このコースについて

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自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
約11時間で修了
英語
字幕:英語

習得するスキル

Statistical AnalysisMachine LearningPython ProgrammingComputer ProgrammingLinear Algebra
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
約11時間で修了
英語
字幕:英語

提供:

Alberta Machine Intelligence Institute ロゴ

Alberta Machine Intelligence Institute

シラバス - 本コースの学習内容

1

1

2時間で修了

What Does Good Data look like?

2時間で修了
11件のビデオ (合計65分), 2 readings, 3 quizzes
11件のビデオ
Business Understanding and Problem Discovery9 分
No Free Lunch Theorem5 分
Exploring the process of problem definition7 分
Data Acquisition and Understanding8 分
Metadata Matters5 分
Dealing with Multimodal Data2 分
Features and transformations of raw data6 分
Identifying Data from Problem5 分
Case Study: Problem from Data6 分
Weekly Summary What does good data look like?4 分
2件の学習用教材
Machine Learning Process Lifecycle Review10 分
Match Data to the needs of the learning Algorithm10 分
3の練習問題
Business Understanding and Problem Discovery (BUPD) Review10 分
Data Acquisition and Understanding Review10 分
Module 1 Quiz30 分
2

2

2時間で修了

Preparing your Data for Machine Learning Success

2時間で修了
11件のビデオ (合計61分)
11件のビデオ
Converting to Useful Forms7 分
Data Quality5 分
How Much Data Do I Need?4 分
Everything has to be Numbers6 分
Types of Data5 分
Aligning Similar Data4 分
Imputing Missing Values7 分
Data Transformations7 分
Weekly Summary: Preparing your Data for Machine Learning Success1 分
Data Cleaning: Everybody's favourite task4 分
4の練習問題
Data Warehousing Review10 分
Everything has to be Numbers Review10 分
Types of Data Review10 分
Module 2 Quiz30 分
3

3

5時間で修了

Feature Engineering for MORE Fun & Profit

5時間で修了
8件のビデオ (合計45分), 2 readings, 4 quizzes
8件のビデオ
Useful/Useless Features6 分
How Many Features?5 分
What is Unsupervised Learning6 分
Feature Selection7 分
Feature Extraction2 分
Transfer Learning7 分
Weekly Summary: Feature Engineering for MORE Fun & Profit1 分
2件の学習用教材
Possibilities for Text Features10 分
Word Embeddings10 分
3の練習問題
Understanding Features6 分
Building Good Features6 分
Understanding Transfer Learning4 分
4

4

2時間で修了

Bad Data

2時間で修了
9件のビデオ (合計48分)
9件のビデオ
Generalization and how machines actually learn6 分
Bias in Data Sources3 分
Bias and variance tradeoff6 分
Outliers5 分
Skewed Distributions7 分
Badness Multipliers4 分
Live Data Danger6 分
Weekly Summary: Bad Data1 分
4の練習問題
Mistakes Computers Make10 分
Data: Skewed Distributions10 分
Live Data Dangers10 分
Module 4 Quiz30 分

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Machine Learning: Algorithms in the Real World専門講座について

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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