機械学習（ML）モデルの精度を高める方法や、最も有用な特徴量を作成するためのデータ列の見極め方を学びたい方におすすめのコースです。この Feature Engineering コースでは、「良い」特徴量と「悪い」特徴量について説明し、それらをモデルで最大限に活用できるように前処理して変換する方法を解説します。
This course is part of the Machine Learning with TensorFlow Google Cloud 日本語版 Specialization
Feature Engineering 日本語版
About this Course
Skills you will gain
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
About the Machine Learning with TensorFlow Google Cloud 日本語版 Specialization
問題を設定し、勾配降下を使用して適切な解決策を見つけ、データセットを作成する方法について学びます。また、Tensorflow でスケーラブルな分散型機械学習モデルを作成して、これらのモデルのトレーニングを拡張し、高性能な予測ができるようになります。さらに、機械学習（ML）がデータから重要な特質を学習したり、人間による分析を問題に取り入れるように、生データを変換します。最後に、正確で一般化されたモデルを生成し、特定の ML 問題を解決する理論について、および適切なパラメータの組み合わせ方を学びます。まず ML 集中型の戦略の構築から始め、その後 Google Cloud Platform のハンズオンラボを通じてモデルのトレーニング、最適化、本稼働まで、ML に関する手順全体を実習します。
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Last Updated: September 1, 2015
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