Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
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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الإنجليزية
PyTorch Basics for Machine Learning
This course is the first part in a two part course and will teach you the fundamentals of PyTorch. In this course you will implement classic machine learning algorithms, focusing on how PyTorch creates and optimizes models. You will quickly iterate through different aspects of PyTorch giving you strong foundations and all the prerequisites you need before you build deep learning models.
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Course by
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الإنجليزية
PyTorch and Deep Learning for Decision Makers
Learn how PyTorch, a deep learning framework, can be used to automate and optimize processes through the development and deployment of state-of-the-art AI applications. The course will also help you understand the importance of data quality, how to choose the right model, and the challenges in deploying and maintaining both deep learning and machine learning applications.
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Course by
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Self Paced
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15
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الإنجليزية
Applied Deep Learning Capstone Project
In this capstone project, you'll use either Keras or PyTorch to develop, train, and test a Deep Learning model. Load and preprocess data for a real problem, build the model and then validate it.
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Course by
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64
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الإنجليزية
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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Course by
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الإنجليزية
Deep Learning with Python and PyTorch
This course is the second part of a two-part course on how to develop Deep Learning models using Pytorch.
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Course by
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Self Paced
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72
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الإنجليزية
Deep Learning Fundamentals with Keras
New to deep learning? Start with this course, that will not only introduce you to the field of deep learning but give you the opportunity to build your first deep learning model using thepopular Keras library.
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Course by
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Self Paced
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30
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الإنجليزية
Deep Learning for Real Estate Price Prediction
In this hands-on guided project, we will predict real estate prices with deep learning. In this project, we will predict home sale prices in King County in the U.S. between May, 2014 and May, 2015 using several features such as number of bedrooms, bathrooms, view, and square footage. This guided project is practical and directly applicable to the real estate industry. You can add this project to your portfolio of projects which is essential for your next job interview.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Explainable deep learning models for healthcare - CDSS 3
This course will introduce the concepts of interpretability and explainability in machine learning applications. The learner will understand the difference between global, local, model-agnostic and model-specific explanations. State-of-the-art explainability methods such as Permutation Feature Importance (PFI), Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanation (SHAP) are explained and applied in time-series classification.
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Course by
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Self Paced
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30 ساعات
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الإنجليزية
Facial Expression Classification Using Residual Neural Nets
In this hands-on project, we will train a deep learning model based on Convolutional Neural Networks (CNNs) and Residual Blocks to detect facial expressions. This project could be practically used for detecting customer emotions and facial expressions.
By the end of this project, you will be able to:
- Understand the theory and intuition behind Deep Learning, Convolutional Neural Networks (CNNs) and Residual Neural Networks.
- Import Key libraries, dataset and visualize images.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
Detect Fake News in Python with Tensorflow
"Fake News" is a word used to mean different things to different people. At its heart, we define "fake news" as any news stories which are false: the article itself is fabricated without verifiable evidence, citations or quotations. Often these stories may be lies and propaganda that is deliberately intended to confuse the viewer, or may be characterized as "click-bait" written for monetary incentives (the writer profits on the number of people who click on the story).
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Introduction to Deep Learning & Neural Networks with Keras
Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks?
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Course by
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Self Paced
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8 ساعات
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الإنجليزية
Remote Sensing Image Acquisition, Analysis and Applications
Welcome to Remote Sensing Image Acquisition, Analysis and Applications, in which we explore the nature of imaging the earth's surface from space or from airborne vehicles. This course covers the fundamental nature of remote sensing and the platforms and sensor types used. It also provides an in-depth treatment of the computational algorithms employed in image understanding, ranging from the earliest historically important techniques to more recent approaches based on deep learning.
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Course by
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Self Paced
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23 ساعات
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الإنجليزية
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).
- Import Key libraries, dataset and visualize images.
- Perform image normalization and convert from color-scaled to gray-scaled images.
- Build a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend.
- Compile and fit Deep Learning model to training data.
- Assess the performance of trained CNN and ensure its generalization using various KPIs.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Using TensorFlow with Amazon Sagemaker
Please note: You will need an AWS account to complete this course. Your AWS account will be charged as per your usage. Please make sure that you are able to access Sagemaker within your AWS account. If your AWS account is new, you may need to ask AWS support for access to certain resources. You should be familiar with python programming, and AWS before starting this hands on project. We use a Sagemaker P type instance in this project, and if you don't have access to this instance type, please contact AWS support and request access.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Data Balancing with Gen AI: Credit Card Fraud Detection
In this 2-hour guided project, you will learn how to leverage Generative AI for data generation to address data imbalance. SecureTrust Financial Services, a financial institution, has asked us to help them improve the accuracy of their fraud detection system. The model is a binary classifier, but it's not performing well due to data imbalance. As data scientists, we will employ Generative Adversarial Networks (GANs), a subset of Generative AI, to create synthetic fraudulent transactions that closely resemble real transactions.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
AI for Everyone: Master the Basics
Learn what Artificial Intelligence (AI) is by understanding its applications and key concepts including machine learning, deep learning and neural networks.
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Course by
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33
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الإنجليزية
Machine Learning for Investment Professionals
This course is uniquely tailored to the needs of investment professionals or those with investment industry knowledge who want to develop a basic, practical understanding of machine learning techniques and how they are used in the investment process. Incorporating real-life case studies, this course covers both the technical and the “soft skills” necessary for investment professionals to stay relevant.
In this course, you will learn how to:
-\tDistinguish between supervised and unsupervised machine learning and deep learning
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Course by
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Self Paced
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17 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
TensorFlow for CNNs: Learn and Practice CNNs
This guided project course is part of the "Tensorflow for Convolutional Neural Networks" series, and this series presents material that builds on the second course of DeepLearning.AI TensorFlow Developer Professional Certificate, which will help learners reinforce their skills and build more projects with Tensorflow.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Convolutions for Text Classification with Keras
Welcome to this hands-on, guided introduction to Text Classification using 1D Convolutions with Keras. By the end of this project, you will be able to apply word embeddings for text classification, use 1D convolutions as feature extractors in natural language processing (NLP), and perform binary text classification using deep learning.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Deep Neural Networks with PyTorch
The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered.
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Course by
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Self Paced
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31 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية