Chapters 1-6: The Story So Far
A consolidation of core concepts before diving into deep learning. This foundation unlocks understanding of modern AI systems.
Essential understanding from the first six chapters
AI learns by finding optimal weights (importance) and bias (baseline) through measuring and minimizing error. Everything else builds on this foundation.
Metrics like accuracy can mislead. Proper evaluation requires precision, recall, confusion matrices, and train/test splits to assess true model performance.
Modern AI outputs probabilities, not certainties. From Bayes' theorem to LLM reasoning, probability is the mechanism for handling uncertainty.
Vectors and matrices enable AI to process millions of data points simultaneously. Matrix multiplication forms the computational heart of neural networks.
Clear distinctions exist between Rule-Based, Statistical ML, Deep Learning, and Reinforcement Learning—each with appropriate use cases.
Practical skills gained from this foundation
When vendors cite "95% accuracy," the right questions emerge: class imbalance status, test set performance, precision vs. recall trade-offs, and business impact alignment.
The ability to describe how neural networks function: data flows through layers as matrices, weights adjust through gradient descent, and models minimize loss functions.
Understanding when to apply regression (predict numbers), classification (predict categories), and why different loss functions matter for each approach.
Large gaps between training and test performance signal memorization rather than learning. Recognition of this pattern enables discussions about regularization and validation strategies.
Vector similarity (dot products, cosine similarity) powers recommendation engines, semantic search, and content matching. The mathematical foundation is now clear.
Informed choices between rule-based systems, traditional ML, and deep learning based on understanding their fundamental differences and appropriate applications.
How each concept builds toward understanding modern AI
prediction = w₁×feature₁ + w₂×feature₂ + bias The Foundation: How machines learn patterns by adjusting weights (importance of each feature) and bias (baseline). House price prediction demonstrated how models find optimal values through iterative refinement.
MSE = average((prediction - actual)²) Measuring Quality: Different problems require different error measures. MSE for regression (predicting numbers), Cross-Entropy for classification (predicting categories). The loss function defines optimization targets.
P(A|B) = P(B|A) × P(A) / P(B) Handling Uncertainty: From the Monty Hall problem to chain-of-thought reasoning in GPT-4, probability powers AI decision-making. Modern LLMs are probability machines—predicting tokens based on conditional probabilities.
Decision Boundary: Separating Renew from Churn From Numbers to Categories: Customer churn prediction demonstrated how classification draws decision boundaries to separate data into groups. More importantly, accuracy alone can dangerously mislead.
cosine_similarity = (A·B) / (||A|| × ||B||) Beyond Points—Direction and Magnitude: Vectors represent both size and direction. Dot products measure similarity, while cosine similarity handles comparisons across different scales.
Y = X × W + b Processing at Scale: Instead of single predictions, matrices enable processing thousands simultaneously. This batch processing forms the computational backbone of neural networks—one matrix multiplication handles entire datasets.
Understanding the broader landscape enables choosing the right tool for each problem
No learning—follows pre-programmed rules. Like traditional IVR menus: "Press 1 for sales, 2 for support."
Learns patterns—requires manual feature engineering. "Given customer age, tenure, support tickets → predict churn."
Learns features automatically—stacks many layers to extract hierarchies of patterns. Powers LLMs, image recognition, speech-to-text.
Learns through trial and error—receives rewards/penalties. Optimizes long-term strategy through experience.
Modern contact centers combine all four: rule-based call routing → statistical ML for sentiment analysis → deep learning for speech-to-text → reinforcement learning for agent scheduling. The best solutions use the right type for each sub-problem.
Scenario-based questions to test understanding
An AI vendor demonstrates their churn prediction model: "We achieve 94% accuracy on your data!" The dataset contains 90% renewal rate, 10% churn rate.
What's the appropriate response?
A call routing system needs implementation based on detected keywords ("billing," "technical," "sales") with 10 well-defined categories.
Which AI type is most appropriate?
A data science team reports: "Training accuracy: 99.2%, Test accuracy: 67.3%"
What does this indicate?