This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML.
本課程是 Machine Learning: Algorithms in the Real World 專項課程 專項課程的一部分
Machine Learning Algorithms: Supervised Learning Tip to Tail
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第 2 門課程（共 4 門）
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完成時間大約為13 小時
建議：12 hours/week
英語（English）
字幕：英語（English）
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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
教學大綱 - 您將從這門課程中學到什麼
完成時間為 4 小時
Classification using Decision Trees and k-NN
Welcome to Supervised Learning, Tip to Tail! This week we'll go over the basics of supervised learning, particularly classification, as well as teach you about two classification algorithms: decision trees and k-NN. You'll get started programming on the platform through Jupyter notebooks and start to familiarize yourself with all the issues that arise when using machine learning for classification.
完成時間為 4 小時
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
完成時間為 2 小時
Functions for Fun and Profit
Welcome to the second week of the course! In this week you'll learn all about regression algorithms, the other side of supervised learning. We'll introduce you to the idea of finding lines, optimization criteria, and all the associated issues. Through regression we'll see the interactions between model complexity and accuracy, and you'll get a first taste of how regression and classification might relate.
完成時間為 2 小時
9 個視頻 （總計 62 分鐘）, 1 個閱讀材料, 4 個測驗
9 個視頻
Line-fitting6分鐘
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
完成時間為 3 小時
Regression for Classification: Support Vector Machines
This week we'll be diving straight in to using regression for classification. We'll describe all the fundamental pieces that make up the support vector machine algorithms, so that you can understand how many seemingly unrelated machine learning algorithms tie together. We'll introduce you to logistic regression, neural networks, and support vector machines, and show you how to implement two of those.
完成時間為 3 小時
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
完成時間為 1 小時
Contrasting Models
Now at the tail end of the course, we're going to go over how to know how well your model is actually performing and what you can do to get even better performance from it. We'll review assessment questions particular to regression and classification, and introduce some other tools that really help you analyze your model performance. The topics covered this week aim to give you confidence in your models, so you're ready to unlock the power of machine learning for your business goals.
完成時間為 1 小時
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
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.
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