Learn Hub
Open-source, technical curriculum pathways. Built for developers, architects, and marketing engineers. Fully interactive simulators with mathematical blueprints.
AI & ML Track
37 ModulesPath from zero to autonomous LLM agents. Deep dive into neurons, gradient descent optimization, attention layers, word embeddings, and prompts.
MarTech Track
31 ModulesAnalytics, server tagging, custom modeling, database warehouse integrations. Built for marketing engineers driving technical pipelines.
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All Curriculum Modules
What is a Neuron?
Learn the foundational math of a perceptron, including weights, biases, and activation thresholds through an interactive demo.
Neural Networks
Deconstruct feedforward neural networks, backpropagation, and how information flows through layers in a step-by-step visual animation.
How Training Works
Visualize gradient descent, loss functions, and learning rates in real time with an interactive optimization graph.
Word Embeddings
Explore the vector space where words capture meaning. Map Word2Vec intuitions in an interactive 2D spatial coordinate visualization.
Attention Mechanism
Trace Query, Key, and Value interactions that power modern LLMs. Run step-by-step self-attention calculations on live token inputs.
The Transformer
Unpack the multi-head attention and encoder-decoder architecture that revolutionized modern Generative AI systems.
How LLMs Work
Learn what happens during pre-training, instruction fine-tuning, RLHF alignment, and active token generation cycles.
RAG Pipelines
Connect document chunking, semantic vector database retrieval, and model generation into an enterprise-ready pipeline.
Building AI Agents
Design autonomous AI agents utilizing tool execution, conversational memory buffers, planning steps, and orchestration routines.
Prompt Engineering
Master techniques like Chain-of-Thought, few-shot prompting, and structured schemas to generate highly reliable outputs.
Linear Regression
Understand the foundation of predictive algorithms. Fit lines of best fit and optimize slopes with gradient descent.
Logistic Regression
Learn binary classification models by mapping data to class probabilities using the mathematical sigmoid function.
Decision Trees
Walk through recursive binary splitting, calculating node entropy and Gini impurity metrics dynamically.
Random Forest
Construct ensemble classifiers that aggregate decision trees through bootstrap sampling and feature bagging.
K-Means Clustering
Cluster unlabeled data iteratively. Visualize centroid migration and class assignment steps in real-time.
Cosine Similarity
Calculate vector direction similarity to run semantic keyword search, context matching, and document rank algorithms.
Types of AI & AI Agents
A taxonomy of AI systems by capability and functionality, and a deep-dive into the six types of AI agents — from simple reflex to multi-agent systems.
Tokenization in NLP
How LLMs turn text into tokens via BPE, WordPiece, and SentencePiece — and why tokenization drives cost and context limits.
Fine-Tuning LLMs
Full fine-tuning vs. LoRA/QLoRA vs. instruction tuning, and when to fine-tune instead of prompting or RAG.
Evaluating LLMs
Benchmarks like MMLU and HumanEval, LLM-as-judge, perplexity, and hallucination metrics for measuring model quality.
Vector Databases Explained
Pinecone, Weaviate, and pgvector — approximate nearest neighbor search, HNSW indexing, and how vector databases power RAG.
Diffusion Models & Image Generation
Forward and reverse diffusion, denoising, and the intuition behind Stable Diffusion and DALL-E.
Generative Adversarial Networks (GANs)
The generator vs. discriminator adversarial game, mode collapse, and how GANs compare to diffusion models.
Reinforcement Learning Basics
Agent, environment, and reward — policy vs. value methods and the intuition behind Q-learning.
RLHF: Reinforcement Learning from Human Feedback
The reward model and PPO pipeline behind alignment — how ChatGPT and Claude were trained to follow instructions.
Convolutional Neural Networks (CNNs)
Convolution, pooling, and filters — how CNNs turn raw pixels into feature maps for image classification.
RNNs and LSTMs
Sequential processing, vanishing gradients, and LSTM gates — and why transformers eventually replaced them.
Overfitting & Regularization
Why models that ace training data fail in production, and the core fixes: L1/L2 regularization, dropout, and early stopping.
Hyperparameter Tuning
Grid search, random search, and Bayesian optimization — systematic strategies for finding good hyperparameters efficiently.
Support Vector Machines (SVM)
Max-margin classifiers and the kernel trick — and when SVMs still beat neural networks on small, high-dimensional data.
Naive Bayes Classifier
A probabilistic classifier built on a deliberately unrealistic independence assumption — still powering spam filters at scale.
PCA & Dimensionality Reduction
Compressing high-dimensional data into its most informative axes — eigenvectors, variance explained, and the curse of dimensionality.
Model Context Protocol (MCP)
Anthropic's open standard for connecting AI models to tools and data — the client-server architecture behind modern agentic AI.
Why LLMs Hallucinate
The structural causes of confident false outputs, and how RAG, grounding, and calibration reduce them.
Context Windows & Long Context
Tokens, quadratic attention cost, and how million-token context windows work — plus what causes context rot.
Multimodal AI (Vision + Language)
How vision-language models like GPT-4V, Claude, and Gemini tokenize images and reason across modalities.
AI Ethics & Bias in Machine Learning
How bias enters ML pipelines, fairness metrics, real-world cases, and responsible AI practices for engineers.
GA4 Architecture
Deep dive into Google Analytics 4 event-driven models, custom parameters, user scopes, and BigQuery schemas.
GTM Deep Dive
Master Google Tag Manager event listeners, trigger groups, dataLayer variables, and advanced preview debugging.
Server-Side GTM
Learn how to deploy server-side tagging containers, set up custom subdomain routing, and bypass ad-blockers securely.
BigQuery for Marketers
Query raw GA4 dataset exports using SQL, build cross-channel attribution reports, and calculate user lifetime value.
Event Taxonomy Design
Create structured naming guidelines, standardize parameter schemas, and document unified tagging architectures.
Consent Mode v2
Configure Google Consent Mode v2 variables, map tags to CMP signals, and set up fallback modeling thresholds.
Meta CAPI Setup
Integrate Meta Conversions API using Server-Side GTM or API calls, managing deduplication and quality match scores.
Hubspot Architecture
Configure custom contact/deal objects, build lifecycle stage tracking, and program multi-step automation workflows.
CRM Data Modeling
Map contact records to multi-tiered company pipelines, standardizing object properties and data integrity.
n8n Workflows
Build automated pipelines connecting external REST APIs, webhooks, and AI nodes using open-source n8n tools.
Make.com Patterns
Design Make.com automation blueprints utilizing arrays, iterators, routing branches, and API error catch routes.
Technical SEO Fundamentals
Crawlability, indexation, Core Web Vitals, robots.txt, canonical tags, and site architecture — the engineering layer that makes pages eligible to rank.
Keyword Research Framework
Search intent types, keyword difficulty, long-tail strategy, and clustering keywords by topic to avoid cannibalization.
Content Marketing Strategy
Topic clusters, pillar pages, funnel-stage content mapping, content calendars, and distribution planning.
Marketing Attribution Models
First-touch, last-touch, linear, time-decay, and data-driven attribution — and why GA4 and ad platforms report different numbers.
Email Marketing Automation
Drip campaigns vs. behavioral triggers, segmentation, and SPF/DKIM/DMARC deliverability fundamentals.
Lead Scoring Models
Explicit vs. implicit scoring, MQL/SQL thresholds, and how score feeds lifecycle-stage automation.
Google Ads Campaign Structure
Account hierarchy, keyword match types, Smart Bidding strategies, and Quality Score mechanics.
Programmatic SEO
Templated page generation at scale, data-driven landing pages, thin content risk, and real pSEO case studies.
Conversion Rate Optimization (CRO)
The CRO research-to-test process, heatmaps and session recordings, hypothesis-driven testing, and common friction points.
Marketing Mix Modeling (MMM)
Statistical, privacy-safe channel effectiveness measurement — adstock and saturation curves, and MMM vs. multi-touch attribution.
Customer Data Platforms (CDP)
What a CDP is, how it differs from a CRM and DMP, and how identity resolution unifies customer data across systems.
UTM Parameters & Campaign Tracking
How UTM parameters work, naming convention governance, and how they populate GA4 traffic-source data.
A/B Testing & Statistical Significance
Null hypothesis, p-values, statistical power, sample size calculation, and the peeking and multiple-comparisons pitfalls.
MarTech Stack Architecture
How to architect a MarTech stack as a system of data-flow layers, plus build vs. buy decisions.
Zapier Automation Patterns
Triggers, actions, multi-step zaps, filters and paths, and Zapier vs. Make vs. n8n tradeoffs.
Looker Studio Dashboards
Blending data sources, calculated fields, connecting BigQuery, and dashboard design principles for stakeholder reporting.
Schema Markup & Structured Data
JSON-LD, key schema.org types, and how structured data earns rich snippets in search results.
Facebook Ads Pixel & Tracking
Pixel base code and standard events, how the Conversions API works alongside the Pixel, and iOS 14.5's impact on tracking.
Marketing Analytics & KPIs
CAC, LTV, LTV:CAC ratio, payback period, and marketing efficiency ratio (MER) — vanity metrics vs. actionable metrics.
API Integrations for MarTech
REST API basics for marketers, OAuth2 and API key authentication, webhooks vs. polling, and rate limits explained.