中
An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio’s AutoAI experiment tool, explaining the underlying technology at work as developed by IBM Research. The focus will be on working with an auto-generated Python notebook. Learners will be provided with test data sets for two use cases.
Machine Learning Rapid Prototyping with IBM Watson Studio
您将获得的技能
- MLOps (Machine Learning Operations)
- Artificial Intelligence and Machine Learning (AI/ML)
- Machine Learning
- Feature Engineering
- Python Programming
- Automation
- Performance Tuning
- Applied Machine Learning
- Data Science
- Machine Learning Methods
- Data Processing
- Scikit Learn (Machine Learning Library)
- Data Cleansing
- Predictive Modeling
- Data Transformation
- IBM Cloud
- Exploratory Data Analysis
要了解的详细信息
了解顶级公司的员工如何掌握热门技能
该课程共有4个模块
In this module, you'll learn about the developing landscape of AutoAI technologies. You'll also become familiar with the Watson Studio platform in order to be able to perform your own AutoAI Experiments. After observing the AutoAI tool build prototypes for two use cases, you will try out the tool for yourself to build additional prototypes.
涵盖的内容
7个视频14篇阅读材料4个作业
In this module, you will learn about the automated data preparation techniques performed by AutoAI and get a chance to experiment with different settings for data preprocessing in the AutoAI-generated Python notebook. You'll also learn about the procedure for automated model selection and experiment using different models on the datasets.
涵盖的内容
9个视频11篇阅读材料3个作业
In this module, you will learn about the algorithm for automated feature engineering and perform some exploratory data analysis to try to understand why the algorithm performed particular feature transformations. You'll also learn about sophisticated methods for optimizing hyperparameters and explore hyperparameter tuning on the datasets using the AutoAI-generated Python notebook.
涵盖的内容
9个视频11篇阅读材料3个作业
In this module, you will evaluate prototypes using the different evaluation metrics calculated by the AutoAI tool. You will also deploy the prototype for testing using the Watson Machine Learning API.
涵盖的内容
4个视频9篇阅读材料3个作业1次同伴评审
提供方
从 Machine Learning 浏览更多内容
状态：免费试用
状态：免费试用
Coursera Project Network
状态：免费试用
人们为什么选择 Coursera 来帮助自己实现职业发展
Felipe M.
自 2018开始学习的学生
''能够按照自己的速度和节奏学习课程是一次很棒的经历。只要符合自己的时间表和心情，我就可以学习。'
Jennifer J.
自 2020开始学习的学生
''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'
Larry W.
自 2021开始学习的学生
''如果我的大学不提供我需要的主题课程，Coursera 便是最好的去处之一。'
Chaitanya A.
''学习不仅仅是在工作中做的更好：它远不止于此。Coursera 让我无限制地学习。'
学生评论
4.2
15 条评论
- 5 stars
73.33%
- 4 stars
0%
- 3 stars
13.33%
- 2 stars
0%
- 1 star
13.33%
显示 3/15 个
NB
5
已于 Sep 13, 2020审阅
Very much informative and useful with hands on excercise
常见问题
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
更多问题
提供助学金，
¹ 本课程的部分作业采用 AI 评分。对于这些作业，将根据 Coursera 隐私声明使用您的数据。