Microsoft AI Classroom Series

The digital workplace of the future will need employees skilled on emerging technologies like AI, data science, and augmented reality. To become the creators and innovators of the not-too-distant future, students need to develop their tech skills on these technologies.Launching Microsoft AI Classroom series for students, in association with

NASSCOM | futureskills

Supported by GitHub

Microsoft has joined forces with NASSCOM FutureSkills® to deliver Microsoft’s AI, machine learning and data science expertise to students through easy-to-consume modules including live demos, hands-on workshop and assignments.

About the program

• Microsoft AI Classroom series is an initiative by Microsoft in association with NASSCOM FutureSkills® and supported by GitHub to train 1M students over 12 months.
• The AI Classroom series is divided into three (3) easy to consume modules of 2.5 hours each; and includes live demos, hands-on experiments, and post session assignments to enable students to check their knowledge.
• Please attend all three (3) modules to complete your learning for the series.
• Each module will be introduced by leading data scientists and AI influencers; followed by 2 hours of learning session and 30 minutes of live Q&A with subject matter experts.
• Two weeks of online support at no cost. Post the workshop series, students have access to online experts to address queries and to prepare for industry recognized AI certifications.
• This program is open to students enrolled in Indian universities and are residents of India
• Limited seats. Attendance is on first come first basis. If you are unable to join a session it may have reached capacity.

Assessment & Certification | link

• Post the workshop completion, each student will receive an assessment link for knowledge check and to collect the participation certificate.
• The assessment is designed as multiple-choice questions. You have 20 minutes to answer 30 questions.
• You will get 3 attempts to clear this assessment.
• The assessment link will be live between September 28 to October 10, 2020.
• You have to score 80% or more to pass and download your “Microsoft AI workshop | September 2020” participation certificate.
• The certificate will be a joint certificate offered by Microsoft and NASSCOM FutureSkills Share your certificate on social media and tag #AlwaysLearning. Be a proud member of the AI Skilled community.

Pre-requisites
If you need support to activate any of the following resources, please write in to StudentQ@microsoft.com
To make the most of your learning sessions and gain practical knowledge via lab exercise, please activate the following pre-requisites:

1. Environment Setup (VS Code Jupyter Notebooks)

• Visit https://code.visualstudio.com/download and download VS Code as per your machine configuration and then install Python 3. Follow this link for installation https://www.python.org/downloads/
• Next, open VS Code Editor and install Python, Jupyter, IntelliCode extension and restart it.
• Go to extension section (left side menu) and search for these extensions. Now to create a new Jupyter Notebook you need to press CTRL + Shift + P and run this command Python: Create Blank New Jupyter Notebook. Once you do this a Jupyter notebook will get created automatically.

2. Azure Student Subscription

• Visit https://azure.microsoft.com/en-in/free/students/ and click on Activate Now. If you don’t have an account, click on Create New and follow the steps
• NOTE: Provide your Institute ID to access Azure for Students Subscription

3. Azure ML Studio Classic Subscription

• Visit https://studio.azureml.net/ and Sign In

4. GitHub Account

• Click here to login into your account https://github.com/login
• To create a new account, visit https://github.com/. Click on Sign Up and follow the steps.
• You will receive an email from GitHub. Verify Email Address to activate your account.

5. GitHub Student Developer Pack

• Visit https://education.github.com/pack to get started.

Module 1: Data Science Basics and Introduction to Microsoft AI Platform | 24 sept | 8:00 to 10:30pm slot.

Ganes Kesari | Co-founder & Cheif Decision Scientist, Gramener | LinkedIn

• Tip 1: Sharpen your ability to handle data.
• Tip 2: Techniques are more important than the tools.
• Tip 3: Master the application of techniques.
• Tip 4: Learn secondary skills to stand out from the crowd.

Bhavesh Goswami | Founder & CEO, CloudThat | LinkedIn

• DataScience LifeCycle

- Types of Learning Algorithms - Supervised - Unsupervised - Semi-supervised - Algorithms - Regression - Classification - Forecasting - Clustering - Neural Networks - ANNs - Resposible AI and it's Principles

Questions for AI-900

1. A cement manufacturing company wants to predict how much cement should they manufacture on a given day. Which kind of data science problem is that?

• Clustering
• Regression
• Classification

2. As a data scientist you're required to create a model that can evaluate if a loan can be given to a customer. You have data of past customer who were given a loan, and whethr they paid or not. Which kind of algorithms will you use to create a model?

• Supervised
• Unsupervised
• Semi-supervised

3. You have a dataset from a radio taxi which the distance traveled, taxi id, and total fare. You need to generate a model that can predict fare for passenger. Which is the label for dataset?

• Distance travelled
• Taxi id
• Total fare

Module 2: Building Machine Learning Models on Azure | 25 sept | 8:00 to 10:30pm slot.

Sandeep Alur | Director Microsoft Technology Center | LinkedIn

• Introduction to the Azure AI/ML platform

1. What is a Workspace? Top level resource of Azure Machine Learning Sevice, it serves as a hub for building and deploying models. It stores the experiment objects that are required for each model we create. 2. What is a Datastore? It is an abstraction over an Azure Storage account. Each workspace has a registered default datastore, still we can register another Azure blob or File storage contianers as a datastore. 3. What is a Pipeline? It is a tool to create and manage workflows during a datascience process (data manipulation, model trainig & testing, development). Each step of the process can run unattended in different compute targets, which makes it easier to allocate resources. 

• Introduction to Azure Machine Learning Service
  • Model Management
  • Model Training
  • Model Selection
  • Hyper-paramete Tuning
  • Feature Selection
  • Model Evaluation

1. Jupyter Notebook
   • On local machine
   • Consumes resources
   • Computation power depends on the machine

2. Azure Notebook
   • similiar to jupyter notebook (as per interface)
   • we can create n' run the notebook using free resources provided by azure (FREE COMPUTE)

3. Azure Machine Learning Studio Classic
   • Visual Drag and Drop ML Training and Development
   • Complete Machine Learning Environment
   • Ideal for learning and beginner data scientists

Workflow
import data →explore and create summaries →pre-process and clean the data →algorithm selection →model training and tuning →deploy and consume

4. Automated Machine Learning

FAQs | MOC and AZURE pass

1. What Azure pass?
   To access Azure platform and try out services, you need subscription. So Microsoft is giving 50$ as Azure pass. You can utilize these credits for creating azure resources.
2. Any Guides for MOC and Azure pass?
   MOC Guide, Azure Pass Guide

more info

• Announcement: GoDo links for Module 1 http://aka.ms/Module1Reference, http://aka.ms/Module1GoDo , http://aka.ms/AI900Certification
• Any questions post session can be asked at StudentQ@microsoft.com

There will be similar sessions from 19-Oct to 24-Oct, you can register!!