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Our Courses
Traffic Sign Classification Using Deep Learning in Python/Keras
In this 1-hour long project-based course, you will be able to: - Understand the theory and intuition behind Convolutional Neural Networks (CNNs).
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Course by
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Self Paced
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2 hours
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English
Deep learning in Electronic Health Records - CDSS 2
Overview of the main principles of Deep Learning along with common architectures. Formulate the problem for time-series classification and apply it to vital signals such as ECG. Applying this methods in Electronic Health Records is challenging due to the missing values and the heterogeneity in EHR, which include both continuous, ordinal and categorical variables. Subsequently, explore imputation techniques and different encoding strategies to address these issues. Apply these approaches to formulate clinical prediction benchmarks derived from information available in MIMIC-III database.
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Course by
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Self Paced
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32 hours
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English
Machine Learning Data Lifecycle in Production
**Starting May 8, enrollment for the Machine Learning Engineering for Production Specialization will be closed.
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Course by
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Self Paced
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22 hours
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English
Deploying Machine Learning Models in Production
**Starting May 8, enrollment for the Machine Learning Engineering for Production Specialization will be closed. Please enroll in this specialization or to individual courses by then to gain access to this course material.** In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. You will build scalable and reliable hardware infrastructure to deliver inference requests both in real-time and batch depending on the use case.
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Course by
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Self Paced
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33 hours
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English
Introduction to Deep Learning
Deep Learning is the go-to technique for many applications, from natural language processing to biomedical. Deep learning can handle many different types of data such as images, texts, voice/sound, graphs and so on. This course will cover the basics of DL including how to build and train multilayer perceptron, convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders (AE) and generative adversarial networks (GANs).
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Course by
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Self Paced
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60 hours
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English
Introduction to Machine Learning with Python
This course will give you an introduction to machine learning with the Python programming language. You will learn about supervised learning, unsupervised learning, deep learning, image processing, and generative adversarial networks. You will implement machine learning models using Python and will learn about the many applications of machine learning used in industry today. You will also learn about and use different machine learning algorithms to create your models. You do not need a programming or computer science background to learn the material in this course.
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Course by
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Self Paced
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13 hours
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English
Intermediate Intel® Distribution of OpenVINO™ toolkit for Deep Learning Applications
This course is designed for application developers who wants to deploy computer vision inference workloads using the Intel® Distribution of OpenVINOTM toolkit. The course looks at computer vision neural network models from a variety of popular machine learning frameworks and covers writing a portable application capable of deploying inference on a range of compute devices.
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Course by
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Self Paced
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English
Computer Vision with Embedded Machine Learning
Computer vision (CV) is a fascinating field of study that attempts to automate the process of assigning meaning to digital images or videos. In other words, we are helping computers see and understand the world around us!
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Course by
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Self Paced
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31 hours
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English
Fake News Detection with Machine Learning
In this hands-on project, we will train a Bidirectional Neural Network and LSTM based deep learning model to detect fake news from a given news corpus. This project could be practically used by any media company to automatically predict whether the circulating news is fake or not. The process could be done automatically without having humans manually review thousands of news related articles. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
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Course by
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Self Paced
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3 hours
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English
Classify Images of Cats and Dogs using Transfer Learning
This is a self-paced lab that takes place in the Google Cloud console. 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. This lab uses transfer learning to train your machine. In transfer learning, when you build a new model to classify your original dataset, you reuse the feature extraction part and re-train the classification part with your dataset.
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Course by
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Self Paced
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1 hour
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English
AI Capstone Project with Deep Learning
In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate it. Learners will then present a project report to demonstrate the validity of their model and their proficiency in the field of Deep Learning.
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Course by
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Self Paced
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16 hours
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English
Introduction to Artificial Intelligence (AI)
Artificial Intelligence (AI) is all around us, seamlessly integrated into our daily lives and work. Enroll in this course to understand the key AI terminology and applications and launch your AI career or transform your existing one. This course covers core AI concepts, including deep learning, machine learning, and neural networks. You’ll examine generative AI models, including large language models (LLMs) and their capabilities.
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Course by
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Self Paced
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9 hours
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English
Build Decision Trees, SVMs, and Artificial Neural Networks
There are numerous types of machine learning algorithms, each of which has certain characteristics that might make it more or less suitable for solving a particular problem. Decision trees and support-vector machines (SVMs) are two examples of algorithms that can both solve regression and classification problems, but which have different applications. Likewise, a more advanced approach to machine learning, called deep learning, uses artificial neural networks (ANNs) to solve these types of problems and more.
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Course by
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Self Paced
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22 hours
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English
Transfer Learning for NLP with TensorFlow Hub
This is a hands-on project on transfer learning for natural language processing with TensorFlow and TF Hub.
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Course by
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Self Paced
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2 hours
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English
Introduction to Computer Vision
Introduction to Computer Vision guides learners through the essential algorithms and methods to help computers 'see' and interpret visual data. You will first learn the core concepts and techniques that have been traditionally used to analyze images. Then, you will learn modern deep learning methods, such as neural networks and specific models designed for image recognition, and how it can be used to perform more complex tasks like object detection and image segmentation.
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Course by
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Self Paced
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8 hours
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English
Introduction to AI in the Data Center
Welcome to the Introduction to AI in the Data Center Course!
As you know, Artificial Intelligence, or AI, is transforming society in many ways.
From speech recognition to improved supply chain management, AI technology provides enterprises with the compute power, tools, and algorithms their teams need to do their life’s work.
But how does AI work in a Data Center? What hardware and software infrastructure are needed?
These are some of the questions that this course will help you address.
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Course by
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Self Paced
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5 hours
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English
Create Image Captioning Models
This course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images
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Course by
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Self Paced
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1 hour
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English
Fashion Image Classification using CNNs in Pytorch
In this 1-hour long project-based course, you will learn how to create Neural Networks in the Deep Learning Framework PyTorch.
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Course by
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Self Paced
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2 hours
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English
Machine Translation
Welcome to the CLICS-Machine Translation MOOC This MOOC explains the basic principles of machine translation. Machine translation is the task of translating from one natural language to another natural language. Therefore, these algorithms can help people communicate in different languages. Such algorithms are used in common applications, from Google Translate to apps on your mobile device. After taking this course you will be able to understand the main difficulties of translating natural languages and the principles of different machine translation approaches.
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Course by
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Self Paced
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28 hours
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English
Explainable AI: Scene Classification and GradCam Visualization
In this 2 hour long hands-on project, we will train a deep learning model to predict the type of scenery in images. In addition, we are going to use a technique known as Grad-Cam to help explain how AI models think. This project could be practically used for detecting the type of scenery from the satellite images.
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Course by
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Self Paced
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3 hours
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English
Fashion Classification with Deep Learning for Beginners
Hello everyone and welcome to this hands-on guided project on deep learning 101.
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Course by
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Self Paced
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2 hours
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English
Introduction to Machine Learning on AWS
In this course, we start with some services where the training model and raw inference is handled for you by Amazon. We'll cover services which do the heavy lifting of computer vision, data extraction and analysis, language processing, speech recognition, translation, ML model training and virtual agents. You'll think of your current solutions and see where you can improve these solutions using AI, ML or Deep Learning. All of these solutions can work with your current applications to make some improvements in your user experience or the business needs of your application.
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Course by
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7 hours
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English
Generative Deep Learning with TensorFlow
In this course, you will: a) Learn neural style transfer using transfer learning: extract the content of an image (eg. swan), and the style of a painting (eg. cubist or impressionist), and combine the content and style into a new image.
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Course by
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Self Paced
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17 hours
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English
Natural Language Processing with Attention Models
In Course 4 of the Natural Language Processing Specialization, you will: a) Translate complete English sentences into Portuguese using an encoder-decoder attention model, b) Build a Transformer model to summarize text, c) Use T5 and BERT models to perform question-answering. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, and created tools to translate languages and summarize text! Learners should have a working knowledge of machine learning, intermediate Python including experience with a deep learning fra
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Course by
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Self Paced
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35 hours
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English
Structuring Machine Learning Projects
In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.
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Course by
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Self Paced
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7 hours
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English