An introduction to the basic concepts of AI/ML and some modern tools that are beneficial for business students

1. AI/ML in Business: Unlocking Opportunities and
Navigating Challenges
Dr. Sivaranjan Goswami
Python Backend and Data Engineer
TechVariable Private Limited, Guwahati
sivaranjan@techvariable.com

2. Plan of Talk
I. Introduction
II. Understanding AI/ML
III. Key Concepts in AI/ML
IV. AI/ML Applications in Business
V. Ethical Considerations and Challenges
VI. Implications for Business Leaders
VII. Conclusion
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3. Basic Concepts of AI/ML
AI (Artificial Intelligence) refers to the development of computer systems that
can perform tasks that would typically require human intelligence. Machine
Learning (ML) is a subset of AI that focuses on algorithms and models that
allow computers to learn from data and make predictions or decisions without
explicit programming. ML enables systems to analyze vast amounts of data,
identify patterns, and improve their performance over time. These technologies
have applications in various fields, including healthcare, finance, autonomous
vehicles, and more, revolutionizing industries and shaping the future of
technology.

4. Artificial Intelligence vs Machine
Learning
AI is a broader field that encompasses various
techniques and approaches, while ML is a specific
subset of AI that focuses on algorithms and models
that enable computers to learn and make decisions
based on data.

5. Artificial Intelligence
● AI refers to the development of computer systems that can
perform tasks that typically require human intelligence, such
as speech recognition, problem-solving, and
decision-making.
● AI aims to create machines that can simulate human
intelligence, allowing them to perceive the environment,
reason, learn, and interact with humans in a natural way.
● AI encompasses a wide range of applications, including
virtual assistants, autonomous vehicles, image recognition
systems, and personalized recommendations in online
platforms.

6. Machine Learning
● ML is a subset of AI that focuses on developing algorithms
and models that allow computers to learn and make
predictions or decisions based on data, without being
explicitly programmed.
● ML algorithms learn from examples or data inputs, identifying
patterns and relationships to make informed predictions or
take actions.
● ML is used in various applications, such as spam filters,
recommendation systems (like personalized movie
recommendations), fraud detection, and self-driving cars.

7. Branches of AI
● Machine Learning (ML)
● Natural Language Processing (NLP)
● Computer Vision
● Expert Systems
● Robotics
● Knowledge Representation and
Reasoning
● Planning and Scheduling
● Neural Networks

8. Common AI Problems
● Classification problems: Predicting the category or class to which an
observation belongs.
● Regression problems: Predicting a numerical value or quantity based on
input variables.
● Reinforcement learning: An approach to machine learning where an
agent learns to take actions in an environment to maximize a reward
signal.
● Natural language processing: The branch of AI that involves the
interaction between computers and human language, including tasks like
text analysis and language generation.
● Computer vision: The field of AI that deals with enabling computers to
extract information and understand the content of images or videos.

9. Supervised Learning
● Supervised learning is a machine learning
approach where we have labeled data that
helps our model learn patterns and make
predictions.
● It involves a clear relationship between input
variables (features) and output variables
(labels).
Example: Predicting Email Spam, Identifying
objects in an image, Recommendation system,
Fraud detection

10. Unsupervised Learning
Unsupervised learning is a machine learning
approach where we have unlabeled data, and
the model must find patterns or structure on its
own.
It focuses on discovering hidden relationships,
clusters, or patterns within the data.
Example:
Customer segmentation, Anomaly detection (for
example in financial transactions)

11. Semi-Supervised Learning
● Combination of labeled and unlabeled data.
● Leveraging limited labeled data to enhance
performance.
● Use cases: document classification, speech
recognition.
● Semi-supervised learning provides a middle
ground when labeled data is scarce but still allows
us to benefit from the power of supervised
learning with the aid of unsupervised learning.

12. Reinforcement Learning
Reinforcement learning is a machine learning
approach where an agent learns by interacting
with an environment. The agent takes actions,
receives feedback in the form of rewards or
penalties, and adjusts its behavior based on
the outcomes.
Example: Training an autonomous robot,
Solving complex mathematical problems.

13. 6 Revolutionary AI Tools You Need to Know
About

14. AI Art Generator
● Can combine concepts,
attributes, and styles and
expand beyond the original
canvas.

15. Versatile AI Chatbot
● ChatGPT is an AI chatbot
that can perform many
functions beyond writing,
including coding,
conversation, and solving
math equations.
● Available to the public at no
cost.

16. Generative AI
● Generative AI systems can
be used to create new
content, including audio,
music, art, and entire virtual
worlds.
● Have practical uses such as
generating high-resolution
weather forecasts.

17. Image Generation and Editing AI
Tool
● Can create different
variations of an image from a
single original.

18. AI Tool for Note-taking and
Scheduling
● Fireflies.ai is an AI tool that
assists in taking notes,
scheduling, and transcribing.
● Free version with limited
features, pro version starts at
$10/month.

19. Excel Formula Bot
● Excel Formula Bot is an AI
tool that can generate
formulas in Microsoft Excel,
making it a practical AI tool
for businesses.
● Saves time and effort in
financial planning and data
analysis.

20. THANK YOU