2. About the Organization…
Infosys Springboard, an educational initiative by Infosys, is a pioneering platform that offers
transformative learning experiences in the fields of information technology, data science, artificial
intelligence, and more. This innovative platform is committed to providing quality education, fostering
career development, and nurturing talent in the ever-evolving landscape of technology and digital
innovation.
This initiative underscores Infosys Springboard's dedication to nurturing talent and providing learners
with valuable practical experiences.
It seeks to bridge the gap between traditional education and the demands of a rapidly changing industry
by providing learners with the tools and resources they need to succeed in the digital age.
The vision of this platform is equally ambitious — to empower individuals from diverse backgrounds to
acquire relevant skills and become future-ready in a world where technology is at the forefront of
transformation.
3. Pre-Requisites for Artificial Intelligence Primer
Introduction to Data Science
Introduction to Natural Language Processing
Introduction to Artificial Intelligence
Introduction to Deep Learning
Computer Vision 101
Introduction to Robotic Process Automation
4. Introduction to Data Science
Data Science is the study of various machine learning algorithm to find the hidden patterns in the
available data
Data Science requires skills in mathematics, statistics, business, computer and Information Technology
and domain knowledge.
The main purpose is to transfer the row data into some valuable information.
Data can be collected from various fields such as web smartphones, customers,banks etc. and we need
sources of data such as texts,videos,imageswe need analytical tools and algorithms to draw meaningful
insights.
Data is available as small in size mostly is structured, semi-structured and large in size is unstructured
form of data.
6. Introduction to Natural Language Processing
Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the
interaction between computers and human language.
NLP involves a wide array of techniques, including text preprocessing, tokenization, part-of speech
tagging, and sentiment analysis.
It enables machines to understand, interpret, and generate human language, making it a critical
component of many AI applications, such as chatbots, language translation, sentiment analysis,
and more.
Our curiosity about NLP arises from its transformative role in bridging the gap between human
communication and technology.
8. What is ARTIFICIAL INTELLIGENCE?
Artificial Intelligence (AI) is the field of computer science that aims to create machines
and systems capable of performing tasks that typically require human intelligence.
It encompasses a broad spectrum of technologies, including machine learning, robotics,
computer vision, and natural language processing.
AI systems can analyze vast datasets, make predictions, recognize patterns, and adapt to
changing environments.
AI systems can be designed to operate autonomously or with human supervision, and
can be trained on large amounts of data to improve their accuracy and effectiveness.
9. Need for Artificial Intelligence
Artificial Intelligence (AI) is being used in a variety of engineering applications
to improve efficiency, accuracy, and decision-making.
Here are some of the key types of AI used in engineering:
Machine learning: Machine learning involves training algorithms to learn from
data and improve over time.
Computer vision: Computer vision involves using AI algorithms to interpret
visual data, such as images and videos.
Robotics: Robotics involves using AI to develop intelligent machines that can
perform tasks autonomously.
Expert systems: Expert systems involve using AI to replicate the decision-
making processes of human experts..
11. Introduction to Deep Learning
Deep Learning is a subfield of machine learning that focuses on neural networks with multiple layers,
known as deep neural networks.
It has gained immense popularity due to its ability to handle complex data and perform tasks such as
image and speech recognition, natural language understanding, and autonomous decision-making.
Deep learning models, including convolutional neural networks (CNNs) and recurrent neural
networks (RNNs), have shown remarkable performance in various applications.
Deep learning has the potential to drive innovation in fields such as healthcare, finance, and
autonomous robotics.
13. What is Computer Vision ?
Computer vision is a subset of AI that focuses on teaching machines to interpret and understand
visual information from the world, including images and videos.
Our interest in computer vision arose from its ability to extract valuable insights from visual data.
Computer vision techniques involve image preprocessing, feature extraction, and deep learning
based object recognition.
This knowledge can enable us to develop applications such as self-driving cars, medical diagnostic
tools, and augmented reality experiences.
We aimed to grasp the fundamental principles of computer vision and its role in various industries.
15. What is Robotic Process Automation?
Robotic Process Automation (RPA) is a transformative technology that involves the use of
software robots or "bots" to automate repetitive, rule-based tasks and processes within an
organization.
These software robots are designed to mimic human actions, interacting with digital
systems and applications to execute tasks with speed, accuracy, and consistency.
Robotic Process Automation is a pivotal technology in the digital transformation journey
of organizations, offering improved operational efficiency, reduced costs, and increased
agility.
It plays a crucial role in optimizing business processes across various sectors, ultimately
driving competitiveness and innovation.
