The Ultimate Beginner's Guide to AI and Machine Learning
Plus: (1) AI and Humans, (2) Generative AI and Leaders, (3) AI and Operations, (4) AI and Business Strategy
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2025 Updated with Recent Strategies.
Artificial Intelligence uses data-driven methods to simulate human-like problem-solving, with Machine Learning and Deep Learning as core techniques. It powers predictions, automation, and decision-making across business, healthcare, marketing, and operations. AI enhances collaboration, efficiency, and innovation, but requires ethical governance, quality data, and human oversight to ensure responsible, impactful, and sustainable use.
What you'll learn
AI mimics human decision-making, ML learns from data, and Deep Learning solves complex problems using neural networks.
Supervised uses labeled data, unsupervised discovers patterns, and reinforcement learns through actions and rewards.
AI processes faster but lacks emotions, consciousness, creativity, real understanding, and common sense.
High-quality, diverse, and clean data improves AI accuracy, predictions, decision-making, and real-world performance.
AI transforms healthcare, marketing, HR, finance, automation, supply chain, personalization, and customer experiences.
Responsible AI needs transparency, fairness, accountability, bias control, and continuous human oversight.
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Over 20 hours of structured video lessons covering AI fundamentals, machine learning, deep learning, real-world applications, and practical demos.
Get access to an active AI learning community for discussions, doubt-solving, networking, project support, and continuous learning opportunities.
Work on real-world AI projects to apply concepts, build problem-solving skills, gain practical experience, and strengthen your professional portfolio.
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Syllabus
Chapter 1
Understand AI, Machine Learning, Deep Learning, their differences, key milestones, and how AI compares with human intelligence.
Chapter 2
Learn supervised, unsupervised, and reinforcement learning, including data, features, labels, algorithms, models, encoding, and decoding.
Chapter 3
Explore neural networks, function approximators, CNN, RNN, GAN, their structure, working, and real-world problem-solving applications.
Chapter 4
Study AI use in healthcare, marketing, finance, HR, automation, decision-making, predictive analytics, and digital transformation benefits.
Chapter 5
Discuss AI ethics, governance, human-AI collaboration, trust-building, job impacts, innovation strategies, and future AI trends.
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Your instructor
Amit Kurmi is a skilled instructor with deep expertise in Artificial Intelligence, Machine Learning, and Business Applications of AI. He simplifies complex concepts through practical examples, real-world use cases, and engaging teaching methods. His guidance helps learners build strong foundations, develop future-ready skills, and confidently apply AI in professional and organizational environments.
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What students are saying
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FAQs
AI is the simulation of human-like thinking and decision-making using data, algorithms, and computer systems.
Basic coding helps, but many AI tools are no-code or low-code, making it beginner-friendly.
AI improves decision-making, automation, customer experience, marketing, forecasting, and reduces operational costs.
AI replaces repetitive tasks, but also creates new roles requiring creativity, strategy, and human-AI collaboration
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