Coursera近期刚刚推了一门新专项课程：谷歌云平台上基于TensorFlow的高级机器学习专项课程（Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization)，看起来很不错。这个系列包含5门子课程，涵盖端到端机器学习、生产环境机器学习系统、图像理解、面向时间序列和自然语言处理的序列模型、推荐系统等内容，感兴趣的同学可以关注：Learn Advanced Machine Learning with Google Cloud. Build production-ready machine learning models with TensorFlow on Google Cloud Platform.
课程链接：http://coursegraph.com/coursera-specializations-advanced-machine-learning-tensorflow-gcp
五门子课程分别是：
1、基于TensorFlow的端到端机器学习（End-to-End Machine Learning with TensorFlow on GCP)
http://coursegraph.com/coursera-end-to-end-ml-tensorflow-gcp
这门课程将首先回顾一下谷歌云平台上的TensorFlow机器学习专项课程系列（Machine Learning with TensorFlow on Google Cloud Platform Specialization）的相关内容。 回顾这些内容的最佳方法之一是就是使用学到的概念和技术。 因此，这门课程被设置为研讨会，在这个研讨会中，学员将在谷歌云平台上使用TensorFlow进行端到端机器学习。先决条件：基础的SQL知识，熟悉Python和TensorFlow。
In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization. One of the best ways to review something is to work with the concepts and technologies that you have learned. So, this course is set up as a workshop and in this workshop, you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform Prerequisites: Basic SQL, familiarity with Python and TensorFlow
2、生产环境机器学习系统（Production ML Systems）
http://coursegraph.com/coursera-gcp-production-ml-systems
在该系列的第二个课程中，将深入研究生产环境中高性能机器学习系统的组件和最佳实践。 先决条件：基础的SQL知识，熟悉Python和TensorFlow。
In the second course of this specialization, we will dive into the components and best practices of a high-performing ML system in production environments. Prerequisites: Basic SQL, familiarity with Python and TensorFlow
3、Image Understanding with TensorFlow on GCP（通过TensorFlow进行图像理解）
http://coursegraph.com/coursera-specializations-advanced-machine-learning-tensorflow-gcp
这是谷歌云平台高级机器学习专项课程系列第三课：通过TensorFlow进行图像理解。在这门课程中，首先将介绍使用卷积神经网络构建图像分类器的不同策略。其次将通过数据增强，特征提取和超参数调优来提高模型的准确性，同时避免过度拟合数据。学习过程中还将研究实际出现的问题，例如，当图像数据不足时如何处理问题以及如何将最新的研究成果纳入我们的模型。最后在这门课程的实践平台上，学员将在不同的公共数据集上构建和优化自己的图像分类器模型。先决条件：基础的SQL知识，熟悉Python和TensorFlow。
This is the third course of the Advanced Machine Learning on GCP specialization. In this course, We will take a look at different strategies for building an image classifier using convolutional neural networks. We’ll improve the model’s accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting our data. We will also look at practical issues that arise, for example, when you don’t have enough data and how to incorporate the latest research findings into our models. You will get hands-on practice building and optimizing your own image classification models on a variety of public datasets in the labs we’ll work on together. Prerequisites: Basic SQL, familiarity with Python and TensorFlow
4、Sequence Models for Time Series and Natural Language Processing（面向时间序列和自然语言处理的序列模型）
http://coursegraph.com/coursera-sequence-models-tensorflow-gcp
谷歌云平台高级机器学习专项课程系列第四课：面向时间序列和自然语言处理的序列模型。这门课程将主要介绍序列模型及其应用，包括序列模型结构概览以及如何处理可变长输入。预测时间序列的未来值 • 对自由格式文本进行分类 • 使用递归神经网络解决时间序列和文本问题 • 在RNN/LSTM和更简单的模型之间进行选择 • 在文本问题中训练和重用词嵌入模型。在这门课程的实践平台上，学员将在不同的公共数据集上亲自构建和优化自己的文本分类器和序列模型。先决条件：基础的SQL知识，熟悉Python和TensorFlow。
This course is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length. • Predict future values of a time-series • Classify free form text • Address time-series and text problems with recurrent neural networks • Choose between RNNs/LSTMs and simpler models • Train and reuse word embeddings in text problems You will get hands-on practice building and optimizing your own text classification and sequence models on a variety of public datasets in the labs we’ll work on together. Prerequisites: Basic SQL, familiarity with Python and TensorFlow
5、Recommendation Systems with TensorFlow on GCP（基于TensorFlow的推荐系统）
http://coursegraph.com/coursera-recommendation-models-gcp
谷歌云平台高级机器学习专项课程系列第四课：基于TensorFlow的推荐系统。在本课程中，将应用分类模型和嵌入(embeddings)的知识来构建充当推荐引擎的机器学习组件。 设计基于内容的推荐引擎 • 实现一个协同过滤推荐引擎 • 构建具有用户和内容嵌入的混合推荐引擎。
In this course, you’ll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. • Devise a content-based recommendation engine • Implement a collaborative filtering recommendation engine • Build a hybrid recommendation engine with user and content embeddings
注：原创文章，转载请注明出处“课程图谱博客”：http://blog.coursegraph.com
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