AI Foundations for Decision Makers: From Zero to LLMs
- Description
- Curriculum
- FAQ
- Reviews
You can find the eBook for this course attached to Lecture 5.
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The launch of ChatGPT in November 2022 signaled a paradigm shift, underscoring that Large Language Models (LLMs) will shape the technological, social, economic, and political landscape for decades. Yet, the sheer volume of courses and content on AI can overwhelm decision-makers, especially those without a technical background. Even professionals familiar with machine learning must adopt a new approach to understanding LLMs and AI-driven automation.
Unlike typical AI courses that focus on using or building LLMs, this course is designed for business professionals, startup founders, and investors seeking a strategic understanding of AI’s transformative potential. Instead of just learning how to use AI tools, participants will gain a deep yet accessible framework for evaluating AI’s long-term impact and formulating an AI-first strategy for sustainable competitive advantage.
Who Should Enroll?
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Product managers, analysts, and entrepreneurs seeking a practical decision-making approach to AI.
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Executives and business leaders aiming to bridge the gap between AI technology and business strategy.
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Non-technical professionals who need to hire AI talent, assess AI-driven products, or collaborate with technical teams.
What You Will Learn:
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AI and Machine Learning Fundamentals – Key principles of machine learning, model selection, and trade-offs without complex math.
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Understanding Large Language Models (LLMs) – How LLMs work, their mechanics, capabilities, and real-world applications.
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AI Agents and the AI-First Organization – How AI agents transform business models, decision-making, and strategy.
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Evaluating and Choosing an LLM – Open-source vs. proprietary models, performance benchmarks, cost considerations, and compliance requirements.
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The Agent Economy – Business models, monetization strategies, and the rise of autonomous AI-powered workflows.
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Challenges in AI Deployment – Hallucinations, memorization, bias, context loss, and computational costs.
What Sets This Course Apart?
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Designed for decision-makers, not just developers.
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Provides a deeper focus on AI models, LLM training, and AI agents compared to standard business-oriented AI courses.
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No coding required, but offers enough technical depth to help participants engage meaningfully with engineering teams.
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Includes relevant research papers and strategic frameworks for assessing AI’s long-term impact.
By the end of this course, participants will have the technical foundation and strategic insights needed to navigate AI-driven transformations, collaborate with engineering teams, and make informed, future-proof business decisions. This is not just a course on AI tools—it’s a deep dive into AI’s mechanics, potential, and strategic implications.
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What is AI? -
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Narrow AI vs Broad AI
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How does ML differ from traditional software?
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Types of Machine Learning
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Features | Data as the foundation: Features, Labels, and Datasets
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Labels | Data as the foundation: Features, Labels, and Datasets.
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Datasets | Data as the foundation: Features, Labels, and Datasets.
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Training vs. Inference: How do AI models learn and make predictions?
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Common challenges: Overfitting
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Common challenges: Bias
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Common challenges: Generalization
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Key AI/ML Topics: Feature Engineering
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Key AI/ML Topics: Key Data Sources
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Key AI/ML Topics: Model Selection: Different types of ML Models
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Key AI/ML Topics: Model Selection: How to select a suitable model?
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Key AI/ML Topics: Model Evaluation: Validation and Cross-Validation
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Key AI/ML Topics: Evaluating Model Performance
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Key AI/ML Topics: Hyperparameters
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Key AI/ML Topics: Model Regularization
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Deep Learning and Artificial Neural Networks: What are Artificial Neural Network
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Forward Propagation, Backpropagation & Gradient Descent...
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Activation Functions
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Optimization Algorithms
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Intro to NLP: Text Processing in NLP
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Applications of NLP
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Core NLP Tasks
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NLP Approaches
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Traditional Language Models
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Challenges in NLP
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Perplexity
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Encoding-Decoding Architecture
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Review
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What is Attention?
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Understanding Transformers: The shift from RNNs & CNNs to Transformers
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"Attention Is All You Need": How self-attention enables LLMs
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Self-Attention and Cross-Attention
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Scaling Laws
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How LLMs learn and adapt
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Types of Pre-training
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Self-supervised Learning
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LLM Model Architectures
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Model Size and Capabilities
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Fine-tuning Fundamentals
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Parameter-Efficient Fine-Tuning (PEFT)
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Post-Training Fundamentals
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Ways to interact with LLMs
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Zero-shot Prompting
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Chain-of-Thought (CoT) Reasoning
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Zero-shot Chain-of-Thought (CoT)
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Few-shot Prompting
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Few-shot Prompting CoT
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Demonstrate-Search-Predict
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Interleaved Retrieval guided by Chain-of-Thought (IRCoT)
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Self-Consistency and Tree of Thoughts (ToT)
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Retrieval-Augmented Generation (RAG)
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AI Agents & Autonomous Reasoning
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Open-Source vs. Proprietary LLMs
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GPT Series (OpenAI)
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BERT & RoBERTa (Google & Meta)
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T5 & UL2 (Google)
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Claude (Anthropic)
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LLaMA (Meta)
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Mistral & Mixtral
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Gemini (Google DeepMind)
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DeepSeek r1
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Grok xAI
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Command R+ (Cohere) & Other Enterprise LLMs
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Key Selection Factors for LLMs
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LLM Performance Benchmarks
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LLM Leaderboards
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LLM Deployment Strategies
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Technical Challenges in LLMs
