Generative AI for Business Leaders
Advertising with Meta
How Google does Machine Learning
دوراتنا
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. Named entity recognition models can be used to identify mentions of people, locations, organizations, etc. Named entity recognition is not only a standalone tool for information extraction, but it also an invaluable preprocessing step for many downstream natural language processing applications like machine translation, question answering, and text summarization.
-
Course by
-
Self Paced
-
2 ساعات
-
الإنجليزية
Create a Superhero Name Generator with TensorFlow
In this guided project, we are going to create a neural network and train it on a small dataset of superhero names to learn to generate similar names. The dataset has over 9000 names of superheroes, supervillains and other fictional characters from a number of different comic books, TV shows and movies. Text generation is a common natural language processing task. We will create a character level language model that will predict the next character for a given input sequence.
-
Course by
-
Self Paced
-
3 ساعات
-
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.
-
Course by
-
Self Paced
-
3 ساعات
-
الإنجليزية
Machine Learning Data Lifecycle in Production
**Starting May 8, enrollment for the Machine Learning Engineering for Production Specialization will be closed.
-
Course by
-
Self Paced
-
22 ساعات
-
الإنجليزية
Feature Engineering - 한국어
이 과정에서는 Vertex AI Feature Store 사용의 이점, ML 모델의 정확성을 개선하는 방법, 가장 유용한 특성을 만드는 데이터 열을 찾는 방법을 살펴봅니다. 이 과정에는 BigQuery ML, Keras, TensorFlow를 사용한 특성 추출에 관한 콘텐츠와 실습도 포함되어 있습니다.
-
Course by
-
Self Paced
-
الإنجليزية
Using Machine Learning in Trading and Finance
This course provides the foundation for developing advanced trading strategies using machine learning techniques. In this course, you’ll review the key components that are common to every trading strategy, no matter how complex. You’ll be introduced to multiple trading strategies including quantitative trading, pairs trading, and momentum trading.
-
Course by
-
Self Paced
-
19 ساعات
-
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.
-
Course by
-
Self Paced
-
3 ساعات
-
الإنجليزية
Getting Started with Tensorflow.js
By the end of this project, you will learn how to code a smart webcam to detect people and other everyday objects using a pre-trained COCO-SSD image recognition model with Tensorflow.js.
Based on an older library called deeplearn.js, Tensorflow.js is a deep learning library that leverages Tensorflow to create, train and run inference on artificial neural network models directly in a web browser, utilizing the client's GPU/CPU resources (accelerated using WebGL). Tensorflow.js brings Tensorflow to the web!
JavaScript/Typescript experience is heavily recommended.
-
Course by
-
Self Paced
-
2 ساعات
-
الإنجليزية
Generate Synthetic Images with DCGANs in Keras
In this hands-on project, you will learn about Generative Adversarial Networks (GANs) and you will build and train a Deep Convolutional GAN (DCGAN) with Keras to generate images of fashionable clothes. We will be using the Keras Sequential API with Tensorflow 2 as the backend.
-
Course by
-
Self Paced
-
3 ساعات
-
الإنجليزية
Bank Loan Approval Prediction With Artificial Neural Nets
In this hands-on project, we will build and train a simple deep neural network model to predict the approval of personal loan for a person based on features like age, experience, income, locations, family, education, exiting mortgage, credit card etc.
By the end of this project, you will be able to:
- Understand the applications of Artificial Intelligence and Machine Learning techniques in the banking industry
- Understand the theory and intuition behind Deep Neural Networks
- Import key Python libraries, dataset, and perform Exploratory Data Analysis.
-
Course by
-
Self Paced
-
3 ساعات
-
الإنجليزية
Fine-tuning Convolutional Networks to Classify Dog Breeds
In this 2 hour-long project, you will learn how to approach an image classification task using TensorFlow. You will learn how to effectively preprocess your data to improve model generalizability, as well as build a performant modeling pipeline. Furthermore, you will learn how to accurately evaluate model performance using a confusion matrix; how to interpret results; and how to ask poignant questions about your dataset. Finally, you will fine-tune an existing, state-of-the-art-ready model to improve performance further.
-
Course by
-
Self Paced
-
3 ساعات
-
الإنجليزية
Introduction to Computer Vision with TensorFlow
This is a self-paced lab that takes place in the Google Cloud console. In this lab you create a computer vision model that can recognize items of clothing and then explore what affects the training model.
-
Course by
-
Self Paced
-
1 ساعات
-
الإنجليزية
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.
-
Course by
-
Self Paced
-
4 ساعات
-
الإنجليزية
Custom Prediction Routine on Google AI Platform
Please note: You will need a Google Cloud Platform account to complete this course. Your GCP account will be charged as per your usage. Please make sure that you are able to access Google AI Platform within your GCP account. You should be familiar with python programming, and Google Cloud Platform before starting this hands on project. Please also ensure that you have access to the custom prediction routine feature in Google AI Platform.
-
Course by
-
Self Paced
-
2 ساعات
-
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.
-
Course by
-
الإنجليزية
Object Localization with TensorFlow
Welcome to this 2 hour long guided project on creating and training an Object Localization model with TensorFlow. In this guided project, we are going to use TensorFlow's Keras API to create a convolutional neural network which will be trained to classify as well as localize emojis in images. Localization, in this context, means the position of the emojis in the images. This means that the network will have one input and two outputs. Think of this task as a simpler version of Object Detection.
-
Course by
-
Self Paced
-
3 ساعات
-
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.
-
Course by
-
Self Paced
-
2 ساعات
-
الإنجليزية
Image Data Augmentation with Keras
In this 1.5-hour long project-based course, you will learn how to apply image data augmentation in Keras. We are going to focus on using the ImageDataGenerator class from Keras’ image preprocessing package, and will take a look at a variety of options available in this class for data augmentation and data normalization.
Since this is a practical, project-based course, you will need to prior experience with Python programming, convolutional neural networks, and Keras with a TensorFlow backend.
-
Course by
-
Self Paced
-
3 ساعات
-
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).
-
Course by
-
Self Paced
-
2 ساعات
-
الإنجليزية
Advanced Deployment Scenarios with TensorFlow
Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. In this final course, you’ll explore four different scenarios you’ll encounter when deploying models. You’ll be introduced to TensorFlow Serving, a technology that lets you do inference over the web. You’ll move on to TensorFlow Hub, a repository of models that you can use for transfer learning.
-
Course by
-
Self Paced
-
13 ساعات
-
الإنجليزية
Save, Load and Export Models with Keras
In this 1 hour long project based course, you will learn to save, load and restore models with Keras. In Keras, we can save just the model weights, or we can save weights along with the entire model architecture. We can also export the models to TensorFlow's Saved Mode format which is very useful when serving a model in production, and we can load models from the Saved Model format back in Keras as well.
In order to be successful in this project, you should be familiar with python programming, and basics of neural networks.
-
Course by
-
Self Paced
-
1 ساعات
-
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.
-
Course by
-
Self Paced
-
2 ساعات
-
الإنجليزية
Identify Horses or Humans with TensorFlow and Vertex AI
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you use convolutions to recognize features in an image where the subject can be anywhere in the image!
-
Course by
-
Self Paced
-
2 ساعات
-
الإنجليزية
Build and Operate Machine Learning Solutions with Azure
Azure Machine Learning is a cloud platform for training, deploying, managing, and monitoring machine learning models. In this course, you will learn how to use the Azure Machine Learning Python SDK to create and manage enterprise-ready ML solutions. This is the third course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurecertification exam. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning.
-
Course by
-
Self Paced
-
32 ساعات
-
الإنجليزية
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. By the time you complete this project, you will have used the R programming language to build, train, and evaluate a neural network model to classify images of clothing items into categories such as t-shirts, trousers, and sneakers. We will be training the deep learning based image classification model on the Fashion MNIST dataset which contains 70000 grayscale images of clothes across 10 categories.
-
Course by
-
Self Paced
-
3 ساعات
-
الإنجليزية