Launching into Machine Learning 日本語版

Google Cloud via Coursera

Go to Course: https://www.coursera.org/learn/launching-machine-learning-jp

Introduction

**Course Review and Recommendation: Launching into Machine Learning 日本語版** If you're interested in exploring the expansive world of Machine Learning (ML) without diving deep into extensive coding, then "Launching into Machine Learning 日本語版," available on Coursera, is a course worthy of your attention. Here's an in-depth look at the course structure, its content, and why I recommend it for both beginners and those looking to enhance their ML skills. ### Overview The course kicks off with a thorough discussion on data quality and how to conduct exploratory data analysis. It emphasizes the significance of clean, well-structured data as a foundational element for any successful ML model. Notably, participants will gain insights into Vertex AI AutoML and learn how to construct, train, and deploy ML models without needing to write a single line of code. Additionally, the course delves into the advantages of BigQuery ML and offers strategies for optimizing ML models, contextualizing generalization and sampling in the process. ### Course Syllabus Highlights 1. **Introduction** - The course begins with an introduction that lays out the agenda and learning objectives. This module is essential as it sets the stage for what students can expect to achieve. 2. **Understanding Data: Improving Data Quality through Exploratory Data Analysis** - In this module, you will learn techniques to enhance data quality via exploratory data analysis (EDA). This section underscores the importance of organizing data and how issues like missing values can skew results. Through hands-on activities, learners will perform EDA on datasets, making concepts tangible. 3. **Practical Machine Learning** - This module introduces various types of ML, encouraging learners to identify which types might suit their preferences or roles. It is a great way to familiarize oneself with the landscape of ML applications and techniques. 4. **Training AutoML Models Using Vertex AI** - You’ll explore how to train AutoML models using Vertex AI, empowering participants to leverage powerful tools without the burden of code. This section is particularly useful for those unfamiliar with programming but keen to implement ML solutions. 5. **Machine Learning on BigQuery** - Here, learners are introduced to BigQuery ML and its capabilities. This module showcases how to effectively combine data storage and ML querying, making data scientists’ workflows much smoother. 6. **Optimization** - The focus shifts to optimizing ML models, a crucial aspect for ensuring that your models perform well in real-world scenarios. The insights gained here can greatly influence project outcomes, making this module a must for aspiring ML practitioners. 7. **Generalization and Sampling** - This advanced topic tackles when the most accurate ML model may not be the best choice. It discusses the importance of creating reproducible training, evaluation, and test datasets, and establishing performance benchmarks. Students will learn about potential pitfalls in model selection and the significance of generalization. ### Recommendation "Launching into Machine Learning 日本語版" is an expertly designed course that caters to individuals looking to understand the nuances of machine learning, specifically in a Japanese context. The course structure is logical and builds progressively, ensuring that even those with minimal prior knowledge can grasp key concepts. The hands-on approach, particularly through EDA and the use of tools like Vertex AI and BigQuery ML, allows for practical application of learned skills, thereby enhancing retention. Moreover, the course is ideal for those seeking to transition into ML roles without extensive programming backgrounds, as it offers a code-free path to model building. Whether you are a student, a working professional, or a tech enthusiast, I highly recommend enrolling in this course. The skills and knowledge you gain will be invaluable in today’s data-driven landscape, positioning you well for future opportunities in machine learning and data science.

Syllabus

はじめに

このモジュールでは、コースの概要とその目標を説明します。

データについて知る: 探索的データ分析によるデータ品質の改善

このモジュールでは、探索的データ分析を実行することで、データの品質を向上させる方法と、データを探索する方法について紹介します。機械学習におけるデータ整理の重要性について説明し、データ品質にどのように影響するかを説明します。たとえば、欠損値があると結果に歪みが生じる可能性があります。また、データ探索の重要性についても学びます。データを整理したら、データセットに対して探索的データ分析を実行しましょう。

機械学習の実践

このモジュールでは、ML 担当者としての成長を加速できるように、機械学習の主要なタイプを紹介します。

Vertex AI を使用した AutoML モデルのトレーニング

このモジュールでは、Vertex AI を使用した AutoML モデルのトレーニングについて紹介します。

BigQuery の機械学習: データが存在する ML モデルの開発

このモジュールでは、BigQuery ML とその機能を紹介します。

最適化

このモジュールでは、ML モデルを最適化する方法を説明します。

一般化とサンプリング

次に、かなり変わった質問にお答えいただきます。最も正確な ML モデルが適切な選択モデルではないのは、どのような場合ですか？ 最適化に関する最後のモジュールで示唆したとおり、モデルのトレーニング データセットの損失指標が 0 だからといって、実際の環境の新しいデータでうまく機能するわけではありません。再現可能なトレーニング、評価、テスト データセットを作成し、パフォーマンス ベンチマークを確立する方法を学習します。