Phases of a Machine Learning Project | End-to-End Machine Learning Workflow Explained

Every successful AI model begins with a structured Machine Learning project lifecycle. 🚀

In this video, we’ll break down all the Phases of a Machine Learning Project — from problem definition to deployment and monitoring. Whether you’re a beginner or a professional, this guide will help you understand how ML systems are built in the real world.

You’ll learn:
1️⃣ Defining the Problem and Business Objective
2️⃣ Data Collection and Understanding
3️⃣ Data Cleaning and Preprocessing
4️⃣ Feature Engineering and Selection
5️⃣ Model Selection and Training
6️⃣ Model Evaluation and Optimization
7️⃣ Deployment and Monitoring
8️⃣ Feedback Loop and Continuous Improvement

💡 You’ll also learn industry best practices used by data scientists and ML engineers at AWS, Google, and Microsoft AI teams.

By the end of this video, you’ll have a clear roadmap to execute your own ML projects confidently — step by step.

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