KLiC AI-ML Basics | MKCL's KLiC

The KLiC AI-ML course by offers a thorough exploration of Artificial Intelligence (AI) and Machine Learning (ML). Designed for individuals of all levels, it covers historical context, intelligence concepts, and ethical considerations in AI, functioning of AI-ML models, and AI-ML apps to make daily tasks more productive.

Course Features:

Language: English
Duration: 60 hours (1 months)
Learning Mode: Center
Jurisdiction: Nationwide
Certificate of Completion

Eligibility

Learner should preferably a std. 10th Pass student (Not Compulsory)
It is desirable that Learner should have done MS-CIT Course (Not Compulsory)

List of ALCs

Introduction

The course illustrates AI's impact across sectors like healthcare and finance, explores AI fields deeply, and introduces practical AI tools for education. It covers machine learning fundamentals, data science integration, and features case studies of AI in major companies. The course concludes with insights into AI adoption, tech giants, career prospects, and government initiatives, making it a comprehensive guide in the AI and ML landscape.

Who Should Join

Students and Graduates in Computer Science and Related Fields: To gain foundational knowledge in AI and ML, enhancing academic and career prospects.
IT Professionals Seeking Skill Enhancement: To stay updated with emerging technologies and integrate AI-ML concepts into their work.
Data Analysts and Aspiring Data Scientists: To understand AI-ML models and their applications in data analysis and predictive modeling.
Business Professionals and Managers: To leverage AI-ML for strategic decision-making and improving business processes.
Entrepreneurs and Start-up Enthusiasts: To explore AI-ML applications for innovative product development and business solutions.
Educators and Trainers: To incorporate AI-ML concepts into curricula and training programs.
Individuals Interested in Emerging Technologies: To gain insights into AI-ML and their impact on various industries.

What you'll learn ?

A unique aspect of the course is the inclusion of "Case Studies of AI and ML," where learners will analyse the practical applications of AI and ML in major companies such as Google, Microsoft, YouTube, Uber, Amazon, Apple, Netflix, Facebook, Paytm, and Zomato. These case studies o;er real-world insights into how these companies leverage AI for search algorithms, fraud detection, personalized recommendations, and more.

Certification

MKCL provides certificate to the KLiC learner after his/her successful course completion

Academic Approach

The academic approach of the courses focuses on the “work-centric” education i.e. begin with work (and not from a book!), derive knowledge from work and apply that knowledge to make the work more wholesome, useful and delightful. The ultimate objective is to empower the Learner to engage in socially useful and productive work. It aims at leading the learner to his/her rewarding career as an employee or entrepreneur as well as development of the community to which s/he belongs. Learning methodology:

Step -1: Learners are given an overview of the course and its connection to life and work.
Step -2: Learners are exposed to the specific tool(s) used in the course through the various real-life applications of the tool(s).
Step -3: Learners are acquainted with the careers and the hierarchy of roles they can perform at workplaces after attaining increasing levels of mastery over the tool(s).
Step -4: Learners are acquainted with the architecture of the tool or tool map so as to appreciate various parts of the tool, their functions, utility and inter-relations.
Step -5: Learners are exposed to simple application development methodology by using the tool at the beginner’s level.
Step -6: Learners perform the differential skills related to the use of the tool to improve the given ready-made industry-standard outputs.
Step -7: Learners are engaged in appreciation of real-life case studies developed by the experts.
Step -8: Learners are encouraged to proceed from appreciation to imitation of the experts.
Step -9: After the imitation experience, they are required to improve the expert’s outputs so that they proceed from mere imitation to emulation.
Step-10: Emulation is taken a level further from working with differential skills towards the visualization and creation of a complete output according to the requirements provided. (Long Assignments)
Step-11: Understanding the requirements, communicating one’s own thoughts and presenting are important skills required in facing an interview for securing a work order/job. For instilling these skills, learners are presented with various subject-specific technical as well as HR-oriented questions and encouraged to answer them.
Step-12: Finally, they develop the integral skills involving optimal methods and best practices to produce useful outputs right from scratch, publish them in their ePortfolio and thereby proceed from emulation to self-expression, from self-expression to self-confidence and from self-confidence to self-reliance and self-esteem!

Syllabus

Introduction to Artificial Intelligence and Machine Learning

Kunal's musical adventure with ‘Spotify’

Objectives
Introduction to AI
History of AI
Definition and its importance
What is Generative AI?
Teaching machines to mimic human intelligence
Understanding the basics of AI
Concept of 'Intelligence'
Human Intelligence vs. Machine Intelligence
Types of AI
Narrow AI
General AI
Super intelligent AI
Components of AI
Understanding how machines learn from data
Power of 'Reasoning'
The ability of problem-solving
Discussing how machines interpret sensory information
Linguistic Intelligence
Perception and Computer Vision
Robotics and Motion
Knowledge Representation
Planning and Navigation
Ethics in AI
The 3 key areas of AI ethics
Privacy and data protection
Bias and discrimination
How to establish AI ethics?
Sustainability in AI
Recap and outcome
FAQs
Summary

Objectives
Overview of AI and its growing impact
Understanding AI and its evolution
Importance of Human-AI interaction
Benefits of AI integration in various sectors
Human-AI interaction and collaboration
Balancing automation and human expertise
AI as a supportive tool in decision-making
Fostering trust and understanding between 'Humans' and 'AI'
AI in Healthcare
Medical diagnostics and imaging
Drug discovery and development
Virtual health assistants
AI in Finance
Enhancing fraud detection and risk management
Automated trading systems and market analysis
AI-Powered personalized financial advice
AI in Business and Marketing
AI-driven market analysis and trends
Personalization in customer engagement
AI-powered digital advertising
AI in Education
Personalized learning platforms
AI-driven tutoring and assessment
Adaptive educational content
AI in Manufacturing
Automation and robotics
Predictive maintenance
Quality control and inspection
AI in Fashion
Personalized shopping experience with AI recommendations
AI-driven design and trend prediction
Supply chain optimization and inventory management
AI in Agriculture
Precision farming and crop monitoring
AI-based pest and disease detection
Smart irrigation and yield prediction
AI in Entertainment and Media
Content recommendation systems
AI-generated art and music
Virtual reality and augmented reality experiences
AI in Environmental Sustainability
Climate Modeling and prediction
Smart grids and energy management
Conservation and wildlife monitoring
Recap and outcome
Summary

Objectives
Introduction to Machine Learning
Key components and processes of Machine Learning
How Algorithms work in Machine Learning?
Applications of Machine Learning
Benefits and drawbacks of Machine Learning
Introduction to Deep Learning
Artificial Neural Networks
Structure and components of Artificial Neural Networks
Overview of Neural Network types
Benefits and drawbacks of Deep Learning
Introduction to Natural Language Processing (NLP)
Applications of NLP in Machine Learning
Introduction to Computer Vision
Technologies involved in Computer Vision
Introduction to Cognitive Computing
Benefits of Cognitive Computing
Summary

Objectives
Introduction to AI tools
The importance of AI tools in education
Role of Global Accessibility
A brief overview of various AI tools
Commonly used AI tools and frameworks
Other commonly used tools and frameworks
Exploring Grammarly
Using Grammarly for education purposes
AI generates automatic feedback in Grammarly
More uses of Grammarly in education
Introduction to Google Scholar
Features of Google Scholar
Using Google Scholar for research
Citation analysis and finding conference papers
Metrics and ranking
Interdisciplinary analysis
Introduction to Evernote
Features of Evernote
Utilizing Evernote as a learning aid
Getting started with Evernote
Additional features
Introduction to Coursera
Features of Coursera
Using Coursera to educate students
Using Coursera to educate professionals
Outcome
Introduction to Quizlet
Features of Quizlet
Uses of Quizlet for education
Uses of various modes
Getting started with Quizlet
Outcome
Introduction to Wolfram Alpha
Features of Wolfram Alpha
Use of Wolfram Alpha for students
Use of Wolfram Alpha for teachers
The attractive interface of Wolfram Alpha
Outcome
Introduction to some creative AI tools
ChatGPT
DALL-E 2
Playgroundai.com
Visily.ai
Practical Application of AI Tools
Virtual assistance
Online shopping
Navigation and maps
Language translation
Home device assistance
Various devices that make use of AI
Security and surveillance
Gaming
Facial recognition
Recap and outcome
Summary

Objectives
What is Machine Learning?
Types of Machine Learning
Unsupervised Machine Learning
Semi-Supervised Learning
Reinforcement Learning
Transfer Learning
Practical Applications
Speech Recognition
Cybersecurity
Social Media Personalization
Feature Engineering
Machine Learning Algorithms
Linear Regression
Ethical Considerations in Machine Learning
Future Trends in Machine Learning
Summary

Objectives
Introduction to Data and Data Science
Understanding data and datasets
Different types of data
Real-life examples of data sets
Good data is important
AI reveals meaningful data insights
Getting data ready
Data and AI connection
The connection between AI, Data and Machine Learning
Teaching AI with data
The process of working with data and AI
Tricky parts of data-driven AI
Why is artificial intelligence important?
Summary

Objectives Introduction to AI and ML in daily and professional life Key benefits of AI and ML in daily life Usage of AI through smartphones AI-based applications via smartphones Virtual Assistant usage through smartphones AI-based smart home appliances AI and ML in transportation AI usage for entertainment and information AI solutions for security requirements Scope of AI in personal financial planning AI in healthcare monitoring and tracking AI in career and skill development Summary

Objectives
Introduction to AI and ML case studies
Understanding AI and ML in real-world scenarios
Importance of AI and ML Use Cases
Google
Search algorithms
Ad placements
Self-Driving cars
Voice recognition
Microsoft
Office 365 and productivity
Azure: Predictive analysis and anomaly detection
The emergence of Microsoft Co-pilot
YouTube
Video recommendations
Content moderation
Analytics and insights
Uber
Dynamic Pricing and Ride Predictions
Route Optimization and Driver Support
Amazon
Product recommendations
Supply Chain Optimization
Fraud Detection and Customer Security
Apple
Siri: Natural Language Processing
Face ID: Biometric Authentication
Personalized Experiences and Product Suggestions
Netflix
Content recommendation algorithms
Optimizing streaming quality
AI-enhanced content creation
Facebook
News feed customization
Automated content moderation
AI-powered advertising targeting
Paytm
Fraud detection and prevention
Personalized financial recommendations
Customer support chatbots
Zomato
Restaurant recommendations
Food delivery optimization
Menu personalization
Recap and outcome
Summary

Objectives
Overview of modern applications
Major companies adopting AI
Adobe: Generative Fill and other tools
Swiggy and Zomato
Flipkart: AI in eCommerce
Ola: Real-time scenario of traffic
Global tech giants and AI integration
Google: AI in search and services
Microsoft's Co-pilot, and Bing
Integration of AI in WhatsApp
Innovative AI tools and platforms
Recap and outcome
Summary

Objectives
Overview of new age AI careers
Current outlook on AI jobs
AI careers that you can pursue
Various industries hiring AI professionals
What do you need for an AI career?
AI careers FAQs
Summary

Objectives
Discussing the Impact of AI and ML on citizen lives
Role of AI and ML in the Indian Government
Various Government schemes and initiatives in AI and ML
Aadhar Identification System
Smart cities and urban planning
MyGov AI portal
Predictive Policing
E-Courts and case prediction
COVID-19 management
AI Integration in IRCTC
AI in Agriculture
AI in Aviation
Summary

Evaluation Pattern

Evaluation Pattern of KLiC Courses consists of 4 Sections as per below table:

Section No.	Section Name	Total Marks	Minimum Passing Marks
1	Learning Progression	25	10
2	Internal Assessment	25	10
3	Final Online Examination	50	20
	Total	100	40
4	SUPWs (Socially Useful and Productive Work in form of Assignments)	5 Assignments	2 Assignments to be Completed & Uploaded

YCMOU Mark Sheet

Printed Mark Sheet will be issued by YCMOU on successful completion of Section 1, Section 2 and Section 3 and will be delivered to the learner by MKCL.
YCMOU Mark Sheet will be available only for Maharashtra jurisdiction learners.

MKCL's KLiC Certificate

The certificate will be provided to the learner who will satisfy the below criteria:

Learners who have successfully completed above mentioned 3 Sections i.e. Section 1, Section 2 and Section 3
Additionally, learner should have completed Section 4 (i.e. Section 4 will comprise of SUPWs i.e. Socially Useful and Productive Work in form of Assignments)
- Learner has to complete and upload minimum 2 out of 5 Assignments