Data-Analytics on Samarthya

AI on this blog: a reading map

Mon, 26 Oct 2026 18:50:00 -0400

I’ve written about AI and data on and off since 2023, one post at a time. Read in this order, they tell one story: from how machines learn, to how language models work, to building with them, to letting them act.

1. How machines learn
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Post	In one line
The Core Divide	Supervised vs unsupervised learning
K-Nearest Neighbors	Classify by the company you keep
Mastering TF-IDF	Turning text into numbers that mean something
Data Mining Essentials	Distances, clusters and the core vocabulary
Matrix Multiplication to GPUs	Why AI runs on graphics cards

2. How language models work
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Post	In one line
GenAI	What generative AI is, and its building blocks (2023)
Language Model	Transformers and the attention breakthrough
Two Students, Two Talents	Auto-regressive (GPT) vs auto-encoding (BERT) models
The Evolution of Language Models	From 2017’s transformer to today’s families
The Probability Engine	The maths of choosing the next token
Tokens and tokenomics	What a token is, context windows, and the bill

3. Building with models
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Post	In one line
Ollama	Running language models on your own machine
A minimal LangChain + Ollama demo	Prompt, model and chain, end to end
A practical RAG assistant	Answering from your own documents
From speech to text	Speech recognition in Python
Choosing the right framework	The AI stack, layer by layer
ML pipelines on Vertex AI	Train, evaluate and deploy, repeatably

4. Letting models act
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Post	In one line
From RAG to tools and agents	Function calling and the agent loop
MCP, the Model Context Protocol	One standard plug for tools and data
The Ghost Board	A story about agents nobody was watching

The data shelf
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Analytics and big-data posts that sit underneath much of the above:

Data Mining Essentials

Sat, 08 Nov 2025 12:00:00 +0000

Whether you’re preparing for a quiz or just brushing up on fundamentals, this guide distills the key concepts from Data Mining into bite-sized, memorable chunks. Let’s dive in!

Linear Programming (LP)

Fri, 14 Feb 2025 12:00:00 +0000

What is Linear Programming?
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Linear programming is a mathematical technique used to find the best possible outcome (maximum profit or minimum cost) in a situation where the relationships between the factors involved are linear. It’s like finding the sweet spot where you get the most out of what you have, given certain limitations.

Monte Carlo Optimisation

Fri, 14 Feb 2025 12:00:00 +0000

Imagine you’re trying to find the best solution to a problem, but the problem is really complex and has a lot of randomness involved. Maybe you’re trying to design a new drug, or optimize a financial portfolio, or figure out the best route for a delivery truck.

Business Analytics: The bigger picture

Fri, 07 Feb 2025 12:00:00 +0000

Imagine you’re a business owner trying to figure out why your sales are down. You could guess, or you could use business analytics.

In simple terms, business analytics is like a detective for your business. It uses data to uncover clues and insights that help you make better decisions. Instead of relying on gut feelings, you use facts and figures to understand what’s happening and what you can do about it.

Comparison of Competing Forecasting Models

Fri, 07 Feb 2025 12:00:00 +0000

Performance Measure	Description	Interpretation	Advantages	Disadvantages
Mean Absolute Error (MAE)	Average absolute difference between forecast and actual values.	Lower MAE indicates better accuracy.	Easy to understand and interpret. Robust to outliers.	Doesn’t penalize large errors more than small ones.
Mean Squared Error (MSE)	Average squared difference between forecast and actual values.	Lower MSE indicates better accuracy.	Penalizes larger errors more heavily. Mathematically convenient.	Sensitive to outliers. Units are squared.
Root Mean Squared Error (RMSE)	Square root of MSE.	Lower RMSE indicates better accuracy.	Same units as the original data. Penalizes larger errors more heavily.	Sensitive to outliers.
Mean Absolute Percentage Error (MAPE)	Average absolute percentage difference between forecast and actual values.	Lower MAPE indicates better accuracy.	Easy to understand and interpret. Scale-independent.	Undefined if actual values are zero. Can be biased if actual values are close to zero.
Symmetric Mean Absolute Percentage Error (sMAPE)	A variation of MAPE that handles zero or near-zero actual values better.	Lower sMAPE indicates better accuracy.	Handles zero or near-zero actual values better than MAPE. Scale-independent.	Can still be biased if forecasts are consistently over or under the actual values.
Mean Error (ME) or Bias	Average difference between forecast and actual values.	Closer to zero indicates less bias. Positive ME suggests over-forecasting. Negative ME suggests under-forecasting.	Detects systematic over or under-forecasting.	Doesn’t measure the magnitude of errors, only the direction.
Theil’s U	Compares the forecasting accuracy of the model to a naive forecast (e.g., assuming no change).	U < 1 indicates better performance than the naive forecast. U = 1 means equal performance. U > 1 means worse performance.	Useful for assessing the value added by the model compared to a simple benchmark.	Can be sensitive to outliers.

Imagine you’re predicting how many ice cream cones you’ll sell each day. You have a model that makes these predictions, and you want to see how good it is. These measures help you do that.

Forecasting Methods

Fri, 07 Feb 2025 12:00:00 +0000

Forecasting methods are the techniques used to predict future outcomes based on past data and trends. They’re like having a crystal ball, but instead of magic, they use data and statistics!

Model and Selection

Fri, 07 Feb 2025 12:00:00 +0000

What is a Model?
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In the context of forecasting and business analytics, a model is a simplified representation of a real-world situation or process. It’s like a blueprint or a miniature version that helps us understand, analyze, and make predictions about something complex.

Business Analytics: Simplified

Mon, 03 Feb 2025 12:00:00 +0000

Business Analytics (BA) is like a GPS for businesses—it uses data to guide decisions, solve problems, and predict future outcomes. It turns raw numbers into actionable insights, helping companies answer questions like:

Correlation and Covariance

Sun, 02 Feb 2025 12:00:00 +0000

Introduction
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Correlation and covariance are statistical measures that help us understand the relationship between two numerical variables.

RFM Analysis: A Comprehensive Guide to Customer Segmentation

Fri, 24 Jan 2025 12:00:00 +0000

What is RFM Analysis?
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RFM (Recency, Frequency, Monetary) Analysis is a customer segmentation technique that uses three key metrics:

GenAI

Tue, 03 Oct 2023 12:00:00 +0000

(thumbnail only — original image lost)

Artificial Intelligence (AI) has revolutionized many aspects of our lives, from virtual assistants like Siri and Alexa to self-driving cars and personalized content recommendations. One of the exciting and rapidly evolving subfields of AI is Generative AI, often referred to as GenAI. In this beginner-friendly blog post, I’ll explore the basics of Generative AI, its applications, and how it’s changing the way we interact with technology.

Data-Analytics on Samarthya

AI on this blog: a reading map

1. How machines learn #

2. How language models work #

3. Building with models #

4. Letting models act #

The data shelf #

Data Mining Essentials

Linear Programming (LP)

What is Linear Programming? #

Monte Carlo Optimisation

Business Analytics: The bigger picture

Comparison of Competing Forecasting Models

Forecasting Methods

Model and Selection

What is a Model? #

Business Analytics: Simplified

Correlation and Covariance

Introduction #

RFM Analysis: A Comprehensive Guide to Customer Segmentation

What is RFM Analysis? #

GenAI

1. How machines learn
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2. How language models work
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3. Building with models
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4. Letting models act
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The data shelf
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What is Linear Programming?
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What is a Model?
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Introduction
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What is RFM Analysis?
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