52 Weeks of Complete Artificial Intelligence
Week 1: Introduction to Artificial IntelligenceDay 1: What is AI? History and evolutionDay 2: Types of AI (Narrow, General, Superintelligence)Day 3: AI vs ML vs Deep LearningDay 4: Real-world AI applications across industriesDay 5: AI lifecycle overviewDay 6: Tools and ecosystem overviewDay 7: Hands-on: Setup Python + AI dev environment---Week 2: Python for AIDay 1: Python basics for AIDay 2: Data structures and functionsDay 3: NumPy fundamentalsDay 4: Pandas for data manipulationDay 5: Data visualization with MatplotlibDay 6: Working with APIs in PythonDay 7: Hands-on: Build a mini data pipeline---Week 3: Mathematics for AI (Foundations)Day 1: Linear algebra basics (vectors, matrices)Day 2: Matrix operationsDay 3: Probability basicsDay 4: Distributions and statisticsDay 5: Calculus intuitionDay 6: Gradients and optimization intuitionDay 7: Hands-on: Implement math concepts in Python---Week 4: Data Handling & PreprocessingDay 1: Data collection strategiesDay 2: Data cleaning techniquesDay 3: Handling missing valuesDay 4: Feature engineering basicsDay 5: Data transformationDay 6: Data pipelinesDay 7: Hands-on: Clean and prepare dataset---Week 5: Machine Learning FundamentalsDay 1: What is Machine LearningDay 2: Supervised vs Unsupervised learningDay 3: Training vs testingDay 4: Overfitting vs underfittingDay 5: Model evaluation metricsDay 6: Cross-validationDay 7: Hands-on: Train first ML model---Week 6: Supervised Learning (Regression)Day 1: Linear regressionDay 2: Polynomial regressionDay 3: Regularization (L1/L2)Day 4: Feature scalingDay 5: Gradient descentDay 6: Model tuningDay 7: Hands-on: Build regression project---Week 7: Supervised Learning (Classification)Day 1: Logistic regressionDay 2: Decision treesDay 3: Random forestsDay 4: Support vector machinesDay 5: Evaluation (precision/recall/F1)Day 6: ROC curvesDay 7: Hands-on: Classification system---Week 8: Unsupervised LearningDay 1: Clustering basicsDay 2: K-means clusteringDay 3: Hierarchical clusteringDay 4: Dimensionality reductionDay 5: PCADay 6: Anomaly detectionDay 7: Hands-on: Customer segmentation---Week 9: Model Deployment BasicsDay 1: Model serializationDay 2: Building APIs with FastAPIDay 3: Serving modelsDay 4: Docker basicsDay 5: Cloud deployment introDay 6: Monitoring basicsDay 7: Hands-on: Deploy ML model---Week 10: Deep Learning FoundationsDay 1: Neural networks basicsDay 2: Perceptrons and layersDay 3: Activation functionsDay 4: BackpropagationDay 5: Loss functionsDay 6: OptimizersDay 7: Hands-on: Build NN from scratch---Week 11: TensorFlow & PyTorchDay 1: TensorFlow basicsDay 2: PyTorch basicsDay 3: Model buildingDay 4: Training loopsDay 5: Dataset handlingDay 6: Model evaluationDay 7: Hands-on: Train DL model---Week 12: Computer VisionDay 1: Image processing basicsDay 2: CNN architectureDay 3: Image classificationDay 4: Object detectionDay 5: Transfer learningDay 6: OpenCV basicsDay 7: Hands-on: Image classifier---Week 13: Natural Language Processing (NLP)Day 1: Text preprocessingDay 2: TokenizationDay 3: Embeddings (Word2Vec, GloVe)Day 4: Sequence modelsDay 5: Sentiment analysisDay 6: Text classificationDay 7: Hands-on: NLP pipeline---Week 14: Transformers & LLMsDay 1: Attention mechanismDay 2: Transformer architectureDay 3: BERT & GPT overviewDay 4: Tokenization strategiesDay 5: Prompt engineering basicsDay 6: Fine-tuning basicsDay 7: Hands-on: Build LLM app---Week 15: Prompt Engineering MasteryDay 1: Prompt patternsDay 2: Few-shot promptingDay 3: Chain-of-thoughtDay 4: Role promptingDay 5: Guardrails in promptsDay 6: Evaluation promptsDay 7: Hands-on: Prompt system---Week 16: Retrieval-Augmented Generation (RAG)Day 1: RAG architectureDay 2: Embeddings and vector DBsDay 3: FAISS/ChromaDay 4: Document chunkingDay 5: Retrieval optimizationDay 6: Context injectionDay 7: Hands-on: Build RAG system---Week 17: AI Agents (Foundations)Day 1: What are AI agentsDay 2: Agent architecturesDay 3: Tools and actionsDay 4: Memory systemsDay 5: Planning strategiesDay 6: ReAct patternDay 7: Hands-on: Build first agent---Week 18: Multi-Agent SystemsDay 1: Multi-agent basicsDay 2: Manager-worker patternDay 3: Planner-executor systemsDay 4: Debate/consensus agentsDay 5: LangGraphDay 6: CrewAI / AutoGenDay 7: Hands-on: Multi-agent system---Week 19: AI Memory SystemsDay 1: Short-term memoryDay 2: Long-term memoryDay 3: Vector memoryDay 4: Knowledge graphsDay 5: Memory retrieval strategiesDay 6: Context windowsDay 7: Hands-on: Memory-enabled agent---Week 20: AI Tools & IntegrationsDay 1: API integrationsDay 2: Tool callingDay 3: Web scrapingDay 4: Database integrationsDay 5: External systems (CRM, etc.)Day 6: Automation workflowsDay 7: Hands-on: Tool-using agent---Week 21: AI Evaluation & DebuggingDay 1: Evaluation frameworksDay 2: LLM-as-a-judgeDay 3: Test casesDay 4: Debugging outputsDay 5: Prompt testingDay 6: Metrics trackingDay 7: Hands-on: Evaluation pipeline---Week 22: AI Guardrails & SafetyDay 1: Prompt injection risksDay 2: Output validationDay 3: Safety filtersDay 4: Policy enforcementDay 5: Human-in-the-loopDay 6: Risk mitigationDay 7: Hands-on: Guardrails system---Week 23: AI Observability & MonitoringDay 1: Logging systemsDay 2: TracingDay 3: Cost monitoringDay 4: Latency trackingDay 5: Error handlingDay 6: DashboardsDay 7: Hands-on: Monitoring setup---Week 24: AI System DesignDay 1: End-to-end architectureDay 2: Scalability patternsDay 3: Distributed systemsDay 4: MicroservicesDay 5: Reliability engineeringDay 6: Performance optimizationDay 7: Hands-on: Design AI system---Week 25: AI in Production (MLOps)Day 1: CI/CD for AIDay 2: Model versioningDay 3: Data versioningDay 4: Experiment trackingDay 5: PipelinesDay 6: Deployment strategiesDay 7: Hands-on: MLOps pipeline---Week 26: Capstone Project (End-to-End AI System)Day 1: Project planningDay 2: Architecture designDay 3: Data + model developmentDay 4: Agent + tools integrationDay 5: Guardrails + evaluationDay 6: Deployment + monitoringDay 7: Final demo + portfolio presentation---Week 27: Advanced RAG SystemsDay 1: Hybrid search (vector + keyword)Day 2: Reranking modelsDay 3: Query rewritingDay 4: Multi-hop retrievalDay 5: Context compressionDay 6: Retrieval evaluationDay 7: Hands-on: Advanced RAG pipeline---Week 28: Agent Orchestration at ScaleDay 1: Workflow orchestration patternsDay 2: DAG-based executionDay 3: State managementDay 4: Long-running agentsDay 5: Event-driven systemsDay 6: Failure handlingDay 7: Hands-on: Orchestrated agent system---Week 29: Autonomous AI SystemsDay 1: Goal-driven agentsDay 2: Planning + execution loopsDay 3: Self-improving agentsDay 4: Reflection mechanismsDay 5: Iterative refinementDay 6: Safety in autonomyDay 7: Hands-on: Autonomous agent---Week 30: AI System OptimizationDay 1: Latency optimizationDay 2: Cost optimizationDay 3: Token efficiencyDay 4: Caching strategiesDay 5: ParallelizationDay 6: Load balancingDay 7: Hands-on: Optimize AI pipeline---Week 31: Multimodal AI SystemsDay 1: Vision + text modelsDay 2: Speech + text systemsDay 3: Image generation modelsDay 4: Video understanding basicsDay 5: Multimodal embeddingsDay 6: Cross-modal retrievalDay 7: Hands-on: Multimodal app---Week 32: Speech AI & Voice SystemsDay 1: Speech-to-textDay 2: Text-to-speechDay 3: Voice assistantsDay 4: Real-time audio processingDay 5: Conversational pipelinesDay 6: Latency handlingDay 7: Hands-on: Voice AI system---Week 33: AI for Software EngineeringDay 1: AI code generationDay 2: AI-assisted debuggingDay 3: Test generationDay 4: Code review automationDay 5: Repo-level reasoningDay 6: Secure code generationDay 7: Hands-on: AI dev assistant---Week 34: AI for Data EngineeringDay 1: AI-driven ETLDay 2: Data pipeline automationDay 3: Schema understandingDay 4: Data quality checksDay 5: Data enrichmentDay 6: Streaming pipelinesDay 7: Hands-on: AI data pipeline---Week 35: AI for Product ManagementDay 1: AI product lifecycleDay 2: Use case identificationDay 3: AI UX designDay 4: Metrics and KPIsDay 5: ExperimentationDay 6: RoadmappingDay 7: Hands-on: AI product spec---Week 36: AI for Business StrategyDay 1: AI transformation strategyDay 2: Value identificationDay 3: ROI modelingDay 4: Competitive advantageDay 5: Scaling AI initiativesDay 6: Org design for AIDay 7: Hands-on: AI strategy doc---Week 37: AI Governance (Foundations)Day 1: What is AI governanceDay 2: Risk frameworksDay 3: Bias and fairnessDay 4: TransparencyDay 5: AccountabilityDay 6: Governance lifecycleDay 7: Hands-on: Governance plan---Week 38: AI Compliance & RegulationsDay 1: Global AI regulationsDay 2: EU AI ActDay 3: NIST AI RMFDay 4: ISO standardsDay 5: Data privacy (GDPR etc.)Day 6: Audit readinessDay 7: Hands-on: Compliance mapping---Week 39: Responsible AI SystemsDay 1: Ethical AI principlesDay 2: Fairness metricsDay 3: ExplainabilityDay 4: Model transparencyDay 5: Trust systemsDay 6: Incident responseDay 7: Hands-on: Responsible AI design---Week 40: AI SecurityDay 1: Prompt injection attacksDay 2: Data poisoningDay 3: Model attacksDay 4: Secure pipelinesDay 5: Access controlDay 6: Threat modelingDay 7: Hands-on: Secure AI system---Week 41: AI InfrastructureDay 1: Cloud AI architectureDay 2: GPU/TPU basicsDay 3: Distributed trainingDay 4: Storage systemsDay 5: NetworkingDay 6: Cost managementDay 7: Hands-on: Cloud AI setup---Week 42: Scaling AI SystemsDay 1: Horizontal scalingDay 2: Vertical scalingDay 3: AutoscalingDay 4: Load testingDay 5: High availabilityDay 6: Fault toleranceDay 7: Hands-on: Scale system---Week 43: AI SaaS & MonetizationDay 1: AI business modelsDay 2: Pricing strategiesDay 3: Subscription modelsDay 4: APIs as productsDay 5: GTM strategyDay 6: Growth loopsDay 7: Hands-on: AI SaaS idea---Week 44: Building AI StartupsDay 1: Idea validationDay 2: MVP buildingDay 3: Product-market fitDay 4: Fundraising basicsDay 5: Scaling startupsDay 6: Hiring strategyDay 7: Hands-on: Startup pitch---Week 45: AI for EnterprisesDay 1: Enterprise AI architectureDay 2: Integration with legacy systemsDay 3: Change managementDay 4: Stakeholder alignmentDay 5: Governance at scaleDay 6: Enterprise toolsDay 7: Hands-on: Enterprise AI design---Week 46: AI for Cloud & DevOpsDay 1: AI in CI/CDDay 2: Infrastructure automationDay 3: Monitoring automationDay 4: DevOps agentsDay 5: Incident managementDay 6: Cloud-native AIDay 7: Hands-on: DevOps AI system---Week 47: AI + RoboticsDay 1: Robotics fundamentalsDay 2: Perception systemsDay 3: Motion planningDay 4: Reinforcement learningDay 5: Simulation environmentsDay 6: Real-world deploymentDay 7: Hands-on: Robotics simulation---Week 48: Reinforcement LearningDay 1: RL basicsDay 2: Markov decision processesDay 3: Q-learningDay 4: Policy gradientsDay 5: Deep RLDay 6: Exploration vs exploitationDay 7: Hands-on: RL agent---Week 49: Frontier AI ModelsDay 1: Foundation modelsDay 2: Scaling lawsDay 3: Training pipelinesDay 4: Fine-tuning strategiesDay 5: Alignment techniquesDay 6: Evaluation challengesDay 7: Hands-on: Model experimentation---Week 50: Self-Evolving AI SystemsDay 1: Continuous learningDay 2: Feedback loopsDay 3: Model retrainingDay 4: Skill-based agentsDay 5: Memory evolutionDay 6: Autonomous improvementDay 7: Hands-on: Self-improving agent---Week 51: AI Leadership & StrategyDay 1: AI leadership skillsDay 2: Building AI teamsDay 3: Decision-making with AIDay 4: AI transformation roadmapDay 5: Stakeholder communicationDay 6: AI cultureDay 7: Hands-on: Leadership strategy---Week 52: Final Capstone — Production AI SystemDay 1: Problem selectionDay 2: Architecture designDay 3: Full system buildDay 4: Agents + RAG + toolsDay 5: Guardrails + evaluationDay 6: Deployment + scalingDay 7: Final demo + portfolio + presentation---
Get it → schoolofail.gumroad.com