Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
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 ساعات
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الإنجليزية
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. We will creating a Convolutional Neural Network for a 10 Class Image Classification problem which can be extended to more classes. We will start off by looking at how perform data preparation and Augmentation in Pytorch.
We will be building a Neural Network in Pytorch. We will add the Convolutional Layers as well as Linear Layers. We will then look at how to add optimizer and train the model. Finally, we will test and evaluate our model on test data.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
Fashion Classification with Deep Learning for Beginners
Hello everyone and welcome to this hands-on guided project on deep learning 101. The objective of this project is to predict fashion class such as pants, shirts, and shoes from grayscale images. This guided project is practical and directly applicable to the fashion industry. You guys 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
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 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data.
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Course by
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Self Paced
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22 ساعات
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الإنجليزية
Semantic Segmentation 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.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Diabetic Retinopathy Detection with Artificial Intelligence
In this project, we will train deep neural network model based on Convolutional Neural Networks (CNNs) and Residual Blocks to detect the type of Diabetic Retinopathy from images. Diabetic Retinopathy is the leading cause of blindness in the working-age population of the developed world and estimated to affect over 347 million people worldwide. Diabetic Retinopathy is disease that results from complication of type 1 & 2 diabetes and can develop if blood sugar levels are left uncontrolled for a prolonged period of time.
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Course by
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Self Paced
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4 ساعات
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الإنجليزية
Machine Learning Modeling Pipelines 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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48 ساعات
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الإنجليزية
Using SAS Viya REST APIs with Python and R
SAS Viya is an in-memory distributed environment used to analyze big data quickly and efficiently. In this course, you’ll learn how to use the SAS Viya APIs to take control of SAS Cloud Analytic Services from a Jupyter Notebook using R or Python. You’ll learn to upload data into the cloud, analyze data, and create predictive models with SAS Viya using familiar open source functionality via the SWAT package -- the SAS Scripting Wrapper for Analytics Transfer. You’ll learn how to create both machine learning and deep learning models to tackle a variety of data sets and complex problems.
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Course by
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Self Paced
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18 ساعات
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الإنجليزية
Introduction to Search Techniques in Python: Binary Search
By the end of this project, you will be able to code the binary search technique using Python programming language. Throughout the tasks, you will be able to identify and apply the basic skills needed for every programming language such as lists, functions, recursion and if conditions. Each part of this project will prepare you to code on your own in Python language, whether to work on search techniques or simple coding
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
Image Super Resolution Using Autoencoders in Keras
Welcome to this 1.5 hours long hands-on project on Image Super Resolution using Autoencoders in Keras. In this project, you’re going to learn what an autoencoder is, use Keras with Tensorflow as its backend to train your own autoencoder, and use this deep learning powered autoencoder to significantly enhance the quality of images. That is, our neural network will create high-resolution images from low-res source images.
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Course by
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Self Paced
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2 ساعات
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الإنجليزية
What is Data Science?
Do you want to know why data science has been labeled the sexiest profession of the 21st century? After taking this course, you will be able to answer this question, understand what data science is and what data scientists do, and learn about career paths in the field. The art of uncovering insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and accurately predicted the Nile River's flooding every year. Since then, people have continued to use data to derive insights and predict outcomes.
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Course by
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Self Paced
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19 ساعات
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الإنجليزية
Sentiment Analysis with Deep Learning using BERT
In this 2-hour long project, you will learn how to analyze a dataset for sentiment analysis. You will learn how to read in a PyTorch BERT model, and adjust the architecture for multi-class classification. You will learn how to adjust an optimizer and scheduler for ideal training and performance. In fine-tuning this model, you will learn how to design a train and evaluate loop to monitor model performance as it trains, including saving and loading models.
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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
AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take.
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Course by
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Self Paced
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11 ساعات
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الإنجليزية
Unsupervised Learning, Recommenders, Reinforcement Learning
In the third course of the Machine Learning Specialization, you will: • Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection. • Build recommender systems with a collaborative filtering approach and a content-based deep learning method. • Build a deep reinforcement learning model. The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online.
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Course by
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Self Paced
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28 ساعات
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الإنجليزية
Advanced Deep Learning Methods for Healthcare
This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project. The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data.
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Course by
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17 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية
Data Pipelines with TensorFlow Data Services
Bringing a machine learning model into the real world involves a lot more than just modeling.
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Course by
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Self Paced
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12 ساعات
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الإنجليزية
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.
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Course by
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Self Paced
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24 ساعات
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الإنجليزية
Sequences, Time Series and Prediction
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In this fourth course, you will learn how to build time series models in TensorFlow. You’ll first implement best practices to prepare time series data. You’ll also explore how RNNs and 1D ConvNets can be used for prediction.
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Course by
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Self Paced
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23 ساعات
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الإنجليزية
Neural Networks and Deep Learning
In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.
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Course by
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Self Paced
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25 ساعات
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الإنجليزية
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 ساعات
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الإنجليزية