June 8, 2026
Build Your First ML Dataset From Scratch — Live Walkthrough | Master AI & ML Ep 10
This is the Module 2 capstone — a live, end-to-end walkthrough of building your first ML dataset from scratch. We take a real problem, find public data, audit it, clean it, check for bias, split it correctly, and document it for handoff. Build along using the Telco Churn dataset linked below.
In this episode:
→ Why you start with the problem, not the data
→ Where to find public datasets — Kaggle, UCI, and Hugging Face
→ First audit — shape, types, missing values, class imbalance
→ Cleaning live — missing values, type fixes, one-hot encoding
→ Bias check — tenure as an age proxy, SeniorCitizen churn disparity
→ Stratified train/validation/test split — why it matters for imbalanced data
→ Writing the dataset documentation card — the 15-minute artifact that saves hours
This is Episode 10 of Master AI & Machine Learning — the Module 2 closer.
──────────────────────────────
⬅ Ep 09 — Bias in Data
➡ Ep 11 — Linear Regression (Module 3)
🌐 TechnovativeAI → www.technovativeai.com
──────────────────────────────
⏱ TIMESTAMPS
00:00 — Hook: from zero to dataset, live
00:30 — Step 1: define the problem first
01:30 — Step 2: find and download the data
02:30 — Step 3: first audit
04:00 — Steps 4 & 5: clean and check for bias
06:30 — Step 6: stratified train/val/test split
07:45 — Step 7: dataset documentation card
09:00 — Module 2 wrap & Module 3 preview
──────────────────────────────
Series of Thoughts · Presented by TechnovativeAI
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#MachineLearning #DataScience #BuildDataset #MLproject #KaggleDataset #DataCleaning #DataBias #LearnAI #TechnovativeAI #SeriesOfThoughts
In this episode:
→ Why you start with the problem, not the data
→ Where to find public datasets — Kaggle, UCI, and Hugging Face
→ First audit — shape, types, missing values, class imbalance
→ Cleaning live — missing values, type fixes, one-hot encoding
→ Bias check — tenure as an age proxy, SeniorCitizen churn disparity
→ Stratified train/validation/test split — why it matters for imbalanced data
→ Writing the dataset documentation card — the 15-minute artifact that saves hours
This is Episode 10 of Master AI & Machine Learning — the Module 2 closer.
──────────────────────────────
⬅ Ep 09 — Bias in Data
➡ Ep 11 — Linear Regression (Module 3)
🌐 TechnovativeAI → www.technovativeai.com
──────────────────────────────
⏱ TIMESTAMPS
00:00 — Hook: from zero to dataset, live
00:30 — Step 1: define the problem first
01:30 — Step 2: find and download the data
02:30 — Step 3: first audit
04:00 — Steps 4 & 5: clean and check for bias
06:30 — Step 6: stratified train/val/test split
07:45 — Step 7: dataset documentation card
09:00 — Module 2 wrap & Module 3 preview
──────────────────────────────
Series of Thoughts · Presented by TechnovativeAI
how to build a dataset machine learning, first ML dataset, Kaggle tutorial beginners, telco churn dataset, dataset documentation ML, class imbalance machine learning, stratified split sklearn, one hot encoding tutorial, ML dataset from scratch, data science project tutorial, Kaggle dataset download, UCI machine learning repository, Hugging Face datasets, ML project walkthrough, feature engineering tutorial, TechnovativeAI, Series of Thoughts, learn AI, machine learning beginner project, data preparation ML project
#MachineLearning #DataScience #BuildDataset #MLproject #KaggleDataset #DataCleaning #DataBias #LearnAI #TechnovativeAI #SeriesOfThoughts