GCP AI Fundamentals - AIML Series 2 - EDA and ML Models
Introduction Machine learning (ML) has become an integral part of modern technology, driving innovations across various sectors. Google Cloud Platform (GCP) offers robust tools and services to harness the power of ML efficiently. In this post, we'll explore key concepts in GCP ML, including Exploratory Data Analysis (EDA), data visualization, supervised learning, AutoML, BigQueryML, recommendation systems, optimization, and performance metrics. Exploratory Data Analysis (EDA) Process Description and Role in ML: Exploratory Data Analysis (EDA) is a crucial step in the ML pipeline, helping to understand the data and uncover underlying patterns. It involves summarizing the main characteristics of the data, often with visual methods, and is essential for validating assumptions, detecting anomalies, and making informed decisions about data preprocessing and model selection. Key Steps in EDA: Initial Inspection : Use functions like head() , tail() , and info() to get an overv