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Students will be able to: Machine Learning in a nutshell Calculation of of pearson
correlation coefficient in MS –
MACHINE LEARNING ALGORITHMS Apply these methods to develop Understanding Correlation, Demonstration of k – Nearest
Differentiate the different types of
Types of Machine Learning
Excel.
machine learning methods.
Supervised Learning
Linear
Demonstration
They will be able to understand
regression in MS – Excel /
the concept behind each machine
Regression, Finding the line,
using python program.
learning methods.
Linear Regression algorithm
Classification – How it works,
Neighbour
python
using
simple solutions for some day-to- day
Types, k – Nearest Neighbour
program.
situations.
algorithm
Demonstration of k – means
Build up this knowledge to the next
Unsupervised Learning
clustering
using
level to apply during Capstone Project
program.
Clustering – How it works, Types,
development.
k -means Clustering algorithm
IBM SkillsBuild - Machine
learning with Python python
Students will be able to: Understanding to Natural Write an article on “IBM Project
Human
Debater – Interesting facts”
Language Complexity
Develop a better understanding of
LEVERAGING LINGUISTICS AND COMPUTER SCIENCE Learn new techniques and algorithms Phases of NLP and Sentiment following platforms:
Create a chatbot on ordering
the complexities of language and the
Introduction
Language Processing (NLP) -
ice-creams using any of the
challenges involved in NLP tasks.
Emotion
for NLP tasks.
Detection
Google Dialogflow
o
Analysis, Classification Problems,
o
Botsify.com
Chatbot
o
Botpress.com
IBM SKillsBuild - Natural
Applications of NLP
Language Processing
Students will be able to: Ethics in Artificial Intelligence Summarize your insights and
Demonstrate an understanding of the The five pillars of AI Ethics interpretations from the video
fundamental principles of ethics and Bias, Bias Awareness, Sources of "Humans need not apply.”
gain insight into ethical considerations Bias Activity: Role Play on biased AI
related to AI technologies. Mitigating Bias in AI Systems systems
AI ETHICS AND VALUES Identify and apply strategies for Moral Machine Game Comparative guidelines involve
Develop an understanding of AI
IBM SkillsBuild - AI Ethics
Developing AI Policies
bias, its sources, and its real-world
of
study
implications, as well as the ethical
policies
(that
AI
considerations.
examining
and
Survival of the Best Fit Game
by
established
principles)
mitigating bias in AI systems to
and
organizations
various
regulatory bodies
promote fairness and transparency in
technology.
Recognize the significance of AI Understanding ethical
dilemma using
policies in promoting responsible, Moral machine Survival of the
safe, and ethical use of AI technologies. best fit
PART – C
1. Practical File
Note: The following to be included in Practical File
• one certification (IBM Skills Build /any other industry certification)
• at least one activity from each unit
• one participation certificate of bootcamp/internship

