One mark questions:

1. What is Artificial Intelligence?

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or software
think intelligently.

2. Who is the father o f

AI?
John McCarthy.

3. What is Azure?
Azure is a cloud computing platform and an online portal that allows you to access and manage
cloud services and resources provided by Microsoft.

4. What is Machine Learning?

Machine learning is a technique that uses mathematics and statistics to create a model that can
predict unknown values.

5. What is Azure Machine Learning studio?

Azure Machine Learning is a service for training and managing machine learning models, for
which you need compute on which to run the training process.

6. What is Azure Automated Machine Learning?

Azure Machine Learning includes an automated machine learning capability that automatically
tries multiple pre-processing techniques and model-training algorithms in parallel.

7. What is Natural Language Processing?

Natural language processing is the technology that is used by machines to understand, analyze,
manipulate and interpret human’s languages.

8. What is Conversational AI?

Conversational AI is a term used to describe various methods of enabling computers to carry on
a conversation with a human.
9. What is Data Analytics?
Data analytics is the science of analyzing raw data to make conclusions about that information.

10. What is Text Analysis?

Text analysis is the process of extracting information from text data.

11. What is Computer Vision?

Computer vision is the ability of a computer to interpret and understand digital images.

12. What is Speech Recognition?

Speech recognition is the ability of a computer to understand human speech.

Five marks questions.

1. What is Computer Vision? Explain its uses.
Computer vision is one of the core areas of artificial intelligence (AI), and focuses on creating
solutions that enable AI applications to "see" the world and make sense of it.
Potential uses for computer vision include:
 Content Organization: Identify people or objects in photos and organize them based on that
identification. Photo recognition applications like this are commonly used in photo storage and
social media applications.
 Text Extraction: Analyze images and PDF documents that contain text and extract the text into
a structured format.
 Spatial Analysis: Identify people or objects, such as cars, in a space and map their movement
within that space.
 To an AI application, an image is just an array of pixel values. These numeric values can be used
as features to train machine learning models that make predictions about the image and its
contents.

2. Explain steps for analyzing images with the Computer Vision service.
 Describing an image: Computer Vision has the ability to analyze an image, evaluate the objects
that are detected, and generate a human-readable phrase or sentence that can describe what was
detected in the image
 Tagging visual features: Computer Vision is based on a set of thousands of recognizable
objects, which can be used to suggest tags for the image.
 Detecting objects: Not only getting the type of object, but also receiving a set of coordinates
that indicate the top, left, width, and height of the object detected, which can be used to identify
the location of the object in the image.
 Detecting brands: This feature provides the ability to identify commercial brands. The service
has an existing database of thousands of globally recognized logos from commercial brands of
products.
 Detecting faces: The Computer Vision service can detect and analyze human faces in an image,
including the ability to determine age and a bounding box rectangle for the location of the
face(s).

3. Explain the types of Machine Learning?

There are two general approaches to machine learning, supervised and unsupervised machine
learning.
The supervised machine learning approach requires starting with a dataset with known label
values. Two types of supervised machine learning tasks include regression and classification.
 Regression: used to predict a continuous value; like a price, a sales total, or some other measure.
 Classification: used to determine a binary class label; like whether a patient has diabetes or not.
The unsupervised machine learning approach starts with a dataset without known label
values. One type of unsupervised machine learning task is clustering.
 Clustering: used to determine labels by grouping similar information into label groups;
like grouping measurements from birds into species.

4. What are the different types of Azure Machine Learning Compute

Resource? There are four kinds of compute resource:
 Compute Instances: Development workstations that data scientists can use to work with data
and models.
 Compute Clusters: Scalable clusters of virtual machines for on-demand processing of
experiment code.
 Inference Clusters: Deployment targets for predictive services that use your trained models.
 Attached Compute:Links to existing Azure compute resources, such as Virtual Machines or
Azure Databricks clusters.
5. What are the steps to be followed to understand the AutoML
process? The steps in a auto machine learning (AutoML) process are:
 Prepare data: Identify the features and label in a dataset. Pre-process, or clean and transform,
the data as needed.
 Train model: Split the data into two groups, training and a validation set. Train a Machine
learning model using the training data set. Test the machine learning model for performance
using the validation data set.
 Evaluate performance: Compare how close the model's predictions are to the known labels.
 Deploy a predictive service: After training a machine learning model, the model can be
deployed as an application on a server or device so that others can use it.

6. What are the key areas used during the data analysis process.
 Prepare: Data preparation is the process of taking raw data and turning it into information that
is trusted and understandable.
 Model: Data modeling is the process of determining how the tables are related to each other.
This process is done by defining and creating relationships between the tables.
 Visualize: Visualizing data is for designing and creating reports for accessibility. By using
appropriate visualizations, an effective report can be provided that guides the reader through the
content quickly and efficiently.
 Analyze: It is important to understand and interpret the information that is displayed on the
report.
 Manage: Power BI consists of many components including reports, dashboards, workspaces,
datasets, and more. Proper management can also help to reduce data repository within the
organization.

7. What are the elements of AI?

The key elements of AI include:
 Natural Language Processing: NLP is a branch of AI that allows machines to use and
understand human language. It is built into products such as automatic language translators used
in multilingual conferences, text-to-speech translation, speech-to-text translation, and knowledge
extraction from text.
 Expert Systems: Expert systems are machines or software applications that provide explanation
and advice to users through a set of rules provided by an expert.
 Robotics: Intelligent robots are mechanical structures in various shapes that are programmed to
perform specific tasks based on human instructions.
 Intelligent Agents: Multi-agent systems (MAS) are a subfield of AI that builds systems capable
of making decisions and take actions autonomously.
 Computational Intelligence: Computational Intelligence is the aspect of AI that focuses on
utilizing and deriving value from data.

8. Explain the Text Analytics Techniques.

There are some commonly used techniques that can be used to build software to analyze text,
including:
 Statistical analysis of terms used in the text. For example, removing common "stop words" and
performing frequency analysis of the remaining words can provide clues about the main subject
of the text.
 Extending frequency analysis to multi-term phrases
 Applying stemming algorithms to normalize words before counting them
 Applying linguistic structure rules to analyze sentences contains nouns, verbs, adjectives, and so
on.
 Encoding words or terms as numeric features that can be used to train a machine learning model
 Creating vectorized models that capture semantic relationships between words by assigning them
to locations in n-dimensional space.

9. How Does Conversational AI Work?

Driven by underlying machine learning and deep neural networks (DNN), a typical conversational
AI flow includes:

 An interface that allows the user to input text into the system or Automatic Speech Recognition
(ASR), a user interface that converts speech into text.
 Natural language processing (NLP) to extract the user's intent from the text or audio input, and
translate the text into structured data.
 Natural Language Understanding (NLU) to process the data based on grammar, meaning, and
context; to comprehend intent and entity; and to act as a dialogue management unit for building
appropriate responses.
 An AI model that predicts the best response for the user based on the user's intent and the AI
model's training data. Natural Language Generation (NLG) infers from the above processes, and
forms an appropriate response to interact with humans.

Ten marks questions:

1. Explain the different capabilities of Language service.
i. Language detection
Use the language detection capability of the language service to identify the language in which
text is written. Multiple documents can be submitted at a time for analysis. For each document
submitted to it, the service will detect:
 The language name (for example "English").
 The ISO 6391 language code (for example, "en").
 A score indicating a level of confidence in the language detection.
ii. Sentiment analysis
The text analytics capabilities in the Language service can evaluate text and return sentiment
scores and labels for each sentence. This capability is useful for detecting positive and negative
sentiment in social media, customer reviews, discussion forums and more.
Using the pre-built machine learning classification model, the service evaluates the text and
returns a sentiment score in the range of 0 to 1, with values closer to 1 being a positive
sentiment. Scores that are close to the middle of the range (0.5) are considered neutral or
indeterminate.
iii. Key phrase extraction
Key phrase extraction is the concept of evaluating the text of a document, or documents, and
then identifying the main talking points of the document(s). Ex: Restaurant scenario.

You might receive a review such as:

"We had dinner here for a birthday celebration and had a fantastic experience. We were greeted
by a friendly hostess and taken to our table right away. The ambiance was relaxed, the food was
amazing, and service was terrific. If you like great food and attentive service, you should try this
place."
Key phrase extraction can provide some context to this review by extracting the phrases such as,
attentive service, great food, birthday celebration, fantastic experience, table, friendly hostess,
dinner, ambiance, place.
 iv. Entity recognition
Language service can be provided with unstructured text and it will return a list of entities in the
text that it recognizes. The service can also provide links to more information about that entity
on the web. An entity is essentially an item of a particular type or a category; and in some cases,
subtype, such as those as shown in the following table.
For example, suppose the language service is used to detect entities in the restaurant review
extract: "I ate at the restaurant in Seattle last week."

2. Explain the Components of Conversational AI.

Conversational AI can be broken down into five core components.
i. Natural language processing
NLP is the ability of a computer to understand human language and respond in a way that is
natural for humans. This involves understanding the meaning of words and the structure of
sentences, as well as being able to handle idiomatic expressions and slang.
 NLP is used to train computers to understand language.
ii. Machine learning
Machine learning is a field of artificial intelligence that enables computers to learn from data
without being explicitly programmed.
 Machine learning is used to train computers to understand language, as well as to recognize
patterns in data.
 It is also used to create models of how different things work, including the human brain.
iii. Text analysis
Text analysis is the process of extracting information from text data. This involves identifying
the different parts of a sentence, such as the subject, verb, and object, different types of words in
a sentence, such as nouns, verbs, and adjectives.
 Text analysis is used to understand the meaning of a sentence, as well as the relationships
between different words.
 It is also used to identify the topic of a text, as well as the sentiment (positive or negative) of
the text.
iv. Computer vision
Computer vision is the ability of a computer to interpret and understand digital images. This
involves identifying the different objects in an image, as well as the location and orientation of
those objects.
  Computer vision is used to identify the contents of an image, as well as the relationships
between different objects in the image.
 It is also used to interpret the emotions of people in photos, and to understand the context of a
photo.
v. Speech recognition
Speech recognition is the ability of a computer to understand human speech. This involves
recognizing the different sounds in a spoken sentence, as well as the grammar and syntax of the
sentence.
 Speech recognition is used to convert spoken words into text, and to understand the meaning
of the words.

3. How to create Conversational AI?

i. Start by understanding the use cases and requirements

The first step in creating conversational AI understands the organization’s specific needs and use
cases. What are you trying to achieve with your chatbot? What type of conversations do you
want it to be able to have? What data do you need to collect and track? Defining these
requirements helps to determine the best approach to creating the chatbot.
ii. Choose the right platform and toolkit
There are a number of different platforms and toolkits that you can use to create conversational
AI. Each platform has its own strengths and weaknesses, so choose the platform that best suits
your needs. Some popular platforms include Microsoft Bot Framework, Amazon Lex, Google
Dialog flow, and IBM Watson.
iii. Build a prototype
Once the requirements have been defined and chosen a platform, it’s time to start building the
prototype. Building a prototype will help to test the chatbot and iron out any kinks before
deploying it to the users.
iv. Deploy and test your chatbot
Once the prototype is finished, it’s time to deploy and test the chatbot. Make sure to test it with a
small group of users first to get feedback and make any necessary adjustments.
v. Optimize and improve your chatbot
The final step is to continually optimize and improve the chatbot. This can be done by tweaking
the algorithms, adding new features, and collecting user feedback.
4. Explain the classification of core components of analytics?
i. Descriptive analytics
Descriptive analytics help answer questions about what has happened based on historical data.
Descriptive analytics techniques summarize large datasets to describe outcomes to stakeholders.
Example of descriptive analytics is generating reports to provide a view of an organization's
sales and financial data.
ii. Diagnostic analytics
Diagnostic analytics help answer questions about why events happened. Diagnostic analytics
techniques supplement basic descriptive analytics, and they use the findings from descriptive
analytics to discover the cause of these events. Then, performance indicators are further
investigated to discover why these events improved or became worse. Generally, this process
occurs in three steps:
 Identify anomalies in the data.
 Collect data that's related to these anomalies.
 Use statistical techniques to discover relationships and trends that explain these anomalies.
iii. Predictive analytics
Predictive analytics help answer questions about what will happen in the future. Predictive
analytics techniques use historical data to identify trends and determine if they're likely to recur.
Predictive analytical tools provide valuable insight into what might happen in the future.
Techniques include a variety of statistical and machine learning techniques such as neural
networks, decision trees, and regression.
iv. Prescriptive analytics
Prescriptive analytics help answer questions about which actions should be taken to achieve a
goal or target. This technique allows businesses to make informed decisions in the face of
uncertainty. Prescriptive analytics techniques rely on machine learning as one of the strategies to
find patterns in large datasets. By analyzing past decisions and events, organizations can estimate
the likelihood of different outcomes.
v. Cognitive analytics
Cognitive analytics attempt to draw inferences from existing data and patterns, derive
conclusions based on existing knowledge bases, and then add these findings back into the
knowledge base for future inferences, a self-learning feedback loop. Cognitive analytics help you
learn what might happen if circumstances change and determine how you might handle these
situations.
5. Explain the applications of AI.
i. AI in Astronomy: AI technology can be helpful for understanding the universe such as how it
works, origin, etc.
ii. AI in Healthcare: Healthcare Industries are applying AI to make a better and faster diagnosis
than humans.
iii. AI in Gaming: The AI machines can play strategic games like chess, where the machine
needs to think of a large number of possible places.
iv. AI in Finance: The finance industry is implementing automation, chatbot, adaptive
intelligence, algorithm trading, and machine learning into financial processes.
v. AI in Data Security: The security of data is crucial for every company and cyber-attacks are
growing very rapidly in the digital world. AI can be used to make your data more safe and
secure.
vi. AI in Social Media: AI can organize and manage massive amounts of data. AI can analyze
lots of data to identify the latest trends, hash tag, and requirement of different users.
vii. AI in Travel & Transport: Travel industries are using AI-powered chat bots which can
make human-like interaction with customers for better and fast response.
viii. AI in Automotive Industry: Some Automotive industries are using AI to provide virtual
assistant to their user for better performance.
ix. AI in Robotics: With the help of AI, we can create intelligent robots which can perform tasks
with their own experiences without pre-programmed.
x. AI in Entertainment: Currently AI based applications are being used in our daily life with
some entertainment services such as Netflix or Amazon. With the help of ML/AI algorithms,
these services show the recommendations for programs or shows.