受众
- 数据科学家
- 机器学习从业者
- AI工程师
- 对机器学习算法和概念有深入理解
- 精通Python编程
- 有Jupyter Notebooks或Google Colab使用经验
課程定制選項
課程形式
高級機器學習模型在推動AI和數據科學的邊界中扮演著至關重要的角色。本課程深入探討使用Google Colab構建、優化和部署機器學習模型的複雜技術，使參與者能夠利用強大的雲端工具進行項目開發。
通過本培訓，參與者將能夠：
這是由講師主導的培訓（線上或線下），旨在幫助高級專業人士提升機器學習模型的知識，改進超參數調優技能，並學習如何使用Google Colab有效部署模型。
- 使用Scikit-learn和TensorFlow等流行框架實現高級機器學習模型。
- 通過超參數調優優化模型性能。
- 使用Google Colab在實際應用中部署機器學習模型。
- 在Google Colab中協作和管理大規模機器學習項目。
- 互動式講座和討論。
- 大量練習和實踐。
- 在實時實驗室環境中進行實踐操作。
- 如需定制本課程的培訓，請聯繫我們進行安排。
超参数调优与优化
高级Machine Learning模型介绍
模型部署
模型可解释性与可解释性
Neural Networks与Deep Learning
实际应用与案例分析
总结与下一步
使用Google Colab进行大规模Machine Learning
- 在医疗、金融和电子商务中应用高级模型
- 案例分析：成功的模型部署
- 高级机器学习中的挑战与未来趋势
- 构建和训练深度神经网络
- 使用预训练模型进行迁移学习
- 优化深度学习模型的性能
- 在Colab中协作机器学习项目
- 使用Colab进行分布式训练和GPU/TPU加速
- 与云服务集成以进行可扩展的模型训练
- 探索模型可解释性技术（LIME, SHAP）
- 深度学习模型的可解释性AI
- 处理机器学习模型中的偏见与公平性
- 网格搜索与随机搜索技术
- 使用Google Colab自动化超参数调优
- 使用高级优化技术（贝叶斯、遗传算法）
- 模型部署策略介绍
- 使用Google Colab在云环境中部署模型
- 实时推理与批处理
- 复杂模型概述：Random Forests、梯度提升、Neural Networks
- 何时使用高级模型：最佳实践与用例
- 集成学习技术介绍
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