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Our Courses
Machine Learning: Concepts and Applications
This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques.
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Course by
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Self Paced
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38 hours
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English
Support Vector Machines in Python, From Start to Finish
In this lesson we will built this Support Vector Machine for classification using scikit-learn and the Radial Basis Function (RBF) Kernel. Our training data set contains continuous and categorical data from the UCI Machine Learning Repository to predict whether or not a patient has heart disease. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project.
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Course by
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Self Paced
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2 hours
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English
TensorFlow on Google Cloud - 한국어
이 과정에서는 TensorFlow 및 Keras를 사용한 ML 모델 빌드, ML 모델의 정확성 개선, 사용 사례 확장을 위한 ML 모델 작성에 대해 다룹니다.
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Course by
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Self Paced
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English
Predict Baby Weight with TensorFlow on AI Platform
In this lab you train, evaluate, and deploy a machine learning model to predict a baby’s weight. You then send requests to the model to make online predictions. This lab is part of a series of labs on processing scientific data.
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Course by
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Self Paced
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2 hours
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English
TensorFlow Serving with Docker for Model Deployment
This is a hands-on, guided project on deploying deep learning models using TensorFlow Serving with Docker.
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Course by
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Self Paced
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3 hours
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English
Regression with Automatic Differentiation in TensorFlow
In this 1.5 hour long project-based course, you will learn about constants and variables in TensorFlow, you will learn how to use automatic differentiation, and you will apply automatic differentiation to solve a linear regression problem.
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Course by
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Self Paced
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2 hours
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English
Advanced Computer Vision with TensorFlow
In this course, you will: a) Explore image classification, image segmentation, object localization, and object detection.
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Course by
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19 hours
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English
Interpretable machine learning applications: Part 3
In this 50 minutes long project-based course, you will learn how to apply a specific explanation technique and algorithm for predictions (classifications) being made by inherently complex machine learning models such as artificial neural networks. The explanation technique and algorithm is based on the retrieval of similar cases with those individuals for which we wish to provide explanations.
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Course by
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Self Paced
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3 hours
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English
ML Pipelines on Google Cloud
In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX.
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Course by
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Self Paced
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11 hours
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English
Basic Image Classification with TensorFlow
In this 2-hour long project-based course, you will learn the basics of using Keras with TensorFlow as its backend and use it to solve a basic image classification problem. By the end of this project, you will have created, trained, and evaluated a Neural Network model that will be able to predict digits from hand-written images with a high degree of accuracy. You also will have learned the fundamentals of neural networks, TensorFlow, and Keras. Note: This course works best for learners who are based in the North America region.
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Course by
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Self Paced
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2 hours
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English
Distributed Multi-worker TensorFlow Training on Kubernetes
This is a self-paced lab that takes place in the Google Cloud console. In this hands-on lab you will explore using Google Cloud Kubernetes Engine and Kubeflow TFJob to scale out TensorFlow distributed training.…
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Course by
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Self Paced
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English
Machine Learning Capstone
This Machine Learning Capstone course uses various Python-based machine learning libraries, such as Pandas, sci-kit-learn, and Tensorflow/Keras. You will also learn to apply your machine-learning skills and demonstrate your proficiency in them. Before taking this course, you must complete all the previous courses in the IBM Machine Learning Professional Certificate. In this course, you will also learn to build a course recommender system, analyze course-related datasets, calculate cosine similarity, and create a similarity matrix.
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Course by
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Self Paced
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19 hours
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English
Running Distributed TensorFlow using Vertex AI
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will use TensorFlow's distribution strategies and the Vertex AI platform to train and deploy a custom TensorFlow image classification model to classify an image classification dataset.
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Course by
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Self Paced
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2 hours
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English
Avoid Overfitting Using Regularization in TensorFlow
In this 2-hour long project-based course, you will learn the basics of using weight regularization and dropout regularization to reduce over-fitting in an image classification problem.
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Course by
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Self Paced
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2 hours
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English
Encoder-Decoder Architecture
This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.
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Course by
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Self Paced
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1 hour
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English
MNIST Fashion Datensatz mit Tensorflow
MNIST Fashion Datensatz mit Tensorflow programmieren.
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Course by
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Self Paced
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1 hour
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German
Feature Engineering em Português Brasileiro
O curso apresenta os benefícios de usar a Vertex AI Feature Store e ensina a melhorar a acurácia dos modelos de ML e a identificar as colunas de dados que apresentam os atributos mais úteis. Ele também oferece conteúdo teórico e laboratórios sobre engenharia de atributos com BigQuery ML, Keras e TensorFlow.
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Course by
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Self Paced
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Portuguese
Intro to TensorFlow en Español
Este curso se enfoca en aprovechar la flexibilidad y facilidad de uso de TensorFlow 2.x y Keras para compilar, entrenar e implementar modelos de aprendizaje automático. Aprenderá sobre la jerarquía de la API de TensorFlow 2.x y conocerá los componentes principales de TensorFlow mediante ejercicios prácticos. Le mostraremos cómo trabajar con conjuntos de datos y columnas de atributos. Aprenderá a diseñar y compilar una canalización de datos de entrada de TensorFlow 2.x.
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Course by
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Self Paced
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Spanish
How Google does Machine Learning em Português Brasileiro
"Quais são as práticas recomendadas para implementar machine learning no Google Cloud? O que é Vertex AI e como é possível usar a plataforma para criar, treinar e implantar modelos de machine learning do AutoML com rapidez e sem escrever nenhuma linha de código? O que é machine learning e que tipos de problema ele pode resolver? O Google pensa em machine learning de uma forma um pouco diferente.
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Self Paced
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Portuguese
AI、機械学習、ディープラーニングのための TensorFlow 入門
ソフトウェア開発者であれば、拡張性のあるAI搭載アルゴリズムを構築したい場合、構築ツールの使い方を理解する必要があります。この講座は今後学んでいく「TensorFlow in Practice 専門講座」の一部であり、機械学習用の人気のオープンソースフレームワークであるTensorFlowのベストプラクティスを学習します。 アンドリュー・エンの「 The Machine Learning（機械学習）」と「Deep Learning Specialization（ディープラーニング専門講座）」では、機械学習とディープラーニングの最も重要かつ基本的な原理を学習します。deeplearning.aiが提供する新しい「TensorFlow in Practice 専門講座」では、TensorFlowを使用してそれらの原理を実装し、拡張性のあるモデルを構築して現実世界の問題に適用する方法を学びます。ニューラルネットワークの仕組みについての理解を深めるには、「ディープラーニング専門講座」を受講することをお勧めします。
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Course by
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Self Paced
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Japanese
Feature Engineering en Español
En este curso, se exploran los beneficios de utilizar Vertex AI Feature Store, cómo mejorar la exactitud de los modelos de AA y cómo descubrir cuáles columnas de datos producen los atributos más útiles. El curso también incluye contenido y labs sobre la ingeniería de atributos en los que se usan BigQuery ML, Keras y TensorFlow.
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Course by
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Self Paced
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Spanish
Intro to TensorFlow 日本語版
このコースの目的は、柔軟で手軽な TensorFlow 2.x と Keras を使用して、機械学習モデルを作成、トレーニング、およびデプロイすることです。TensorFlow 2.x API の階層について学び、TensorFlow の主要コンポーネントを実践演習で理解します。データセットと特徴列の扱い方について学びます。TensorFlow 2.x 入力データ パイプラインの設計と作成の方法について学びます。tf.data.Dataset を使用して csv データ、NumPy 配列、テキストデータ、および画像を読み込む実践演習を行います。数値、カテゴリ、バケット、およびハッシュの特徴列を作成する実践演習も行います。 Keras Sequential API と Keras Functional API を使用してディープ ラーニング モデルを作成する方法を学びます。活性化関数、損失、および最適化について学びます。Jupyter ノートブックの実践演習では、基本的な線形回帰、基本的なロジスティック回帰、および高度なロジスティック回帰の機械学習モデルを作成できます。Cloud AI Platform での大規模な機械学習モデルのトレーニング、デプロイ、および本稼働の方法について学びます。
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Course by
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Self Paced
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Japanese
Art and Science of Machine Learning en Español
Le damos la bienvenida a The Art and Science of Machine Learning. El curso consta de 6 módulos. En este curso, se abordan las habilidades básicas de intuición, buen criterio y experimentación del AA necesarias para ajustar mejor y optimizar modelos de AA a fin de lograr el mejor rendimiento. Aprenderá a generalizar su modelo usando técnicas de regularización y descubrirá los efectos de los hiperparámetros, como el tamaño del lote y la tasa de aprendizaje, en el rendimiento del modelo.
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Course by
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Self Paced
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Spanish
ML Pipelines on Google Cloud - 한국어
이 과정에서는 Google Cloud에서 최신 ML 파이프라인 개발을 담당하는 ML 엔지니어와 트레이너로부터 유익한 지식을 배웁니다. 초반에 진행되는 몇 개 모듈에서는 Google의 TensorFlow 기반 프로덕션 머신러닝 플랫폼으로서 ML 파이프라인과 메타데이터를 관리할 수 있는 TensorFlow Extended(TFX)에 대해 다룹니다. 파이프라인 구성요소와 TFX를 사용한 파이프라인 조정을 알아봅니다. 지속적 통합과 지속적 배포를 통해 파이프라인을 자동화하는 방법과 ML 메타데이터를 관리하는 방법도 배웁니다. 그런 다음 주제를 전환하여 TensorFlow, PyTorch, scikit-learn, xgboost 등 여러 ML 프레임워크에서 ML 파이프라인을 자동화하고 재사용하는 방법을 설명합니다. 또한 Google Cloud의 또 다른 도구인 Cloud Composer를 사용하여 지속적 학습 파이프라인을 조정하는 방법도 알아봅니다. 마지막으로 MLflow를 사용하여 머신러닝의 전체 수명 주기를 관리하는 방법을 살펴봅니다.
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Course by
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Self Paced
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Korean
NLP Modelos y Algoritmos
Este curso te brindará los conocimientos necesarios para la implementación de algoritmos de NLP. Mediante el uso de los últimos algoritmos más populares en NLP se procederá a dar solución a un conjunto de problemas propios del área. Para realizar este curso es necesario contar con conocimientos de programación de nivel básico a medio, deseablemente conocimiento básico del lenguaje Python y es recomendable conocer los Jupyter Notebooks en el entorno Anaconda. Para desarrollar aplicaciones se va a utilizar Python 3.6 o superior.
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Course by
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Self Paced
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11 hours
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Spanish