Course Title Artificial Intelligence Enroll Now! Register here Level Duration Certificate All Levels 4 Days Bharat Academy Education Certified – KHDA – AIAL – Royal Institute for Chartered Engineer – RICE (USA) Course Info Key Highlights Target Audience Course Overview: This comprehensive course covers foundational AI knowledge and practical applications through hands-on learning. It spans four days and introduces core AI concepts, machine learning, data science workflows, and deep learning. Students will work on real-world AI projects, explore AI implementation strategies, and learn how AI is transforming industries such as security and transportation. The course also addresses AI’s societal impact, ethical challenges, and how to effectively integrate AI into businesses for strategic growth and innovation. Lesson Plan: Day 1 – Foundation of Artificial Intelligence 1.1  AI Basics and Core Concepts History of AI and how it has evolved Overview of AI applications and fields Key AI terminologies: algorithms, models, training, testing, data, and tasks 1.2 Introduction to Machine Learning Introduction to machine learning Types of machine learning: supervised, unsupervised, and reinforcement learning How models learn from data: training, validation, and testing processes 1.3 Understanding Data in AI Data types and formats(structured, unstructured) The importance its impact on AI performance Data preprocessing: cleaning of data quality, normalization, and feature engineering 1.4 Simplified Deep Learning for Non-Experts Introduction to deep learning and neural networks Layers of neural networks: input, hidden, and output layers The different between machine learning and deep learning Lesson Plan: Day 2 – Crafting AI Solutions 2.1 Machine Learning Project Lifecycle Introduction of the project lifecycle: problem definition, data collection, model development, evaluation, and development Roles and responsibilities in an AI project team 2.2 Data Science Workflow and Project Strategy The data science workflow: data collection, analysis, model building, and visualization Strategy for selecting AI projects: aligning business needs with technical feasibility 2.3 Collaborating Effectively with AI Teams Roles in an AI team: data scientists, engineers, business analysts Best practices for collaboration between technical and non-technical teams 2.4 Technical Tools for AI Development Overview of AI development tools: Python, TensorFlow, Keras, Scikit-learn Tools for data visualization and exploration: Tableau, Matplotlib, Seaborn Lesson Plan: Day 3 – Integrating AI into Business 3.1 AI Case Studies – Security and Transportation Introduction to AI use cases in business Case Study 1: Security Overview of Security and Crowd Management solutions AI’s role in Perimeter Security and Intrusion Detection Case Study 2: Transportation Overview of 3D Computer Vision in Transportation AI’s role in transportation and smart mobility 3.2 AI Implementation Strategy Steps for AI integration: Identify business goals, pilot programs, data collection, infrastructure setup, and scaling AI Implementation Framework: Planning and setting expectations Defining success metrics for AI projects Aligning AI projects with business objectives 3.3 AI Implementation Strategy Assessing feasibility: Technology, data readiness, and organizational culture Measuring business impact: ROI(Return On Investment) and non-financial benefits Case Study: How a company successfully scaled AI (e.g., Google’s AI-driven advertising model or Netflix’s recommendation system) 3.4 Overcoming AI Adoption Challenges and Pitfalls Challenges in AI Adoption Lack of high-quality data Skills gap in AI talent Resistance to change within organizations Ethical concerns and biases in AI Overcoming Pitfalls Training and upskilling employees Starting with small-scale pilot projects Ensuring transparency and addressing ethical concerns Building a cross-functional AI team (technical and business roles) 4.1 Initiating AI Adoption in Your Company Steps to Initiate AI Adoption Building cross-functional AI teams(data scientists, engineers, business analysts) Setting up pilot projects Continuous evaluation and iteration Roles and Responsibilities Defining clear roles for AI teams, including collaboration with non-technical departments(marketing, sales, HR) Case Study: How a company successfully initiated AI adoption(e.g., IBM’s Watson AI in healthcare or Airbnb’s AI-driven personalization) Lesson Plan: Day 4 – AI’s Societal Impact 5.1 Introduction to AI’s Societal Impact Overview of AI’s impact on various sectors(healthcare, education, law enforcement, and employment) Introduction to the economic disruption caused by AI Ethical challenges: bias, accountability, and transparency 5.2 AI Bias and Discrimination Explore the causes and consequences of bias in AI systems Case studies of AI systems that have demonstrated bias (e.g., facial recognition, hiring algorithms) Ethical concerns: fairness, inclusivity, and transparency 5.3 AI’s Impact on Employment and Emerging Markets Discuss the economic impacts of AI on employment(job creation vs. job displacement) Reskilling and upskilling for AI-driven roles Predict how AI will impact the workforce in the coming decade 5.4 Adversarial Threats to AI Systems and Ethical Framework Introduction to adversarial threat to AI system(data poisoning, adversarial examples) Real-world examples of AI systems compromised by adversarial attacks Security solutions and best practices to mitigate threats 5.5 AI’s Societal Impact – Final Reflections and Review Recap of key topics from the week (AI’s societal impact, bias, employment, emerging markets, adversarial threats, ethical concerns) Review of important assignments and discussion points   Beginners Fresh 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