课程信息
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第 4 门课程(共 6 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

高级

完成时间大约为6 小时

建议:This course requires 7.5 to 9 hours of study....

英语(English)

字幕:英语(English)

您将获得的技能

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming

第 4 门课程(共 6 门)

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

高级

完成时间大约为6 小时

建议:This course requires 7.5 to 9 hours of study....

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间为 4 小时

Model Evaluation and Performance Metrics

6 个视频 (总计 18 分钟), 19 个阅读材料, 6 个测验
6 个视频
Evaluation Metrics2分钟
Introduction to Predictive Linear and Logistic Regression3分钟
Linear Models4分钟
Watson Natural Language Understanding Service Overview3分钟
Case Study Introduction1分钟
19 个阅读材料
Evaluation metrics: Through the eyes of our Working Example3分钟
Evaluation Metrics3分钟
Regression metrics5分钟
Classification metrics10分钟
Multi-class and multi-label metrics3分钟
Model performance: Through the eyes of our Working Example3分钟
Generalizing well to unseen data3分钟
Model plots, bias, variance4分钟
Relating the evaluation metric to a business metric4分钟
Linear models: Through the eyes of our Working Example3分钟
Generalized linear models5分钟
Linear and logistic regression5分钟
Regularized regression3分钟
Stochastic gradient descent classifier3分钟
Watson Natural Language Understanding: Through the eyes of our Working Example3分钟
Watson Developer Cloud Python SDK10分钟
Performance and business metrics: Through the eyes of our Working Example3分钟
Getting started with performance and business metrics case study (hands-on)2小时
Summary/Review10分钟
6 个练习
Check for Understanding2分钟
Check for Understanding2分钟
Check for Understanding2分钟
Check for Understanding2分钟
Check for Understanding2分钟
End of Module Quiz10分钟
2
完成时间为 3 小时

Building Machine Learning and Deep Learning Models

5 个视频 (总计 15 分钟), 14 个阅读材料, 5 个测验
5 个视频
Introduction to Tree Based Methods2分钟
Neural Networks2分钟
Introduction to neural networks4分钟
IBM Watson Visual Recognition Overview2分钟
14 个阅读材料
Tree-based methods: Through the eyes of our Working Example3分钟
Decision trees4分钟
Bagging and Random forests4分钟
Boosting2分钟
Ensemble learning4分钟
Neural networks: Through the eyes of our Working Example3分钟
Multilayer perceptron (MLP)4分钟
Neural network architectures4分钟
On interpretability2分钟
Watson Visual Recognition: Through the eyes of our Working Example3分钟
Watson Developer Cloud Python SDK10分钟
TensorFlow: Through the eyes of our Working Example3分钟
Getting started with Convolutional neural networks and TensorFlow (hands-on)2小时
Summary/Review10分钟
5 个练习
Check for Understanding2分钟
Check for Understanding2分钟
Check for Understanding2分钟
Check for Understanding2分钟
End of Module Quiz10分钟

讲师

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Mark J Grover

Digital Content Delivery Lead
IBM Data & AI Learning
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Ray Lopez, Ph.D.

Data Science Curriculum Leader
IBM Data & Artificial Intelligence

关于 IBM

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame....

关于 IBM AI Enterprise Workflow 专项课程

This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production. The learning aims to elevate the skills of practicing data scientists by explicitly connecting business priorities to technical implementations, connecting machine learning to specialized AI use cases such as visual recognition and NLP, and connecting Python to IBM Cloud technologies. The videos, readings, and case studies in these courses are designed to guide you through your work as a data scientist at a hypothetical streaming media company. Throughout this specialization, the focus will be on the practice of data science in large, modern enterprises. You will be guided through the use of enterprise-class tools on the IBM Cloud, tools that you will use to create, deploy and test machine learning models. Your favorite open source tools, such a Jupyter notebooks and Python libraries will be used extensively for data preparation and building models. Models will be deployed on the IBM Cloud using IBM Watson tooling that works seamlessly with open source tools. After successfully completing this specialization, you will be ready to take the official IBM certification examination for the IBM AI Enterprise Workflow....
IBM AI Enterprise Workflow

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