このコースについて

13,501 最近の表示
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。
約18時間で修了
英語
字幕:英語
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。
約18時間で修了
英語
字幕:英語

提供:

SAS ロゴ

SAS

シラバス - 本コースの学習内容

1

1

1時間で修了

Course Overview

1時間で修了
1件のビデオ (合計1分), 4 readings, 1 quiz
1件のビデオ
4件の学習用教材
Learner Prerequisites1 分
Using SAS® Viya® for Learners with This Course (Required)10 分
Course Information (Required)10 分
Using Forums and Getting Help5 分
2時間で修了

SAS® Viya® and Open Source Integration

2時間で修了
10件のビデオ (合計55分)
10件のビデオ
Cloud Analytic Services2 分
Jupyter Notebooks and Open Source Development Interfaces2 分
SAS Scripting Wrapper for Analytics Transfer2 分
CAS Actions in SAS Viya2 分
Connecting to CAS and Reading in Data1 分
DataFrames and CAS Tables on the Clients and Server2 分
Advantages to Open Source Integration2 分
Demo: Getting Started with CAS and the R API18 分
Demo: Getting Started with CAS and the Python API18 分
5の練習問題
Question 2.0110 分
Question 2.0210 分
Question 2.0310 分
Question 2.0410 分
SAS® Viya® and Open Source Integration Quiz30 分
2

2

4時間で修了

Machine Learning

4時間で修了
15件のビデオ (合計107分)
15件のビデオ
Data Partitioning: Preventing Overfitting2 分
Logistic Regression Models3 分
Support Vector Machines2 分
Decision Trees2 分
Ensemble of Trees2 分
Neural Network Models3 分
Autotuning Hyperparameters1 分
Model Performance Assessment2 分
Model Performance Charts: ROC and Lift2 分
Demo: Using the R API to Create and Assess Models26 分
Demo: Using the Python API to Create and Assess Models25 分
Demo: Creating a Gradient Boosting Model in SAS Studio7 分
Demo: Using R Functions and Looping for Efficient Coding11 分
Demo: Using Python Functions and Looping for Efficient Coding11 分
4の練習問題
Question 3.0110 分
Question 3.0210 分
Question 3.0310 分
Machine Learning Quiz30 分
3

3

2時間で修了

Text Analytics

2時間で修了
9件のビデオ (合計48分)
9件のビデオ
Natural and Formal Languages1 分
Processing Words1 分
Processing Context2 分
Processing Concepts1 分
Extracting Information from the Term-Document Matrix3 分
Word Embedding3 分
Demo: Using the R API to Explore Text Documents15 分
Demo: Using the Python API to Explore Text Documents15 分
3の練習問題
Question 4.0110 分
Question 4.0210 分
Text Analytics Quiz30 分
3時間で修了

Deep Learning

3時間で修了
13件のビデオ (合計67分)
13件のビデオ
Hidden Unit Activation Functions2 分
Weight Initialization1 分
Regularization Methods3 分
Nonlinear Optimization Algorithms (or Gradient-Based Learning)3 分
Processors for Analytics1 分
Deep Neural Networks (DNN) versus Recurrent Neural Networks (RNN)2 分
Recurrent Neural Network Architecture1 分
Improving RNN Models1 分
Gated Recurrent Unit (GRU)2 分
Long Short-Term Memory (LSTM)2 分
Demo: Deep Learning Sentiment Prediction Using the R API21 分
Demo: Deep Learning Sentiment Prediction Using the Python API21 分
3の練習問題
Question 5.0110 分
Question 5.0210 分
Deep Learning Quiz30 分
4

4

3時間で修了

Time Series

3時間で修了
11件のビデオ (合計63分)
11件のビデオ
Model Performance and Assessment2 分
Weighted Averages1 分
Simple Exponential Smoothing2 分
ARIMAX Models and Stationarity1 分
Autoregressive and Moving Average Terms2 分
Forecasting with Recurrent Neural Networks43
Demo: Automatic Forecasting Using the R API8 分
Demo: Automatic Forecasting Using the Python API8 分
Demo: Deep Learning Forecasting Using the R API16 分
Demo: Deep Learning Forecasting Using the Python API16 分
4の練習問題
Question 6.0110 分
Question 6.0210 分
Question 6.0310 分
Time Series Quiz30 分
2時間で修了

Image Classification

2時間で修了
7件のビデオ (合計43分)
7件のビデオ
Convolutional Neural Networks for Image Classification1 分
Convolution Layers3 分
Pooling Layers1 分
Fully Connected and Output Layers59
Demo: Classifying Color Images Using the R API16 分
Demo: Classifying Color Images Using the Python API16 分
2の練習問題
Question 7.0110 分
Image Classification Quiz30 分
2時間で修了

Factorization Machines

2時間で修了
4件のビデオ (合計29分)
4件のビデオ
Factorization Machines for Recommendation3 分
Demo: Modeling Sparse Data Using the R API11 分
Demo: Modeling Sparse Data Using the Python API11 分
2の練習問題
Question 8.0110 分
Factorization Machines Quiz30 分

よくある質問

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

  • When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you’ve earned a Course Certificate, even if you complete the course within the two-week refund period. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You’ll be prompted to complete an application and will be notified if you are approved. Learn more.

さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。