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Data Analytics and Machine Learning (ML Ops) on the Cloud
注册接收电子邮件 关于开放大学的机器学习项目.
课程描述:
Machine Learning as a field is now incredibly pervasive, with applications in areas including business intelligence, 国土安全, 生化相互作用分析, 基础设施监控, 和天体物理学. Deep learning is a branch of machine learning concerned with the development and application of modern neural networks. Deep learning algorithms extract layered high-level representations of data in a way that maximizes performance of a give task. Deep learning is behind many recent advances in AI, 包括Siri的语音识别功能, Facebook的标签建议, machine language translation and self-driving cars. This course is an introduction to 使用TensorFlow的机器学习.0, which is a very popular framework for building predictive models. The course will provide a step by step approach to building complex machine learning models starting from the very basics concepts of machine learning and the TensorFlow 2.Google的0框架. We will be using a variety of tools and platforms such as Python, TensorFlow / Keras, and Google Collaboratory Notebooks for building, 测试, 部署机器学习模型.
This 7-week program contains 42 contact hours of online, synchronous instruction and is broken into 3 modules and covers fundamental topics exposing students to Artificial Intelligence and Machine Learning. The program is ideal for graduating and working engineers new to the Artificial Intelligence and Machine Learning world.
This program contains specializations for 零售, 医疗保健, 金融服务 and 工业/制造业. You can select one or more specializations as part of the course (each specialization is 3-5 weeks long). You will understand the use cases defined below and implement one use case end-to-end as a part of your project.
A PACE Certificate of Achievement will be awarded upon successful completion of the program.
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学习成果
By the end of the course, students will be able to:
- 解释机器学习模型是如何工作的
- Frame tasks into machine learning problems
- Use machine learning toolkits to implement the designed models
- Justify when and why specific machine learning techniques work for specific problems
- Build, test, and deploy complex machine learning models to solve specific problems
暂定课程大纲
Each module contains corresponding hands-on labs covering module topics.
- Python教程,包括:
- 数据类型 & 字符串
- 用户自定义函数
- 熊猫系列
- λ & Map
- 类和对象导论
This module covers the fundamentals of Python programming. 完成本模块后, students will be able to write reasonably complex Python code for working with data. The following table shows the topics covered in this module.
Python基础知识
主题 | Details |
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Python基础知识 |
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数据结构 |
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使用模块 |
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类和对象导论 |
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Files, streams, database connectivity and API |
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- 机器学习概论
- 什么是机器学习?
- 介绍TensorFlow
- 构建TensorFlow模型
- 扩展和模型部署
This module focuses on the fundamentals of machine learning and the commonly used ML and Deep Learning models on the Google Cloud platform. 使用TensorFlow构建模型, training and assessing their performance using TensorBoard, and deploying the models will be discussed.
使用TensorFlow的机器学习.0
主题 | Details |
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机器学习概论 |
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什么是机器学习? |
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介绍TensorFlow |
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构建TensorFlow模型 |
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扩展和模型部署 |
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模块2的实验
Lab1: Implement a Linear Regression and KNN Model.
Lab2: Create a model using TensorFlow - Feature Engineering for a DNN Model
Lab3: Improve the model performance using Feature Engineering.
Lab4: Deploy the TensorFlow model using Flask API.
This module is industry specialization for 零售, 医疗保健, 金融服务 and 工业/制造业. Students can select one or more specializations as part of the course. This module will focus on the use cases defined below, and students will implement one use case end-to-end as part of the capstone project.
行业集中 | 用例 |
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零售 |
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医疗保健 |
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金融服务 |
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工业/制造业 |
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模块3的实验
Lab1: Understand a business problem and implement an exploratory data analysis using Python.
Lab2: Create a machine learning model using TensorFlow
Lab3: Improve the model performance using Feature Engineering
Lab4: Deploy the TensorFlow model using Flask API