Acerca de este Curso
294 calificaciones
48 revisiones

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel avanzado

Aprox. 20 horas para completar

Sugerido: 4 weeks of study, 4-6 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Habilidades que obtendrás

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

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel avanzado

Aprox. 20 horas para completar

Sugerido: 4 weeks of study, 4-6 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

5 horas para completar

Introduction to deep learning

17 videos (Total 65 minutos), 5 readings, 2 quizzes
17 videos
Introduction - Romeo Kienzler30s
Introduction - Ilja Rasin1m
Introduction - Niketan Pansare30s
Introduction - Tom Hanlon1m
Course Logistics1m
Cloud Architectures for AI and DeepLearning4m
Linear algebra6m
Deep feed forward neural networks12m
Convolutional Neural Networks4m
Recurrent neural networks1m
Auto encoders and representation learning2m
Methods for neural network training8m
Gradient Descent Updater Strategies6m
How to choose the correct activation function3m
The bias-variance tradeoff in deep learning3m
5 lecturas
IBM Digital Badge10m
Video summary on environment setup10m
Where to get all the code and slides for download?10m
Introduction to ApacheSpark10m
Link to Github10m
1 ejercicios de práctica
DeepLearning Fundamentals14m
7 horas para completar

deep learning frameworks

24 videos (Total 168 minutos), 1 reading, 5 quizzes
24 videos
Neural Network Debugging with TensorBoard7m
Automatic Differentiation2m
Introduction video44s
Keras overview5m
Sequential models in keras6m
Feed forward networks7m
Recurrent neural networks9m
Beyond sequential models: the functional API3m
Saving and loading models2m
What is SystemML (1/2) ?3m
What is SystemML (2/2) ?6m
Demo - How to use Apache SystemML on IBM DSX (1/3)4m
Demo - How to use Apache SystemML on IBM DSX (2/3)3m
Demo - How to use Apache SystemML on IBM DSX (3/3)8m
Introduction to DeepLearning4J12m
Demo: Running Java in Data Science Experience8m
DL4J Neural Network Code Example, Mnist Classifier14m
PyTorch Installation2m
PyTorch Packages2m
Tensor Creation and Visualization of Higher Dimensional Tensors6m
Math Computation and Reshape7m
Computation Graph, CUDA17m
Linear Model17m
1 lecturas
Link to files in Github10m
4 ejercicios de práctica
Apache SystemML12m
DL4J Fundamentals12m
PyTorch Introduction12m
6 horas para completar

DeepLearning Applications

18 videos (Total 115 minutos), 2 readings, 5 quizzes
18 videos
How to implement an anomaly detector (1/2)11m
How to implement an anomaly detector (2/2)2m
How to deploy a real-time anomaly detector2m
Introduction to Time Series Forecasting4m
Stateful vs. Stateless LSTMs6m
Batch Size5m
Number of Time Steps, Epochs, Training and Validation8m
Trainin Set Size4m
Input and Output Data Construction7m
Designing the LSTM network in Keras10m
Anatomy of a LSTM Node12m
Number of Parameters7m
Training and loading a saved model4m
Classifying the MNIST dataset with Convolutional Neural Networks5m
Image classification with Imagenet and Resnet503m
Autoencoder - understanding Word2Vec8m
Text Classification with Word Embeddings4m
2 lecturas
Generative Adversarial Networks (GANs) (optional)10m
Generative Adversarial Networks (GANs) (optional)10m
4 ejercicios de práctica
Anomaly Detection12m
Sequence Classification with Keras LSTM Network12m
Image Classification6m
4 horas para completar

scaling and deployment

5 videos (Total 40 minutos), 3 readings, 2 quizzes
5 videos
Creating and Scaling a Keras Model in ApacheSpark using DL4J14m
Creating and Scaling a Keras Model in ApacheSpark using DL4J (Demo)16m
Computer Vision with IBM Watson Visual Recognition2m
Text Classification with IBM Watson Natural Language Classifier1m
3 lecturas
Parallel Neural Network Training10m
Scale a Keras Model with IBM Watson Machine Learning10m
Link to Github10m
1 ejercicios de práctica
Run a Notebook using Keras and DL4J6m
48 revisionesChevron Right


comenzó una nueva carrera después de completar estos cursos


consiguió un beneficio tangible en su carrera profesional gracias a este curso


consiguió un aumento de sueldo o ascenso

Principales revisiones

por RCApr 26th 2018

It was really great learning with coursera and I loved the course. The way faculty teaches here is just awesome as they are very much clear and helped a lot while learning this coursea

por MKApr 16th 2018

Useful information course have. There are some challenges.\n\nHowever, the instructor, Romeo is great!\n\nA real Jedi master!



Romeo Kienzler

Chief Data Scientist, Course Lead
IBM Watson IoT

Niketan Pansare

Senior Software Engineer
IBM Research

Tom Hanlon

Training Director

Max Pumperla

Deep Learning Engineer

Ilja Rasin

Data Scientist
IBM Watson Health

Acerca de IBM

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame....

Acerca del programa especializado Advanced Data Science with IBM

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
Advanced Data Science with IBM

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

  • 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:

    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

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