What will you in How to use Artificial Intelligence – A guide for everyone! Course
- Grasp core AI concepts: machine learning vs. deep learning, supervised vs. unsupervised methods
- Navigate popular AI tools and platforms (e.g., TensorFlow, PyTorch, Google Cloud AI, ChatGPT) at a conceptual level
- Understand the AI workflow: data collection, model training, evaluation, and deployment
- Identify real-world AI use cases across industries healthcare, finance, marketing, and more
- Evaluate ethical considerations, bias mitigation, and responsible AI guidelines
Program Overview
Module 1: Introduction to AI Fundamentals
⏳ 30 minutes
Defining AI, ML, and DL; history and evolution of the field
Overview of AI subdomains and key terminology
Module 2: The AI Development Workflow
⏳ 45 minutes
Data gathering and preprocessing essentials
Training, validation, and testing phases with performance metrics
Module 3: Machine Learning Techniques
⏳ 1 hour
Supervised learning algorithms: linear regression, decision trees, and support vector machines
Unsupervised methods: clustering (k-means) and dimensionality reduction (PCA)
Module 4: Deep Learning & Neural Networks
⏳ 1 hour
Neural network architecture, activation functions, and backpropagation
Introduction to CNNs for image tasks and RNNs for sequence data
Module 5: AI Tools & Platforms Overview
⏳ 45 minutes
High-level demos of TensorFlow/Keras, PyTorch, and popular AutoML services
Using AI APIs (NLP, vision, speech) without code
Module 6: Real-World Applications & Case Studies
⏳ 45 minutes
AI in healthcare diagnostics, fraud detection, recommendation engines, and chatbots
Business impact analysis and ROI considerations
Module 7: Responsible AI & Ethics
⏳ 30 minutes
Bias identification and mitigation strategies
Privacy, transparency, and regulatory frameworks
Module 8: Next Steps & Career Pathways
⏳ 30 minutes
Building an AI portfolio: sample projects and Kaggle challenges
Recommended learning paths: specialization courses, certifications, and communities
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Job Outlook
- AI literacy is critical for roles like AI Product Manager, Data Analyst, and Business Intelligence Specialist
- Equips professionals in non-technical fields to collaborate effectively with data science teams
- Lays groundwork for deeper technical careers: ML Engineer, Data Scientist, and AI Researcher
- Valuable for entrepreneurs integrating AI into startups or existing business processes
Specification: How to use Artificial Intelligence – A guide for everyone!
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