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In this video you will learn what is Data Preparation in Machine Learning Lifecycle. Data Preparation is an important process in Data Science projects since the raw data received from Data Sources may not be in perfect format. It needs to be cleaned and enriched and transformed. We discuss about what are the basic issue usually we have in raw data and how it should be cleaned and enriched. 🔴 Related Videos about Data: ► How to build Data Pipeline on AWS?: • How To Create Data Pipeline on AWS? #aws #... ► Spark Streaming Vs Kafka Streams: • Spark Streaming Vs Kafka Streams || Which ... ► Data Preparation in Data Science or Machine Learning: • Data Preparation in Machine Learning #data... #datapreparation #dataengineering #techtter 📌 Videos related to Kubernetes: ► Kubernetes Basics Explained: • Kubernetes Basics Explained in 10 Minutes ... ► What is Terraform: • AWS EKS Terraform [Ep1]: Create Kubernetes... ► Provisioning EKS cluster on AWS using Terraform: • Видео ► Kubernetes(EKS) Cluster Autoscaling & Horizontal Pod Autoscaling on AWS: • Видео ► Jenkins installation on Kubernetes / AWS using Terraform: • How To Install Jenkins on AWS EC2 with Ter... ► Octant - Kubernetes Dashboard UI: • Octant: GUI for Kubernetes Cluster [ DevOps ] ► KinD - Kubernetes in Docker for K8s cluster on laptop: • KinD - Create Kubernetes Cluster (Multi-No... ► Kuberenetes Cheat Sheet: • Kubernetes Cheat Sheet for DevOps #DevOps ... 📌 Docker related Videos: ► Docker Compose Explained with Spring Boot: • Docker Compose Explained with 3 Microservi... ► Docker Volumes Explained with Postgresql: • Docker Volumes Explained with Spring Boot ... ---------------------------------------------------------------------------------------------- ✅ Subscribe To Channel on Social Media: YOUTUBE: ► https://www.youtube.com/Techtter?sub_... FACEBOOK: ► / techtter TWITTER: ► / thetechtter INSTAGRAM: ► / techtter DEV: ► https://dev.to/techtter WEB: ► https://www.techtter.net ----------------------------------------------------------------------------------------------- 🔥 Disclaimer/Policy: The content of the videos, ideas given in videos all are self opinionated. The code samples shown in the video are collected from opensource projects shared on github and from other sources. The trademarks and logos shown on thumbnails and in videos are registered for respective companies. This video is not certified or accredited by them.