Course: AI for Everyone: Learn, Create & Work Smarter
You do not need to code to start learning AI
Start using AI with one practical task, a clear prompt, and a simple way to check the result.
Blog
Kishor Academy blog
Short, practical articles from each course: how to check AI answers, evaluate models, build tutors and agents, and keep AI features safe.
Course: AI Foundations & Responsible AI
A confident answer still needs evidence
Learn to separate a fluent AI response from a claim supported by facts, sources, and appropriate checks.
Course: Python & Data for Machine Learning
Your first machine-learning skill is understanding the data
Before training a model, learn what a row means, when each field becomes available, and how cleaning changes your conclusions.
Course: Applied Machine Learning: Build, Evaluate, Improve
Why a simple baseline belongs in every ML project
Compare a learned model with a simple alternative using the same data and metric before claiming an improvement.
Course: Large Language Models: From Prompts to Practice
Prompt engineering starts with a task you can check
Build useful prompts by defining the task, examples, output format, and evaluation before judging the response.
Course: LLM Engineering: RAG, Evals & Multimodal Apps
A course tutor should be able to show its evidence
Learn why retrieval, permissions, citations, and unanswered questions matter when building an AI learning assistant.
Course: Agentic AI: Tools, Workflows & MCP
Before giving an AI agent tools, define its boundaries
Design a small agent workflow with clear state, limited tools, exact-content approval, and meaningful failure tests.
Course: Reasoning Models & Test-Time Compute
When is it worth paying a model to think longer?
Reasoning models and sampling spend more compute per answer. Learn when that helps, when it does not, and how to measure the difference on your own task.
Course: Post-Training: Fine-Tuning, RLHF & DPO
Should you fine-tune? Usually not first
Fine-tuning changes behaviour, not knowledge. Learn when prompting or retrieval is enough, and how to audit data before you train.
Course: AI Safety & Alignment for Builders
Your system prompt is not a security boundary
Prompt injection cannot be filtered away. Enforce limits in code where actions happen, and test with attacks you did not write.