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完成時間大約為12 小時

建議:12 hours/week...

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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。

完成時間大約為12 小時

建議:12 hours/week...

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

Classification using Decision Trees and k-NN

8 個視頻 (總計 46 分鐘), 4 個閱讀材料, 2 個測驗
8 個視頻
What does a classifier actually do?5分鐘
Classification in scikit-learn3分鐘
What are decision trees?6分鐘
Generalization and overfitting8分鐘
Classification using k-nearest neighbours8分鐘
Distance measures8分鐘
Weekly summary2分鐘
4 個閱讀材料
Math Review10分鐘
Scikitlearn documentation for decision trees (Optional)10分鐘
Scikitlearn documentation for random forests (Optional)10分鐘
Scikitlearn documentation for k-nearest neighbours (Optional)10分鐘
2 個練習
Supervised Learning Basics
Understanding Classification with Decision Trees and k-NN20分鐘
2
完成時間為 2 小時

Functions for Fun and Profit

9 個視頻 (總計 62 分鐘), 1 個閱讀材料, 4 個測驗
9 個視頻
Optimal line-fitting8分鐘
Loss and Convexity7分鐘
Gradient Descent9分鐘
Nonlinear features and model complexity6分鐘
Bias and variance tradeoff6分鐘
Regularizers5分鐘
Loss for Classification7分鐘
Weekly summary4分鐘
1 個閱讀材料
Scikitlearn documentation for linear regression (Optional)10分鐘
4 個練習
Regression Basics
Understanding Model Complexity
From Regression to Classification2分鐘
The Regression side of Supervised Learning20分鐘
3
完成時間為 3 小時

Regression for Classification: Support Vector Machines

6 個視頻 (總計 34 分鐘), 1 個閱讀材料, 2 個測驗
6 個視頻
Neural Networks9分鐘
Hinge Loss6分鐘
Basics of Support Vector Machines6分鐘
Kernels6分鐘
Weekly Summary1分鐘
1 個閱讀材料
Scikitlearn documentation for SVMs (Optional)10分鐘
2 個練習
Understanding Support Vector Machines
Regression-based Classification10分鐘
4
完成時間為 1 小時

Contrasting Models

8 個視頻 (總計 46 分鐘), 1 個閱讀材料, 1 個測驗
8 個視頻
Classification assessment6分鐘
Learning Curves6分鐘
Testing your models7分鐘
Cross validation5分鐘
Parameter tuning and grid search5分鐘
Model Parameters6分鐘
Weekly Summary1分鐘
1 個閱讀材料
Some resources on model assessment (Optional)10分鐘
1 個練習
Contrasting Models
4.8
4 個審閱Chevron Right

來自Machine Learning Algorithms: Supervised Learning Tip to Tail的熱門評論

創建者 MJOct 30th 2019

Great course! I received so much useful information from AMII.

講師

Avatar

Anna Koop

Senior Scientific Advisor
Alberta Machine Intelligence Institute, University of Alberta

關於 Alberta Machine Intelligence Institute

The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based research institute that pushes the bounds of academic knowledge and guides business understanding of artificial intelligence and machine learning....

關於 Machine Learning: Algorithms in the Real World 專項課程

This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application. After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning....
Machine Learning: Algorithms in the Real World

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