AIML BOOTCAMP | Episode 8 — Data Collection, Preprocessing & Feature Engineering for AI/ML
Most machine learning models fail not because of bad algorithms — but because of bad data. This episode covers everything you need to know about acquiring, understanding, and preparing data before a single line of model training code is written.
If you are serious about building AI and Machine Learning systems that actually work in the real world, this is the foundation you cannot skip.
WHAT YOU WILL LEARN IN THIS VIDEO
This episode of AIML Bootcamp walks you through the complete data pipeline — from raw collection to model-ready features. By the end, you will understand not just what to do with data, but why every step matters for model accuracy, performance, and scalability.
Topics covered in this video:
-How data is collected in real-world AI systems - APIs, relational databases, IoT devices, and web-based sources
-The difference between SQL, NoSQL, Cloud, and Vector databases - and when to use each
-What vector databases are and how modern AI models like LLMs use them for similarity search and retrieval
-Common data formats - CSV, JSON, and Parquet - and their practical use cases in data science pipelines
-Data preprocessing techniques - handling missing values, removing duplicates, normalizing and standardizing numerical data
-Categorical encoding strategies - label encoding vs one-hot encoding
Feature engineering - how to extract meaningful signals from raw data to improve model performance
-Dimensionality reduction - PCA and why reducing feature space can increase both speed and accuracy
-A preview of what comes next - ML algorithms, model training, and evaluation
WHY THIS MATTERS FOR YOUR AI/ML CAREER
Every data scientist, ML engineer, and AI researcher spends the majority of their working time on data - not on models. In industry, poor data quality is the number one reason machine learning projects fail. Understanding how to collect, store, clean, and engineer features is not optional background knowledge — it is the core skill that separates professionals from beginners.
Vector databases, in particular, are now central to how modern AI applications like ChatGPT plugins, recommendation systems, and semantic search are built. This video gives you a practical understanding of that ecosystem before you ever touch a model.|
ABOUT AIML BOOTCAMP
AIML Bootcamp is a structured, beginner-to-advanced series covering Artificial Intelligence, Data Science, and Machine Learning from the ground up. Each episode builds directly on the last, with clear explanations, real-world context, and practical examples.
Previous episode: • Matplotlib for Machine Learning | Data Vis...
Next episode: [Supervised Learning Algorithms - Linear Regression, Decision Trees, and Model Training]
Whether you are a student, a developer switching careers, or someone exploring AI for the first time — this series is built for you.
TAKE ACTION
If this video helped you, subscribe and turn on notifications so you never miss an episode. Every video in this series builds on the previous one — you do not want to fall behind.
Leave a comment below with your biggest question about data preprocessing or vector databases. Questions from this section directly shape future videos.
Share this with a friend who is learning AI/ML. The best way to learn is to learn together.
RESOURCES AND LINKS
GitHub Repository (code + notebooks):https://github.com/dhruv-15-03/AI-ML
Connect on LinkedIn: / dhruv-rastogi-3b744032b
Full Bootcamp Playlist: • AI/ML Course From Scratch
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