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Build production-ready data science projects with reproducible machine learning pipelines and a hybrid recommendation system.
Learn to build, deploy, and operationalize machine learning models on Amazon SageMaker, using AWS Lambda and Step Functions to automate scalable workflows.
Build supervised models with linear regression, decision trees, Naive Bayes and SVMs, apply K-means and Gaussian mixtures, and train a TensorFlow neural network image classifier.
Build supervised, unsupervised, and deep learning models with PyTorch: fit classification and regression models, cluster with K-means and Gaussian mixtures, and train an image classifier.
In this course, you'll learn about different types of supervised learning and how to use them to solve real-world problems.
Eight short videos unpack Kaggle’s introductory machine learning lessons, from model validation and overfitting to random forests, competitions, and AutoML.
Explore deep learning through convolutional models, transfer learning, data augmentation, TensorFlow, Keras, and TPU acceleration.
Explore TPU architecture, systolic arrays, bfloat16, TFRecords, and practical techniques for troubleshooting and optimizing deep-learning workloads.
In this series of videos, Kaggle Data Scientist, Rachael, will walk you through how to use SQL.
Learn Dashboarding with Notebooks with Kaggle! In this course, you will learn about what information to include, best practices for making dashboards, running notebooks with Kaggle API, scheduling notebook runs using cloud services, and testing and valid…
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