ACADEMY / COURSES / COURSES / ML-ADVANCED12 LESSONS · 12H 0M
COURSE 05 / 08
Machine LearningAdvancedAdvanced ML: Unsupervised Learning & Production
Master advanced machine learning techniques including unsupervised learning, neural networks fundamentals, and production deployment. Build production-ready ML systems with modern MLOps practices.
COURSE DATASHEETv2.1
Lessons12
Total runtime12h 0m
DifficultyAdvanced
CategoryMachine Learning
TierFree
Last updated2025-10-15
LocaleEN · RU · ZH
§A · OBJECTIVES
What you’ll be able to do.
01.Master unsupervised learning: clustering, dimensionality reduction, anomaly detection
02.Understand neural network fundamentals and deep learning basics
03.Learn time series analysis and reinforcement learning intro
04.Deploy ML models to production with proper infrastructure
05.Implement MLOps best practices: monitoring, versioning, pipelines
06.Optimize models for production: quantization, pruning, distillation
07.Handle data drift, A/B testing, and model debugging
08.Build scalable ML serving infrastructure
§B · SYLLABUS
All lessons. Read in order, or jump.
№LESSONKINDTIME
01Clustering Algorithms: K-Means, DBSCAN, HierarchicalCONCEPT· 60 min60 min→02Dimensionality Reduction: PCA, t-SNE, UMAPCONCEPT· 60 min60 min→03Anomaly Detection: Isolation Forest & One-Class SVMCONCEPT· 60 min60 min→04Gaussian Mixture Models & EM AlgorithmCONCEPT· 60 min60 min→05Neural Networks Fundamentals: Perceptrons to BackpropagationCONCEPT· 60 min60 min→06Deep Learning Basics: Architectures & TrainingCONCEPT· 60 min60 min→07Time Series Analysis: ARIMA, Prophet, ML ForecastingCONCEPT· 60 min60 min→08Reinforcement Learning Introduction: Q-Learning & AgentsCONCEPT· 60 min60 min→09Model Deployment: API Design & Serving InfrastructureCONCEPT· 60 min60 min→10MLOps Fundamentals: Pipelines, Monitoring, VersioningCONCEPT· 60 min60 min→11Model Optimization: Quantization, Pruning, DistillationCONCEPT· 60 min60 min→12Production Best Practices: A/B Testing, Drift, DebuggingCONCEPT· 60 min60 min→