Predicting House Prices(Real Estate) is one of the most practical and popular applications of machine learning. Predicting House prices is a classic machine learning problem that combines data analysis, feature engineering, and regression modeling. In this step by step guide, we will build a price prediction model using a real estate dataset in Pthon. In […]
Machine Learning Model for Real Estate Price Prediction Read More »
Choose the correct answer:- Which of the following HTML form control is not created using <INPUT> tag? Ans- d-select list Which of the following controls can be set to one of the two positions only? Ans- C – Both a and b In a list of cities, the city Mumbai was already selected in an
Q1. What is AI? AI stands for Artificial Intelligence.Artificial Intelligence is a technology that mimics human behavior(acts like a human). For example— Humanoid Robots,Alexa, Siri, etc. Q2 What is DATA? Data is the smallest unit of any information. In other words, Data is raw facts and figures.For example: Roll-18, name-Aakash, Std -7 Q3. What is
Here is a recap and cheat sheet to wrap up Generative AI in Data Science blog series – a one-stop summary for readers who want to review, revisit, or start from the top. Over the past 10 parts, we explored how generative AI is reshaping the way data scientists build, refine, and scale machine learning
Wrap-Up: Generative AI in Data Science Series Recap & Practical Cheat Sheet Read More »
Here is Part 10 of the Generative AI in Data Science blog series. This post goes model-specific. Now that you know how to generate, benchmark, and tune synthetic data, it’s time to align that data with the specific needs of different model architectures. Synthetic data is not one-size-fits-all. A prompt that works great for a
This is Part 9 of Generative AI in Data Science blog series – the post that turns feedback-driven synthetic data generation into a measurable, optimizable pipeline. Now that you have got adaptive data loops in place (Part 8), this is where we define how to evaluate their impact rigorously. By now, you have built
Part 9: Measuring What Matters -Evaluating Feedback-Tuned Synthetic Data Read More »
Here is the Part 8 of Generative AI in Data Science blog series. This one explores how to close the loop between model performance and synthetic data generation – turning your synthetic data pipeline into an adaptive, self-improving system. At this point in the series, we have covered the full synthetic data lifecycle: Building
Part 8: Closing the Loop – Using Feedback to Improve Synthetic Data Generation Read More »
This is Part 7 of Generative AI in Data Science blog series. This one goes straight into the real-world tension between synthetic and real data: when to combine them, how much to trust each, and what it actually does to your model’s performance. In the last six parts of this series, we showed how
Part 7: Combining Real + Synthetic Data -What Works, What Breaks, and Why Read More »
This is Part 6 of the Generative AI in Data Science series picking up where Part 5 (multi-modal synthetic data generation) left off. This post is about the real challenge that comes once the generative magic is working: productionizing synthetic data pipelines in a way that is reliable, reproducible, and MLOps-compatible. Part 6: Productionizing Synthetic
Part 6: Productionizing Synthetic Data Pipelines with MLOps Best Practices Read More »
This blog post is Part 5 of Generative AI in Data Science series. This one tackles multi-modal synthetic data generation combining text, images, and structured data using GPT-4, DALL·E, and prompt chaining. It is built for your audience of data science professionals who care about building real-world, reproducible pipelines. Part 5: Multi-Modal Synthetic Data
Part 5: Multi-Modal Synthetic Data Generation with GPT-4, DALL E & Prompt Chaining Read More »
