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Gain practical experience in data pipelines, experiments, recommendation systems, and more. Develop essential skills in software engineering, data engineering, and experimental design for a successful data science career.
Master foundational ML skills, from supervised learning to deep learning. Deploy models to production environments and complete a capstone project in your chosen domain.
Learn supervised and unsupervised machine learning techniques using TensorFlow and Python. Master regression, neural networks, clustering, and more to solve real-world problems and build an image classifier.
Dive into machine learning fundamentals with engaging videos covering models, data exploration, validation, and advanced concepts like random forests and AutoML.
Comprehensive introduction to machine learning using PyTorch, covering supervised and unsupervised techniques with hands-on projects for real-world problem-solving and model building.
Explore deep learning fundamentals, computer vision, TensorFlow, Keras, transfer learning, and advanced topics like TPUs in this comprehensive introduction to neural networks.
Explore Tensor Processing Units (TPUs) for accelerated deep learning, from basics to advanced optimization techniques and real-world applications.
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…
In this course, you'll learn about different types of supervised learning and how to use them to solve real-world problems.
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