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.
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Machine Learning: Algorithms in the Real World 專項課程Alberta Machine Intelligence Institute課程信息
21,949 次近期查看
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第 2 門課程（共 4 門）
完成時間大約為9 小時
英語（English）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 意大利語, 越南語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 2 門課程（共 4 門）
完成時間大約為9 小時
英語（English）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 意大利語, 越南語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）
提供方
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
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
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
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
審閱
來自MACHINE LEARNING ALGORITHMS: SUPERVISED LEARNING TIP TO TAIL的熱門評論
由 DS 提供2020年5月6日
Excellent course for an overview of different ML algorithms. The course is made from a perspective of giving insights in process and not too many mathematical details.
由 SK 提供2020年4月11日
Excellent course. In which I had in-depth knowledge of all algorithms and the way she explained attracts to listen except for her spontaneity and speed in progressing.
由 CW 提供2020年9月29日
Great course, easy to grasp the main idea of how to assess and tune the performance of question-answering machines learned by machine learning algorithms through data
由 RM 提供2020年12月8日
I found the course to be enough detailed to get clarity on the basic concepts of Supervised learning algorithms. I hope to apply the learning from the course in work!
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.
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