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約15時間で修了
英語
字幕:英語, 韓国語, ベトナム語, 中国語(簡体)

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Python ProgrammingMachine Learning ConceptsMachine LearningDeep Learning

受講生の就業成果

32%

コース終了後に新しいキャリアをスタートした

30%

コースが具体的なキャリアアップにつながった
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。
次における4の1コース
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。
約15時間で修了
英語
字幕:英語, 韓国語, ベトナム語, 中国語(簡体)

提供:

ワシントン大学(University of Washington) ロゴ

ワシントン大学(University of Washington)

シラバス - 本コースの学習内容

コンテンツの評価Thumbs Up93%(45,992 件の評価)Info
1

1

3時間で修了

Welcome

3時間で修了
18件のビデオ (合計84分), 8 readings, 1 quiz
18件のビデオ
Who we are5 分
Machine learning is changing the world3 分
Why a case study approach?7 分
Specialization overview6 分
How we got into ML3 分
Who is this specialization for?4 分
What you'll be able to do57
The capstone and an example intelligent application6 分
The future of intelligent applications2 分
Starting a Jupyter Notebook5 分
Creating variables in Python7 分
Conditional statements and loops in Python8 分
Creating functions and lambdas in Python3 分
Starting Turi Create & loading an SFrame4 分
Canvas for data visualization4 分
Interacting with columns of an SFrame4 分
Using .apply() for data transformation5 分
8件の学習用教材
Important Update regarding the Machine Learning Specialization10 分
Slides presented in this module10 分
Getting started with Python, Jupyter Notebook, & Turi Create10 分
Where should my files go?10 分
Important changes from previous courses10 分
Download the Jupyter Notebook used in this lesson to follow along10 分
Download the Jupyter Notebook used in this lesson to follow along10 分
Download Wiki People Data10 分
1の練習問題
SFrames15 分
2

2

2時間で修了

Regression: Predicting House Prices

2時間で修了
19件のビデオ (合計82分), 3 readings, 2 quizzes
19件のビデオ
What is the goal and how might you naively address it?3 分
Linear Regression: A Model-Based Approach5 分
Adding higher order effects4 分
Evaluating overfitting via training/test split6 分
Training/test curves4 分
Adding other features2 分
Other regression examples3 分
Regression ML block diagram5 分
Loading & exploring house sale data7 分
Splitting the data into training and test sets2 分
Learning a simple regression model to predict house prices from house size3 分
Evaluating error (RMSE) of the simple model2 分
Visualizing predictions of simple model with Matplotlib4 分
Inspecting the model coefficients learned1 分
Exploring other features of the data6 分
Learning a model to predict house prices from more features3 分
Applying learned models to predict price of an average house5 分
Applying learned models to predict price of two fancy houses7 分
3件の学習用教材
Slides presented in this module10 分
Download the Jupyter Notebook used in this lesson to follow along10 分
Predicting house prices assignment10 分
2の練習問題
Regression18 分
Predicting house prices6 分
3

3

2時間で修了

Classification: Analyzing Sentiment

2時間で修了
19件のビデオ (合計75分), 3 readings, 2 quizzes
19件のビデオ
What is an intelligent restaurant review system?4 分
Examples of classification tasks4 分
Linear classifiers5 分
Decision boundaries3 分
Training and evaluating a classifier4 分
What's a good accuracy?3 分
False positives, false negatives, and confusion matrices6 分
Learning curves5 分
Class probabilities1 分
Classification ML block diagram3 分
Loading & exploring product review data2 分
Creating the word count vector2 分
Exploring the most popular product4 分
Defining which reviews have positive or negative sentiment4 分
Training a sentiment classifier3 分
Evaluating a classifier & the ROC curve4 分
Applying model to find most positive & negative reviews for a product4 分
Exploring the most positive & negative aspects of a product4 分
3件の学習用教材
Slides presented in this module10 分
Download the Jupyter Notebook used in this lesson to follow along10 分
Analyzing product sentiment assignment10 分
2の練習問題
Classification14 分
Analyzing product sentiment22 分
4

4

2時間で修了

Clustering and Similarity: Retrieving Documents

2時間で修了
17件のビデオ (合計76分), 3 readings, 2 quizzes
17件のビデオ
What is the document retrieval task?1 分
Word count representation for measuring similarity6 分
Prioritizing important words with tf-idf3 分
Calculating tf-idf vectors5 分
Retrieving similar documents using nearest neighbor search2 分
Clustering documents task overview2 分
Clustering documents: An unsupervised learning task4 分
k-means: A clustering algorithm3 分
Other examples of clustering6 分
Clustering and similarity ML block diagram7 分
Loading & exploring Wikipedia data5 分
Exploring word counts5 分
Computing & exploring TF-IDFs7 分
Computing distances between Wikipedia articles5 分
Building & exploring a nearest neighbors model for Wikipedia articles3 分
Examples of document retrieval in action4 分
3件の学習用教材
Slides presented in this module10 分
Download the Jupyter Notebook used in this lesson to follow along10 分
Retrieving Wikipedia articles assignment10 分
2の練習問題
Clustering and Similarity12 分
Retrieving Wikipedia articles18 分

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機械学習専門講座について

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data....
機械学習

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