π
FoundationMathematics for AI
Build the mathematical foundation needed for ML and DL.
- Calculus and optimization
- Linear algebra and vectors
- Gradient descent by hand
Python
NumPy
JupyterAI education for every student
Learn AI by building real projects.
Structured learning paths in Mathematics for AI, Machine Learning, Computer Vision and Generative AI for BCA, MCA and aspiring AI engineers.
Project-based learningMath-first approachPortfolio-ready projects
Your learning dashboard
From concept → practice → project
Current module: Traffic Object Detection
Build a system that detects vehicles, estimates traffic density and prepares a project report.
import cv2, ultralyticsresults = model.track(frame)traffic_count = len(vehicles)
Python
R
MATLAB
OpenCV4Core Tracks
2–3Month Learning Paths
15+Portfolio Projects
100%Project Focused
Choose your track
Learning paths with detailed weekly roadmaps
Every course page now includes the outcome, modules, tools, projects and weekly learning path.
PythonNumPyJupyter⌘
CoreMachine Learning Foundations
Implement classical ML models and evaluate them properly.
- Python, NumPy, Pandas
- Regression and classification
- R and MATLAB exposure
PythonRMATLABNumPy
Pandas
scikit-learn◉
SpecializationComputer Vision Engineer
Use real traffic, object detection and image workflows.
- OpenCV and image processing
- CNNs and YOLO
- Traffic monitoring capstone
PythonOpenCV
PyTorch
TensorFlow
Ultralytics✦
SpecializationGenerative AI Engineer
Build LLM apps, RAG systems and AI agents.
- Prompt engineering
- Vector databases and RAG
- AI agents and deployment
Python
Hugging Face
OpenAI
LangChain
Google GeminiThe iKrishi way
Learn like a builder, not a passive viewer.
Students watch short lessons, practice skills, build real projects and showcase their portfolio on GitHub and LinkedIn.
Get launch updates01
Watch
Clear concept lessons in small learning blocks.
02
Practice
Guided notebooks, exercises and mathematical derivations.
03
Build
Mini-projects and capstones based on real AI use cases.
04
Showcase
Turn each project into portfolio proof.
AI roadmap
From foundations to advanced AI applications
πMathematicsCalculus · Algebra · Probability
⌘Machine LearningModels · Metrics · Tuning
◇Deep LearningNeural nets · Backpropagation
◉Computer VisionCNNs · Detection · OCR
✦Generative AILLMs · RAG · Agents
Completed programs and recognition
Founder-led training experience
iKrishi is built on real teaching experience across AI, machine learning and agricultural applications.
AI Skills for Agricultural Professionals
Delivered a training session on AI and ML concepts, including decision-tree concepts such as Gini impurity and entropy.
University Training Program
Recognized during a training program at the University of Agricultural Sciences, Dharwad.
Faculty & Postgraduate ML Program
The uploaded appreciation certificate records a faculty and postgraduate program on theory and practical applications of Machine Learning.
About the founder
Dr. Abhishek V. Hukkerikar
iKrishi is led by Dr. Abhishek V. Hukkerikar, an AI educator and researcher focused on helping students understand AI from fundamentals to real applications.
The University of Agricultural Sciences, Dharwad engaged Dr. Hukkerikar to deliver a faculty and postgraduate training program on the theory and practical applications of Machine Learning. The program included supervised and unsupervised ML, hands-on Python, MATLAB-based automated ML workflows, crop-yield prediction, satellite imagery applications and generative AI approaches for weather prediction.
The mission is simple: help students build strong foundations, meaningful projects and the confidence to solve real problems with AI.
Free resources
Start learning before joining a track
Derivatives, gradients and optimization explained visually.
Request notes →Vehicle detection, counting and traffic-density estimation roadmap.
View path →Retrieval augmented generation explained with a PDF chatbot example.
View path →Join the course waitlist
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