課程信息
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第 4 門課程(共 6 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

高級

完成時間大約為7 小時

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

英語(English)

字幕:英語(English)

您將獲得的技能

Data ScienceInformation EngineeringArtificial Intelligence (AI)Machine LearningPython Programming

第 4 門課程(共 6 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

高級

完成時間大約為7 小時

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

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 4 小時

Model Evaluation and Performance Metrics

6 個視頻 (總計 18 分鐘), 19 個閱讀材料, 6 個測驗
6 個視頻
Evaluation Metrics2分鐘
Introduction to Predictive Linear and Logistic Regression3分鐘
Linear Models4分鐘
Watson Natural Language Understanding Service Overview3分鐘
Case Study Introduction1分鐘
19 個閱讀材料
Evaluation metrics: Through the eyes of our Working Example3分鐘
Evaluation Metrics3分鐘
Regression metrics5分鐘
Classification metrics10分鐘
Multi-class and multi-label metrics3分鐘
Model performance: Through the eyes of our Working Example3分鐘
Generalizing well to unseen data3分鐘
Model plots, bias, variance4分鐘
Relating the evaluation metric to a business metric4分鐘
Linear models: Through the eyes of our Working Example3分鐘
Generalized linear models5分鐘
Linear and logistic regression5分鐘
Regularized regression3分鐘
Stochastic gradient descent classifier3分鐘
Watson Natural Language Understanding: Through the eyes of our Working Example3分鐘
Watson Developer Cloud Python SDK10分鐘
Performance and business metrics: Through the eyes of our Working Example3分鐘
Getting started with performance and business metrics case study (hands-on)2小時
Summary/Review10分鐘
6 個練習
Check for Understanding2分鐘
Check for Understanding2分鐘
Check for Understanding2分鐘
Check for Understanding2分鐘
Check for Understanding2分鐘
End of Module Quiz10分鐘
2
完成時間為 3 小時

Building Machine Learning and Deep Learning Models

5 個視頻 (總計 15 分鐘), 14 個閱讀材料, 5 個測驗
5 個視頻
Introduction to Tree Based Methods2分鐘
Neural Networks2分鐘
Introduction to neural networks4分鐘
IBM Watson Visual Recognition Overview2分鐘
14 個閱讀材料
Tree-based methods: Through the eyes of our Working Example3分鐘
Decision trees4分鐘
Bagging and Random forests4分鐘
Boosting2分鐘
Ensemble learning4分鐘
Neural networks: Through the eyes of our Working Example3分鐘
Multilayer perceptron (MLP)4分鐘
Neural network architectures4分鐘
On interpretability2分鐘
Watson Visual Recognition: Through the eyes of our Working Example3分鐘
Watson Developer Cloud Python SDK10分鐘
TensorFlow: Through the eyes of our Working Example3分鐘
Getting started with Convolutional neural networks and TensorFlow (hands-on)2小時
Summary/Review10分鐘
5 個練習
Check for Understanding2分鐘
Check for Understanding2分鐘
Check for Understanding2分鐘
Check for Understanding2分鐘
End of Module Quiz10分鐘

講師

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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

關於 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....

關於 IBM AI Enterprise Workflow 專項課程

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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