2071a251/
• GitHub: https://github.com/ciacco85

Collaborators

Francesco Ilario

• Role: Software Engineer for Proge-Software
  • Web Solutions
  • Cloud (Azure)
  • Cognitive Services
  • Bots
  • Containers (Docker)
  • Kubernetes
• GitHub: https://github.com/FrancescoIlario
• LinkedIn: https://www.linkedin.com/in/francesco-ilario-0ba083165/

Caterina Frollo

• Role: Data Scientist
  • Python
  • Jupyter Notebook
  • MS Power BI
• GitHub: https://github.com/poessevero
• LinkedIn: https://www.linkedin.com/in/caterina-frollo/

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/

Agenda

1. Presentation :clock230: 14:30
   • Who Am I
   • Who Is Proge-Software
2. Introduction 14:45
   • Python
     • Pandas
     • NumPy
     • Matplotlib
     • Seaborn
     • Scikit-learn
   • Jupyter Notebook
   • Anaconda
   • Machine Learning
     • Supervised Learning
     • Unsupervised Learning
   • Preprocessing data
   • Forecast Analysis
     • Prophet
     • ARIMA: Autoregressive integrated moving average
3. Azure Machine Learning Studio (Preview):clock330: 15:30
   • What is Azure ML Studio (Preview)
     • 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
     • Notebooks
     • Work with Computes
       • Web
       • Command Line
4. Demo :clock4: 16:00
   • Setup Azure ML Studio
   • Data
     • Preprocessing the Data
     • Code explanation
   • Train the Prophet model and prediction of the trend
   • Test the Prophet model
5. Where to go next 16:20
   • Neural Networks
     • Tensorflow
     • Keras
     • pyTorch
6. Q&A 16:25