Track
New Collar
Duration
60 hours
Skill Level
Foundation
Language
English
About this Course
The KLiC AI-ML course by offers a thorough exploration of Artificial Intelligence (AI) and Machine Learning (ML). Designed for individuals of all …
Learning Mode: Learn at ALC or at Home
Detailed Course Curriculum
Hands-on module breakdown aligned with MKCL production standards and industry requirements.
- 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 case studies
- Understanding AI and ML in real-world scenarios
- Importance of AI and ML Use Cases
- 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
- 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
Eligibility Criteria
• Basic knowledge of computers and keen desire to build skills in this field.
• Open to students, job seekers, and working professionals.
• Open to students, job seekers, and working professionals.
Official Certification
• Official MKCL KLiC Certificate upon successful completion of the course and evaluations.
Target Audience & 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.
• 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.
Work-Centric Learning Approach
• 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!
• 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!
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