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An overview of Artificial Intelligence (AI) and Machine Learning (ML) on Azure. It covers the basics of AI, supervised and unsupervised learning, feature engineering, Azure ML Designer, computer vision, cognitive services, and natural language processing. The document also lists the courses and reading materials available on Azure for learning AI and ML.
Typology: Schemes and Mind Maps
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Assistant Professor Department of Commerce and Management
Vidyavihara #25/1, 17th Main, 2nd Block, Rajajinagar, Bengaluru- Accredited by NAAC with B++ Affiliated to Bengaluru City University
Al-900 pathway consists of 5 courses and 2 reading material:
https://eus-streaming-video-rt-microsoft-com.akamaized.net/78ea2ac8-4edf-4ba1-a0e3- 63daa6fdb06a/81684a6f-0783-442a-86dd-c99a1820_6750.mp
Software that exhibits human-like capabilities, such as:
Artificial Intelligence in Microsoft Azure
Cloud platform for creating and operating machine learning solutions
Cloud service for delivering conversational AI solutions
The answer is, from data. In today's world, we create huge volumes of data as we go about our everyday lives. From the text messages, emails, and social media posts we send to the photographs and videos we take on our phones, we generate massive amounts of information. More data still is created by millions of sensors in our homes, cars, cities, public transport infrastructure, and factories.
Data scientists can use all of that data to train machine learning models that can make predictions and inferences based on the relationships they find in the data.
For example, suppose an environmental conservation organization wants volunteers to identify and catalog different species of wildflower using a phone app. The following animation shows how machine learning can be used to enable this scenario.
https://learn.microsoft.com/en-us/training/wwl-data-ai/get-started-ai- fundamentals/media/machine-learn.gif
Machine Learning
https://eus-streaming-video-rt-microsoft-com.akamaized.net/95c74fa1-3db3-481d-b93d- a5a5050feecc/8e230232-1b5e-47ac-85ac-c13f00f6_6750.mp
For instance, suppose you are given a basket filled with different kinds of fruits. Now the first step is to train the machine with all the different fruits one by one like this:
If the shape of the object is rounded and has a depression at the top, is red in color, then it will be labeled as – Apple.
If the shape of the object is a long curving cylinder having Green-Yellow color, then it will be labeled as – Banana.
Now suppose after training the data, you have given a new separate fruit, say Banana from the basket, and asked to identify it.
Supervised learning is classified into two categories of algorithms:
Classification: A classification problem is when the output variable is a category, such as “Red” or “blue” , “disease” or “no disease”.
Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”
Supervised Machine learning deals with or learns with “labeled” data. This implies that some data is already tagged with the correct answer.
