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Our Courses
AI Workflow: Machine Learning, Visual Recognition and NLP
This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company. The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regressi
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Self Paced
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14 hours
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English
Microsoft Azure Data Scientist Associate (DP-100)
This Professional Certificate is intended for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning s…
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TensorFlow: Data and Deployment
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications.
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Build a Deep Learning Based Image Classifier with R
In this 45-min guided project, you will learn the basics of using the Keras interface to R with Tensorflow as its backend to solve an image classification problem.
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Self Paced
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3 hours
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English
TensorFlow for AI: Get to Know Tensorflow
This guided project course is part of the "Tensorflow for AI" series, and this series presents material that builds on the first course of DeepLearning.
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Self Paced
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3 hours
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English
DeepLearning.AI TensorFlow Developer
TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models. In this hands-on, four-course Professional Certificate program, you’ll learn the necessary tools to build scalable AI-powered applications with TensorFlow. After finishing this program, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects.
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Self Paced
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English
Vertex AI: Qwik Start
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will use BigQuery for data processing and exploratory data analysis, and the Vertex AI platform to train and deploy a custom TensorFlow Regressor model to predict customer lifetime value (CLV). The goal of the lab is to introduce to Vertex AI through a high value real world use case - predictive CLV. Starting with a local BigQuery and TensorFlow workflow, you will progress toward training and deploying your model in the cloud with Vertex AI.
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Self Paced
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2 hours
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English
Encoder-Decoder Architecture - Italiano
Questo corso ti offre un riepilogo dell'architettura encoder-decoder, che è un'architettura di machine learning potente e diffusa per attività da sequenza a sequenza come traduzione automatica, riassunto del testo e risposta alle domande. Apprenderai i componenti principali dell'architettura encoder-decoder e come addestrare e fornire questi modelli. Nella procedura dettagliata del lab corrispondente, implementerai in TensorFlow dall'inizio un semplice codice dell'architettura encoder-decoder per la generazione di poesie da zero.
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Self Paced
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1 hour
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English
Creating Multi Task Models With Keras
In this 1 hour long guided project, you will learn to create and train multi-task, multi-output models with Keras. You will learn to use Keras' functional API to create a multi output model which will be trained to learn two different labels given the same input example. The model will have one input but two outputs. A few of the shallow layers will be shared between the two outputs, you will also use a ResNet style skip connection in the model. If you are familiar with Keras, you have probably come across examples of models that are trained to perform multiple tasks.
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Self Paced
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3 hours
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English
TensorFlow: Advanced Techniques
About TensorFlow TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing. About this Specialization Expand your knowledge of the Functional API and build exotic non-sequential model types.
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Deploy Models with TensorFlow Serving and Flask
In this 2-hour long project-based course, you will learn how to deploy TensorFlow models using TensorFlow Serving and Docker, and you will create a simple web application with Flask which will serve as an interface to get predictions from the served TensorFlow model.
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Self Paced
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3 hours
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English
Building Deep Learning Models with TensorFlow
The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this course you’ll use TensorFlow library to apply deep learning to different data types in order to solve real world problems.
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Self Paced
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7 hours
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English
TensorFlow 2 for Deep Learning
This Specialization is intended for machine learning researchers and practitioners who are seeking to develop practical skills in the popular deep learning framework TensorFlow. The first course of this Specialization will guide you through the fundamental concepts required to successfully build, train, evaluate and make predictions from deep learning models, validating your models and including regularisation, implementing callbacks, and saving and loading models. The second course will deepen your knowledge and skills with TensorFlow, in order to develop fully customised deep learning mode
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English
Deep Learning with Tensorflow
Much of theworld's data is unstructured. Think images, sound, and textual data. Learn how to apply Deep Learning with TensorFlow to this type of data to solve real-world problems.
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Classification Trees in Python, From Start To Finish
In this 1-hour long project-based course, you will learn how to build Classification Trees in Python, using a real world dataset that has missing data and categorical data that must be transformed with One-Hot Encoding. We then use Cost Complexity Pruning and Cross Validation to build a tree that is not overfit to the Training Dataset. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser.
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Self Paced
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2 hours
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English
TensorFlow for Beginners: Basic Binary Image Classification
The goal of this project is to introduce beginners to the basic concepts of machine learning using TensorFlow. The project will include, how to set up the tool and get started as well as understanding the fundamentals of machine learning/neural network model and its key concepts. Learning how to use TensorFlow for implementing machine learning algorithms, data preprocessing, supervised learning. Additionally, learners develop skills in evaluating and deploying machine learning models using TensorFlow.
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Self Paced
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4 hours
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English
Fine Tune BERT for Text Classification with TensorFlow
This is a guided project on fine-tuning a Bidirectional Transformers for Language Understanding (BERT) model for text classification with TensorFlow. In this 2.5 hour long project, you will learn to preprocess and tokenize data for BERT classification, build TensorFlow input pipelines for text data with the tf.data API, and train and evaluate a fine-tuned BERT model for text classification with TensorFlow 2 and TensorFlow Hub.
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Self Paced
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3 hours
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English
Build Multilayer Perceptron Models with Keras
In this 45-minute long project-based course, you will build and train a multilayer perceptronl (MLP) model using Keras, with Tensorflow as its backend.
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3 hours
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English
Autoscaling TensorFlow Model Deployments with TF Serving and Kubernetes
This is a self-paced lab that takes place in the Google Cloud console. AutoML Vision helps developers with limited ML expertise train high quality image recognition models. In this hands-on lab, you will learn how to train a custom model to recognize different types of clouds (cumulus, cumulonimbus, etc.).
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2 hours
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English
Natural Language Processing
Natural Language Processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language. This technology is one of the most broadly applied areas of machine learning and is critical in effectively analyzing massive quantities of unstructured, text-heavy data.
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Image Denoising Using AutoEncoders in Keras and Python
In this 1-hour long project-based course, you will be able to: - Understand the theory and intuition behind Autoencoders - Import Key libraries, dataset and visualize images - Perform image normalization, pre-processing, and add random noise to images - Build an Autoencoder using Keras with Tensorflow 2.
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Self Paced
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3 hours
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English
Named Entity Recognition using LSTMs with Keras
In this 1-hour long project-based course, you will use the Keras API with TensorFlow as its backend to build and train a bidirectional LSTM neural network model to recognize named entities in text data.
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Self Paced
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2 hours
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English
Create Machine Learning Models in Microsoft Azure
Machine learning is the foundation for predictive modeling and artificial intelligence. If you want to learn about both the underlying concepts and how to get into building models with the most common machine learning tools this path is for you. In this course, you will learn the core principles of machine learning and how to use common tools and frameworks to train, evaluate, and use machine learning models. This course is designed to prepare you for roles that include planning and creating a suitable working environment for data science workloads on Azure.
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Self Paced
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13 hours
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English
Machine Learning on Google Cloud
What is machine learning, and what kinds of problems can it solve? How can you build, train, and deploy machine learning models at scale without writing a single line of code?
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English