Anil Tiwari

Technology Lead, AI, Machine Learning, TensorFlow, App modernization, Design and development of enterprise apps.

AI Agents

Build an Multi-agent, AI-Powered SQL Query Generator

Try app at (Use your own Google Gemini API Key): https://mlprojects-text-to-sql.streamlit.app In today’s data-driven world, accessing and understanding data stored in relational databases is more crucial than ever. However, writing SQL queries can be a barrier to entry for many, especially those without a technical background. What if you could simply ask your database a […]

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Time series forecasting app

Forecasting Time Series Data with a Streamlit App and Linear Regression

Do you need to predict future trends based on historical data? Want an easy-to-use tool that handles data upload, date frequencies, and generates interactive charts? Look no further! This blog post introduces a forecasting app built with Streamlit, Python, and a dash of data science magic. Here is the link of application to try with

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confusion matrix

A Practical Guide to Measuring Machine Learning Performance

So, you’ve built a machine learning model. Fantastic! You’ve navigated the complexities of algorithms, massaged the data, and hopefully, created something that addresses a real-world challenge. But, let’s be honest, simply having a model isn’t enough. You need to know how well it performs. Is it truly solving the problem, or is it just producing random guesses? Think

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Essential Python for AI/ML” course

Essential Python for AI/ML: A Crash Course

This short course provides a foundation in Python programming, focusing on the key concepts and libraries essential for embarking on AI/ML projects. Target Audience: Individuals with little to no prior programming experience who want to learn Python for AI/ML. Duration: 20-25 hours (estimated) Learning Objectives: Course Structure: Module 1: Python Fundamentals (5-7 hours) Module 2:

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MLops cycle

End-to-End MLOps: A Practical Guide with Example Project

The allure of machine learning lies in its potential to solve complex problems and unlock hidden insights. However, transforming a promising model from a research environment into a robust, business-impacting application requires a disciplined approach: MLOps. MLOps, short for Machine Learning Operations, is the evolving discipline focused on automating and streamlining the entire ML lifecycle,

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ML flow

Machine Learning Workflow: A Step-by-Step Guide for Beginners

In today’s data-driven environment, machine learning (ML) has emerged as a transformative tool, empowering businesses and individuals to glean actionable intelligence, streamline workflows, and make informed decisions. From personalized recommendations on your favorite streaming platform to fraud detection systems safeguarding your financial transactions, ML is quietly revolutionizing our lives. While the field may seem complex,

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unsupervised learning

A Deep Dive into Unsupervised Learning with K-Means Clustering with Python Example

In the realm of machine learning, we often talk about supervised learning, where labeled data guides our models to make predictions. But what happens when we’re swimming in a sea of unlabeled data? That’s where unsupervised learning comes to the rescue! Unsupervised learning techniques allow us to discover hidden patterns, structures, and relationships within data without any

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python numpy

Module 2: Unleash the Power of NumPy for AI/ML

Welcome back, aspiring AI/ML engineers! You’ve laid the groundwork in Module 1, mastering the fundamentals of Python. Now, it’s time to wield a powerful tool that’s at the heart of almost every AI/ML project: NumPy. NumPy, short for Numerical Python, is a library that provides support for large, multi-dimensional arrays and matrices, along with a

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python pandas

Module 3: Mastering Data Analysis with Pandas for AI/ML

Welcome back! In Module 2, you conquered the numerical world with NumPy. Now, it’s time to tackle the messy, real-world data that fuels AI/ML projects. That’s where Pandas comes in. Pandas is a powerful Python library designed for data manipulation and analysis. Think of it as your go-to tool for cleaning, transforming, and exploring data

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