このコースについて

142,685 最近の表示

受講生の就業成果

23%

コース終了後に新しいキャリアをスタートした

38%

コースが具体的なキャリアアップにつながった

57%

昇給や昇進につながった
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。
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。
上級レベル
約22時間で修了
英語
字幕:英語

習得するスキル

Machine LearningDeep LearningLong Short-Term Memory (ISTM)Apache Spark

受講生の就業成果

23%

コース終了後に新しいキャリアをスタートした

38%

コースが具体的なキャリアアップにつながった

57%

昇給や昇進につながった
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。
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。
上級レベル
約22時間で修了
英語
字幕:英語

提供:

IBM ロゴ

IBM

シラバス - 本コースの学習内容

コンテンツの評価Thumbs Up84%(2,913 件の評価)Info
1

1

5時間で修了

Introduction to deep learning

5時間で修了
16件のビデオ (合計61分), 4 readings, 2 quizzes
16件のビデオ
Introduction - Romeo Kienzler30
Introduction - Ilja Rasin1 分
Introduction - Niketan Pansare30
Course Logistics1 分
Cloud Architectures for AI and DeepLearning2 分
Linear algebra6 分
Deep feed forward neural networks12 分
Convolutional Neural Networks4 分
Recurrent neural networks1 分
LSTMs3 分
Auto encoders and representation learning2 分
Methods for neural network training8 分
Gradient Descent Updater Strategies6 分
How to choose the correct activation function3 分
The bias-variance tradeoff in deep learning3 分
4件の学習用教材
IBM Digital Badge10 分
Video summary on environment setup10 分
Where to get all the code and slides for download?10 分
Link to Github10 分
1の練習問題
DeepLearning Fundamentals14 分
2

2

7時間で修了

DeepLearning Frameworks

7時間で修了
18件のビデオ (合計116分), 1 reading, 5 quizzes
18件のビデオ
Neural Network Debugging with TensorBoard7 分
Automatic Differentiation2 分
Introduction video44
Keras overview5 分
Sequential models in keras6 分
Feed forward networks7 分
Recurrent neural networks9 分
Beyond sequential models: the functional API3 分
Saving and loading models2 分
What is SystemML (1/2)3 分
What is SystemML (2/2)6 分
PyTorch Installation2 分
PyTorch Packages2 分
Tensor Creation and Visualization of Higher Dimensional Tensors6 分
Math Computation and Reshape7 分
Computation Graph, CUDA17 分
Linear Model17 分
1件の学習用教材
Link to files in Github10 分
4の練習問題
TensorFlow12 分
TensorFlow 2.x12 分
Apache SystemML12 分
PyTorch Introduction12 分
3

3

6時間で修了

DeepLearning Applications

6時間で修了
18件のビデオ (合計115分)
18件のビデオ
How to implement an anomaly detector (1/2)11 分
How to implement an anomaly detector (2/2)2 分
How to deploy a real-time anomaly detector2 分
Introduction to Time Series Forecasting4 分
Stateful vs. Stateless LSTMs6 分
Batch Size5 分
Number of Time Steps, Epochs, Training and Validation8 分
Trainin Set Size4 分
Input and Output Data Construction7 分
Designing the LSTM network in Keras10 分
Anatomy of a LSTM Node12 分
Number of Parameters7 分
Training and loading a saved model4 分
Classifying the MNIST dataset with Convolutional Neural Networks5 分
Image classification with Imagenet and Resnet503 分
Autoencoder - understanding Word2Vec8 分
Text Classification with Word Embeddings4 分
4の練習問題
Anomaly Detection12 分
Sequence Classification with Keras LSTM Network12 分
Image Classification6 分
NLP6 分
4

4

4時間で修了

Scaling and Deployment

4時間で修了
3件のビデオ (合計9分), 2 readings, 2 quizzes
3件のビデオ
Computer Vision with IBM Watson Visual Recognition2 分
Text Classification with IBM Watson Natural Language Classifier1 分
2件の学習用教材
Exercise: Scale a Deep Learning Model on IBM Watson Machine Learning10 分
Link to Github10 分
1の練習問題
Methods of parallel neural network training6 分

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Advanced Data Science with IBM専門講座について

As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability. If you choose to take this specialization and earn the Coursera specialization certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging....
Advanced Data Science with IBM

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 enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. 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.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. 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. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • The IBM Watson IoT Certified Data Scientist degree is a Coursera specialization IBM is currently creating. This course is one part of 3-4 courses coming up the next couple of months

    Currently only this and another course exist. The other one is the following:

    https://www.coursera.org/learn/exploring-visualizing-iot-data

    The course above will be modified and renamed to "Fundamentals of Applied DataScience" - but if you pass it today, it counts towards the certificate as well

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。