Generative AI for Business Leaders
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How Google does Machine Learning
دوراتنا
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
Visual Perception for Self-Driving Cars
Welcome to Visual Perception for Self-Driving Cars, the third course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the main perception tasks in autonomous driving, static and dynamic object detection, and will survey common computer vision methods for robotic perception. By the end of this course, you will be able to work with the pinhole camera model, perform intrinsic and extrinsic camera calibration, detect, describe and match image features and design your own convolutional neural networks.
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Course by
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Self Paced
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31 ساعات
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الإنجليزية
Utilisation des méthodes de Deep Learning avec Python pour la prédiction boursière.
#### ***A la fin de ce projet, vous serez en mesure de :*** * Créer une application Web connectée à ***Yahoo Finance*** pour prédire la tendance de plusieurs actions boursières.
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Course by
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Self Paced
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3 ساعات
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الفرنسية
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
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
Unsupervised Text Classification for Marketing Analytics
Marketing data is often so big that humans cannot read or analyze a representative sample of it to understand what insights might lie within. In this course, learners use unsupervised deep learning to train algorithms to extract topics and insights from text data. Learners walk through a conceptual overview of unsupervised machine learning and dive into real-world datasets through instructor-led tutorials in Python. The course concludes with a major project. This course uses Jupyter Notebooks and the coding environment Google Colab, a browser-based Jupyter notebook environment.
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Course by
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Self Paced
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13 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
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 ساعات
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الإنجليزية
Transfer Learning for Food Classification
In this hands-on project, we will train a deep learning model to predict the type of food and then fine tune the model to improve its performance. This project could be practically applied in food industry to detect the type and quality of food. In this 2-hours long project-based course, you will be able to:
- Understand the theory and intuition behind Convolutional Neural Networks (CNNs).
- Understand the theory and intuition behind transfer learning.
- Import Key libraries, dataset and visualize images.
- Perform data augmentation.
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Course by
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Self Paced
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3 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
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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Course by
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Self Paced
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الإنجليزية
TensorFlow Serving with Docker for Model Deployment
This is a hands-on, guided project on deploying deep learning models using TensorFlow Serving with Docker. In this 1.5 hour long project, you will train and export TensorFlow models for text classification, learn how to deploy models with TF Serving and Docker in 90 seconds, and build simple gRPC and REST-based clients in Python for model inference.
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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
TensorFlow for AI: Neural Network Representation
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.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
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.AI TensorFlow Developer Professional Certificate offered at Coursera, 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
TensorFlow for AI: Applying Image Convolution
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.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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2 ساعات
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الإنجليزية
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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Course by
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Self Paced
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الإنجليزية
Technologies and platforms for Artificial Intelligence
This course will address the hardware technologies for machine and deep learning (from the units of an Internet-of-Things system to a large-scale data centers) and will explore the families of machine and deep learning platforms (libraries and frameworks) for the design and development of smart applications and systems.
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Course by
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Self Paced
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3 ساعات
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الإنجليزية
Supervised Text Classification for Marketing Analytics
Marketing data often requires categorization or labeling. In today’s age, marketing data can also be very big, or larger than what humans can reasonably tackle. In this course, students learn how to use supervised deep learning to train algorithms to tackle text classification tasks. Students walk through a conceptual overview of supervised machine learning and dive into real-world datasets through instructor-led tutorials in Python.
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Course by
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Self Paced
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12 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
Series temporales con Deep Learning (RNN, LSTM) y Prophet
En este proyecto aplicado y práctico aprenderás a entrenar redes neuronales recurrentes (RNN y LSTM) y modelos de Prophet para predecir series temporales. Tanto las redes LSTM como Prophet son algunos de los modelos más avanzados para predecir valores futuros en base a series de tiempo. Por ello, te enseñaremos a como pre-procesar y preparar tus datos, a entrenar los modelos, a evaluarlos, a optimizarlos y a utilizarlos para predecir datos futuros.
Al finalizar este curso habrás aprendido a entrenar tus propios modelos y a aplicarlos en tus propios proyectos.
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Course by
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Self Paced
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3 ساعات
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الإسبانية
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
Sequence Models
In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more.
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Course by
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Self Paced
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37 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
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