DeepLearning.AI |
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Advanced Computer Vision with TensorFlow (Coursera) https://www.coursera.org/learn/advanced-computer-vision-with-tensorflow In this course, you will: a) Explore image classification, image segmentation, object localization, and object detection. Apply transfer learning to object localization and detection. b) Apply object detection models such as regional-CNN and ResNet-50, customize existing models, and build your own models to detect, localize, and label your own rubber duck images. c) Implement image segmentation using variations of the fully convolutional network (FCN) including U-Net and d) Mask-RCNN to identif |
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Advanced Deployment Scenarios with TensorFlow (Coursera) https://www.coursera.org/learn/advanced-deployment-scenarios-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 |
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Advanced Learning Algorithms (Coursera) https://www.coursera.org/learn/advanced-learning-algorithms In the second course of the Machine Learning Specialization, you will: • Build and train a neural network with TensorFlow to perform multi-class classification • Apply best practices for machine learning development so that your models generalize to data and tasks in the real world • Build and use decision trees and tree ensemble methods, including random forests and boosted trees The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning. |
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AI and Disaster Management (Coursera) https://www.coursera.org/learn/ai-and-disaster-management In this course, you will be introduced to the four phases of the disaster management cycle; mitigation, preparation, response, and recovery. You’ll work through two case studies in this course. In the first, you will use computer vision to analyze satellite imagery from Hurricane Harvey in 2017 to identify damage in affected areas. In the second, you will use natural language processing techniques to explore trends in aid requests in the aftermath of the 2010 earthquake in Haiti. |
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https://www.coursera.org/learn/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. In this course, you will learn: - The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science - What AI realistically can--and cannot--do - How to spot opportunities to apply AI to problems in your own organization - What it feels like to build machine learnin |
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AI For Everyone （すべての人のためのAIリテラシー講座） (Coursera) https://www.coursera.org/learn/ai-for-everyone-ja AIはエンジニアだけのものではなく、今や社会⼈の基本リテラシーと⾔えます。本 コースは、AIの基礎を学びたい⽅、今の組織をAIを使いこなせる組織へと変⾰させ たい⽅、そんなすべての⽅々に、理系⽂系問わず、肩書きや職種問わず、受講いた だけるコースです。 本コースでは、次のことを学習していただきます： - ニューラルネットワーク、機械学習、ディープラーニング、データサイエンス など、⼀般的なAIに関する専⾨⽤語とその意味 - 実際にAIができること、できないこと - 組織の課題解決のためにAIを適⽤できる可能性とその⽅法 - 機械学習およびデータサイエンスプロジェクトの進め⽅ - AIエンジニアチームと連携して社内でAI戦略を構築する⽅法 - AIを取り巻く倫理的および社会的議論の概要 本コースはDeepLearning.AIが提供している通常の「AI for Everyone」（オリジナルは英語）に、JDLAが制作し、松尾先生が講師を務める”DXとは何か”、”DXにおけるAIの重要性”、“日本におけるAI活用”を追加 した 特別版 となります。 本コースの修了証を提示すると、JD |
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https://www.coursera.org/specializations/ai-for-good Offered by DeepLearning.AI. Learn AI's role in addressing complex challenges. Build skills combining human and machine intelligence for ... |
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AI for Medical Diagnosis (Coursera) https://www.coursera.org/learn/ai-for-medical-diagnosis AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. As an AI practitioner, you have the opportunity to join in this transformation of modern medicine. If you're already familiar with some of the math and coding behind AI algorithms, and are eager to develop your skills further to tackle challenges in the healthcare industry, then this specialization is for you. No pri |
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AI for Medical Prognosis (Coursera) https://www.coursera.org/learn/ai-for-medical-prognosis AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine. Machine learning is a powerful tool for prognosis, a branch of medicine that specializes in predicting the future health of patients. In this second course, you’ll walk through multiple examples of p |
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AI For Medical Treatment (Coursera) https://www.coursera.org/learn/ai-for-medical-treatment AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine. Medical treatment may impact patients differently based on their existing health conditions. In this third course, you’ll recommend treatments more suited to individual patients using data from rando |
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AI for Medicine (CourseraSpecs) https://www.coursera.org/specializations/ai-for-medicine Offered by DeepLearning.AI. |
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Apply Generative Adversarial Networks (GANs) (Coursera) https://www.coursera.org/learn/apply-generative-adversarial-networks-gans In this course, you will: - Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity - Leverage the image-to-image translation framework and identify applications to modalities beyond images - Implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images into map routes (and vice versa) - Compare paired image-to-image translation to unpaired image-to-image translation and identify how their key difference necessitates different GAN a |
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Browser-based Models with TensorFlow.js (Coursera) https://www.coursera.org/learn/browser-based-models-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 first course, you’ll train and run machine learning models in any browser using TensorFlow.js. You’ll learn techniques for handling data in the browser, and at the end you’ll build a computer vision project that recognizes and classifies objects from a webcam. This |
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Build Basic Generative Adversarial Networks (GANs) (Coursera) https://www.coursera.org/learn/build-basic-generative-adversarial-networks-gans In this course, you will: - Learn about GANs and their applications - Understand the intuition behind the fundamental components of GANs - Explore and implement multiple GAN architectures - Build conditional GANs capable of generating examples from determined categories The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-unde |
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Build Better Generative Adversarial Networks (GANs) (Coursera) https://www.coursera.org/learn/build-better-generative-adversarial-networks-gans In this course, you will: - Assess the challenges of evaluating GANs and compare different generative models - Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs - Identify sources of bias and the ways to detect it in GANs - Learn and implement the techniques associated with the state-of-the-art StyleGANs The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting |
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Calculus for Machine Learning and Data Science (Coursera) https://www.coursera.org/learn/machine-learning-calculus After completing this course, learners will be able to: • Analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients • Approximately optimize different types of functions commonly used in machine learning using first-order (gradient descent) and second-order (Newton’s method) iterative methods • Visually interpret differentiation of different types of functions commonly used in machine learning • Perform gradient descent |
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Convolutional Neural Networks (Coursera) https://www.coursera.org/learn/convolutional-neural-networks In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more. By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply the |
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Convolutional Neural Networks in TensorFlow (Coursera) https://www.coursera.org/learn/convolutional-neural-networks-tensorflow 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 course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 2 of the deeplearning.ai TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will |
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Custom and Distributed Training with TensorFlow (Coursera) https://www.coursera.org/learn/custom-distributed-training-with-tensorflow In this course, you will: • Learn about Tensor objects, the fundamental building blocks of TensorFlow, understand the difference between the eager and graph modes in TensorFlow, and learn how to use a TensorFlow tool to calculate gradients. • Build your own custom training loops using GradientTape and TensorFlow Datasets to gain more flexibility and visibility with your model training. • Learn about the benefits of generating code that runs in graph mode, take a peek at what graph code looks li |
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Custom Models, Layers, and Loss Functions with TensorFlow (Coursera) https://www.coursera.org/learn/custom-models-layers-loss-functions-with-tensorflow In this course, you will: • Compare Functional and Sequential APIs, discover new models you can build with the Functional API, and build a model that produces multiple outputs including a Siamese network. • Build custom loss functions (including the contrastive loss function used in a Siamese network) in order to measure how well a model is doing and help your neural network learn from training data. • Build off of existing standard layers to create custom layers for your models, customize a n |
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Data Pipelines with TensorFlow Data Services (Coursera) https://www.coursera.org/learn/data-pipelines-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 third course, you will: - Perform streamlined ETL tasks using TensorFlow Data Services - Load different datasets and custom feature vectors using TensorFlow Hub and TensorFlow Data Services APIs - Create and use pre-built pipelines for generating highly reproducible |
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https://www.coursera.org/specializations/deep-learning Offered by DeepLearning.AI. Become a Machine Learning expert. Master the fundamentals of deep learning and break into AI. Recently updated ... |
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DeepLearning.AI TensorFlow Developer (CourseraSpecs) https://www.coursera.org/professional-certificates/tensorflow-in-practice Offered by DeepLearning.AI. |
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Deploying Machine Learning Models in Production (Coursera) https://www.coursera.org/learn/deploying-machine-learning-models-in-production 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. You will also implement workflow automation and progressive delivery that complies with current MLOps practices to keep your production system running. Additionally, you will continuously moni |
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Device-based Models with TensorFlow Lite (Coursera) https://www.coursera.org/learn/device-based-models-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. This second course teaches you how to run your machine learning models in mobile applications. You’ll learn how to prepare models for a lower-powered, battery-operated devices, then execute models on both Android and iOS platforms. Finally, you’ll explore how to deploy on e |
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Generative Adversarial Networks (GANs) (CourseraSpecs) https://www.coursera.org/specializations/generative-adversarial-networks-gans Offered by DeepLearning.AI. Break into the GANs space. Master cutting-edge GANs techniques through three hands-on courses! |
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Generative AI for Everyone (Coursera) https://www.coursera.org/learn/generative-ai-for-everyone Instructed by AI pioneer Andrew Ng, Generative AI for Everyone offers his unique perspective on empowering you and your work with generative AI. Andrew will guide you through how generative AI works and what it can (and can’t) do. It includes hands-on exercises where you'll learn to use generative AI to help in day-to-day work and receive tips on effective prompt engineering, as well as learning how to go beyond prompting for more advanced uses of AI. You’ll get insights into what generative AI |
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Generative AI with Large Language Models (Coursera) https://www.coursera.org/learn/generative-ai-with-llms In Generative AI with Large Language Models (LLMs), you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications. By taking this course, you'll learn to: - Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection, to performance evaluation and deployment - Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning e |
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Generative Deep Learning with TensorFlow (Coursera) https://www.coursera.org/learn/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. b) Build simple AutoEncoders on the familiar MNIST dataset, and more complex deep and convolutional architectures on the Fashion MNIST dataset, understand the difference in results of the DNN and CNN AutoEncoder models, identify ways to de-noise noisy images, a |
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https://www.coursera.org/learn/ai-for-everyone-es La IA no es solo para ingenieros. Si desea que su organización esté mejor preparada en el uso de la IA, este es el curso que todos deberían hacer, especialmente sus colegas no técnicos. En este curso, aprenderá lo siguiente: - El significado detrás de la terminología común de IA, incluidos términos como redes neuronales, aprendizaje automático, aprendizaje profundo y ciencia de datos - Lo que la IA puede realmente hacer, y lo que no - Cómo detectar oportunidades para aplicar la IA a los proble |
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Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization (Coursera) https://www.coursera.org/learn/deep-neural-network 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. By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient |
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Introduction to Machine Learning in Production (Coursera) https://www.coursera.org/learn/introduction-to-machine-learning-in-production In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application. Understanding machine learning and deep learning con |
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https://www.coursera.org/learn/introduction-tensorflow 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 course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learni |
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Linear Algebra for Machine Learning and Data Science (Coursera) https://www.coursera.org/learn/machine-learning-linear-algebra After completing this course, learners will be able to: • Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence, etc. • Apply common vector and matrix algebra operations like dot product, inverse, and determinants • Express certain types of matrix operations as linear transformations • Apply concepts of eigenvalues and eigenvectors to machine learning problems Mathematics for Machine Learning and Data science is a fo |
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https://www.coursera.org/learn/ai-for-everyone-fr L’IA n’est pas l’apanage des ingénieurs. Si vous souhaitez améliorer les capacités de votre organisation à utiliser de l’IA, vous devriez recommander ce cours à tout votre personnel, en particulier ceux opérant dans des départements non techniques. Ce cours vous fera découvrir : - La signification de la terminologie courante de l’IA, y compris les réseaux neuronaux, l’apprentissage automatique, l’apprentissage en profondeur et la science des données - Ce que l’IA peut réellement faire (et ses |
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Machine Learning Data Lifecycle in Production (Coursera) https://www.coursera.org/learn/machine-learning-data-lifecycle-in-production In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas. Understanding machine le |
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Machine Learning Engineering for Production (MLOps) (CourseraSpecs) https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops Offered by DeepLearning.AI. Become a Machine Learning expert. Productionize your machine learning knowledge and expand your production ... |
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Machine Learning Modeling Pipelines in Production (Coursera) https://www.coursera.org/learn/machine-learning-modeling-pipelines-in-production In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build |
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Mathematics for Machine Learning and Data Science (CourseraSpecs) https://www.coursera.org/specializations/mathematics-for-machine-learning-and-data-science Offered by DeepLearning.AI. Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a ... |
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Natural Language Processing (CourseraSpecs) https://www.coursera.org/specializations/natural-language-processing Offered by DeepLearning.AI. Break into NLP. Master cutting-edge NLP techniques through four hands-on courses! Updated with the latest ... |
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Natural Language Processing in TensorFlow (Coursera) https://www.coursera.org/learn/natural-language-processing-tensorflow 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 Course 3 of the deeplearning.ai TensorFlow Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that |
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Natural Language Processing with Attention Models (Coursera) https://www.coursera.org/learn/attention-models-in-nlp In Course 4 of the Natural Language Processing Specialization, you will: a) Translate complete English sentences into German using an encoder-decoder attention model, b) Build a Transformer model to summarize text, c) Use T5 and BERT models to perform question-answering, and d) Build a chatbot using a Reformer model. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summ |
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Natural Language Processing with Classification and Vector Spaces (Coursera) https://www.coursera.org/learn/classification-vector-spaces-in-nlp In Course 1 of the Natural Language Processing Specialization, you will: a) Perform sentiment analysis of tweets using logistic regression and then naïve Bayes, b) Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize those relationships, and c) Write a simple English to French translation algorithm using pre-computed word embeddings and locality-sensitive hashing to relate words via approximate k-nearest n |
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Natural Language Processing with Probabilistic Models (Coursera) https://www.coursera.org/learn/probabilistic-models-in-nlp In Course 2 of the Natural Language Processing Specialization, you will: a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming, b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is vital for computational linguistics, c) Write a better auto-complete algorithm using an N-gram language model, and d) Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model. By the end of |
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Natural Language Processing with Sequence Models (Coursera) https://www.coursera.org/learn/sequence-models-in-nlp In Course 3 of the Natural Language Processing Specialization, you will: a) Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets, b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model, c) Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and d) Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have |
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Neural Networks and Deep Learning (Coursera) https://www.coursera.org/learn/neural-networks-deep-learning In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning. By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications. The Deep Learning Specializati |
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Probability & Statistics for Machine Learning & Data Science (Coursera) https://www.coursera.org/learn/machine-learning-probability-and-statistics Mathematics for Machine Learning and Data science is a foundational online program created in by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning. After completing this course, learners will be able to: • Describe and quantify the uncertainty inherent in predictions made by machine learning models, using the concepts of probability, random variables, and probability distributions. • Visua |
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Réseaux neuronaux et Deep Learning (Coursera) https://www.coursera.org/learn/neural-networks-deep-learning-fr Vous souhaitez vous lancer dans l’IA de pointe ? Ce cours est là pour vous y aider. Les ingénieurs en Deep Learning sont très convoités et la maîtrise de ce domaine vous ouvrira de nombreuses opportunités professionnelles. Le Deep Learning est également un nouveau « superpouvoir » qui vous permettra de développer des systèmes d’IA qui n’étaient même pas envisageables il y a encore quelques années. Vous découvrirez dans ce cours les bases du Deep Learning. Une fois que vous l’aurez terminé, vous |
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https://www.coursera.org/learn/nlp-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. By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Emb |
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Sequences, Time Series and Prediction (Coursera) https://www.coursera.org/learn/tensorflow-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. Finall |