AI/ML
34 posts
Why DSA Matters in the Agentic Era
Understanding the significance of Data Structures and Algorithms in today's rapidly evolving AI landscape.
Explainable Concept Drift in Process Mining: MLP-Based Non-Linear Causality
An explainable framework for concept drift detection using MLP-based non-linear causal modeling in process mining.
Process Mining: how to find a right use case
In this post, we’ll try to explore how to find the right use case for process mining. If you are new to Process Mining, you may read my l...
Process mining explained
In this post we’ll dig deep into process mining. You can read my last post on data mining and process mining.
Data Mining and Process mining
In this post we’ll directly start with what process science is and how this is connected with data science. You can read my last post on ...
Exploring Business informatics and Process mining
Finally able to write a new post :-)
IEEE Big Data Conference 2021: Serverless Machine Learning: Call for papers on ML
Good opportunity to submit a paper in this upcoming International Workshop on Serverless Machine Learning for Intelligent and Scalable ...
When to use Google Cloud BigTable and BigQuery? A Use case..
Many times cloud folks get confused with Google Cloud’s BigTable and BigQuery services, which one to use when?
Data Science for IoT: FDP: Summary
I have atteneded this online 3-days Faculty Developement Program on Data Science for IoT.
Time Series Data Analysis: Headlines: IIITA
Time Series Data Analysis Workshop at IIITA organzied by Manish Sir Attedned the workshop virtually Shantanu Das I attended from the seco...
Online conference in Data Science: ACM & IITM
Some days back I have attended a virtual conference in Data Science organized by ACM SIGKDD and IIT Madras
Books recommendation series: Hands-On ML
Book
Software Engineer or Data Scientist: thoughts
Image classification of Flowers in Machine Learning: Challenges and developments
Tips to learn Machine Learning
In this COVID-19 lockdown time, I am sure many of us want to learn new things. In technology stack Machine Learning is the most sought fi...
Image classification of Indian cows breed using fastai lib: Train Model
Inspiration of this blog post came from fast.ai course taught by Jeremy Howard. In the Part 1 of this post we’ll learned how to build yo...
Image classification of Indian cows breed using fastai lib
Inspiration of this blog post came from fast.ai course taught by Jeremy Howard. In first part of this post we’ll learn how to build your...
CNN: Understanding edge detection with an example
The convolution operation is one of the fundamental building blocks of a convolutional neural network and here we’ll discuss edge detecti...
Reinforcement Learning Explained in brief for a layperson
As we know, Machine Learning algorithms can broadly be divided into 3 main categories:
Deep learning on large images: challenges and CNN
Applying deep learning on large images is always a challenge but there a solution using convolutional but first, let’s understand in brie...
Prognostic Analytics for Predictive Maintenance, a case study
First, let's try to understand the difference between prognostic analysis and predictive analysis. Predictive analysis ...
MOOCs alert: Practical Machine Learning with Tensorflow at NPTEL
I like the way, Prof. Ravindran explains complex topics in an easy way. I took one of his course in Data analytics and reinforcement lear...
Books recommendation series: Predictive analytics
Book
Books recommendation series: Elements of statistical learning
Since long I was thinking to write down my recommendation of the books I have read recently or in past as well. The plan is to post atlea...
Deep learning specialization notes
A couple of months back I have completed Deep Learning Specialization taught by AI guru Andrew NG. During the learning process, I have ma...
Choropleth Maps in Python
Choropleth maps are a great way to represent geographical data. I have done a basic implementation of two different data sets. I have use...
Notes MI Class Standford
Personal notes which I made during Andrew NG’s Stanford Machine learning class.
Interesting Machine learning algorithms in R
Widely used Machine learning algorithms in R
JHU Data Science Specialization Capstone
I have created a text prediction application as a part of Coursera Johns Hopkins University Capstone project.
Stanford Machine learning class slides
Andrew NG Machine learning class is the best class so far which I took online.
Getting and cleaning data using R programming project notes
Brief notes of my learning from course project of getting and cleaning data course from John Hopkins University.
Stanford DB class
Along with my AI class course, I have also started DB class (Oct-Dec session) from Standford online courses series. Actually, learning D...
AI Class Unit 2 Problem Solving
I have completed the AI-Class Unit 2 problem solving. It has 38 sub-sections so will take some hours to finish. Some of the algorithms co...
Online AI course at Standford
If you want a break from your routine work (Writing code, testing etc) then like me you can also attend online courses form Stanford univ...