学院 / 课程 / COURSES / ML-ADVANCED12 LESSONS · 12时 0分
课程 05 / 08
机器学习高级Advanced 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.
课程数据表v2.1
课时12
总时长12时 0分
难度高级
分类机器学习
类型免费
更新于2025-10-15
语言EN · RU · ZH
§A · 目标
您将能够做什么。
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 · 大纲
所有课程。按顺序阅读，或跳转。
№课程类型时间
01Clustering Algorithms: K-Means, DBSCAN, Hierarchical概念· 60 min60 min→02Dimensionality Reduction: PCA, t-SNE, UMAP概念· 60 min60 min→03Anomaly Detection: Isolation Forest & One-Class SVM概念· 60 min60 min→04Gaussian Mixture Models & EM Algorithm概念· 60 min60 min→05Neural Networks Fundamentals: Perceptrons to Backpropagation概念· 60 min60 min→06Deep Learning Basics: Architectures & Training概念· 60 min60 min→07Time Series Analysis: ARIMA, Prophet, ML Forecasting概念· 60 min60 min→08Reinforcement Learning Introduction: Q-Learning & Agents概念· 60 min60 min→09Model Deployment: API Design & Serving Infrastructure概念· 60 min60 min→10MLOps Fundamentals: Pipelines, Monitoring, Versioning概念· 60 min60 min→11Model Optimization: Quantization, Pruning, Distillation概念· 60 min60 min→12Production Best Practices: A/B Testing, Drift, Debugging概念· 60 min60 min→