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  1. Learn machine learning fundamentals with Google's fast-paced, practical introduction, featuring video lectures, case studies, and exercises. Explore key concepts, best practices, and deep neural networks with TensorFlow APIs.

  2. Learn how to get started, practice, and improve your machine learning skills with these comprehensive tutorials. Covering foundations, algorithms, tools, datasets, and applications for beginners and beyond.

  3. 25 de ene. de 2024 · Learn the basics and advanced concepts of machine learning, a subdomain of artificial intelligence that focuses on developing systems that learn from data. Explore various techniques, such as supervised, unsupervised, and reinforcement learning, and applications, such as natural language processing and neural networks.

  4. Learn how to complete a machine learning project end-to-end using Python and SciPy. This tutorial covers data loading, summarizing, visualizing, evaluating and making predictions with the iris dataset.

  5. To become an expert in machine learning, you first need a strong foundation in four learning areas: coding, math, ML theory, and how to build your own ML project from start to finish. Begin with TensorFlow's curated curriculums to improve these four skills, or choose your own learning path by exploring our resource library below.

  6. 16 de nov. de 2023 · Build a neural network machine learning model that classifies images. Train this neural network. Evaluate the accuracy of the model. This tutorial is a Google Colaboratory notebook. Python programs are run directly in the browser—a great way to learn and use TensorFlow.

  7. Machine learning is about learning some properties of a data set and then testing those properties against another data set. A common practice in machine learning is to evaluate an algorithm by splitting a data set into two.