Handwritten notes · Part 19
AI, ML and Generative AI Handwritten Notes for Placements (Free PDF)
By Gaurav SharmaPublished
26 pages of handwritten-style AI, machine learning and generative AI notes for placements and interviews: ML types and workflow, regression, trees and ensembles, k-means and PCA, bias-variance, confusion matrix and F1, gradient descent, neural networks, CNN, RNN, transformers and attention, LLMs, prompt engineering, embeddings, RAG, agents and MCP, with Hinglish explanations.
- Pages
- 26
- Format
- A4 PDF
- Language
- English + Hinglish
- Price
- Free by email
What's inside
24 topics, one page each
- 01AI vs ML vs deep learning vs generative AI
- 02Supervised, unsupervised, reinforcement and self-supervised learning
- 03ML workflow, train / validation / test split and data leakage
- 04Linear and logistic regression
- 05Decision trees, random forest, boosting, KNN, SVM and Naive Bayes
- 06Unsupervised learning: k-means and PCA
- 07Bias-variance, overfitting and regularization (L1, L2, dropout)
- 08Confusion matrix, precision, recall and F1 with a worked example
- 09ROC-AUC and regression metrics (MAE, MSE, RMSE, R²)
- 10Gradient descent, learning rate and optimisers
- 11Neural networks, activation functions and backpropagation
- 12CNN and RNN / LSTM
- 13Transformers, self-attention and Q, K, V
- 14LLM basics: tokens, context window, temperature and top-p
- 15Pretraining, fine-tuning, LoRA and RLHF
- 16Prompt engineering techniques and prompt injection
- 17Embeddings, cosine similarity and vector databases
- 18RAG pipeline: chunk, embed, retrieve, generate
- 19Prompting vs RAG vs fine-tuning, and calling an LLM API
- 20AI agents, tool calling and MCP
- 21Evaluation, hallucinations and guardrails
- 22Interview questions with short answers
- 23"Which one?" scenario quiz
- 24Revision cheat sheet
AI and machine learning questions now come up in placement interviews for software, data and AI roles alike. These notes cover classic machine learning first, then deep learning, then the generative AI topics companies hire for today: LLMs, RAG and agents.
What makes these AI and ML notes different?
- Classic ML and GenAI in one place. Regression, trees, metrics and overfitting, followed by transformers, LLMs, RAG and agents, so you can answer both kinds of interview question.
- Worked examples, not just definitions. A confusion matrix with real numbers, regression error calculations, and short scikit-learn and Python snippets you can run.
- Practical GenAI. When to prompt, when to use RAG and when to fine-tune, how tool calling and MCP work, and how to reduce hallucinations and evaluate an LLM app.
- A scenario quiz. Fifteen "which one would you use?" questions on models, metrics and GenAI design, the way interviewers actually ask them.
How should you use these AI and ML notes?
Read the pages in order once, then run each code example in Colab or Jupyter. Work the confusion matrix example by hand, and try explaining RAG and attention out loud in two minutes each. Use the quiz and the last page as revision before the interview.
This is Part 19 of the series. For the Python the examples use, see the Python handwritten notes.
FAQ
Questions about these notes
Are these AI and ML notes really free?
Yes. The full 26-page PDF is free; email me to request the complete PDF. Share it with friends preparing for placements.
Do I need strong maths to follow these notes?
No. School-level maths and basic Python are enough. Formulas such as sigmoid, gradient descent, precision and recall, cosine similarity and attention are shown with plain-language explanations and worked examples.
Do the notes cover generative AI and LLMs?
Yes. About half the notes cover generative AI: transformers and attention, tokens and temperature, pretraining, fine-tuning and RLHF, prompt engineering, embeddings and vector databases, RAG, AI agents with tool calling, MCP, and evaluation and guardrails.
Are these notes useful for AI engineer and data science interviews?
Yes. They include a page of common interview questions with short answers, a scenario quiz (which model or metric to pick), and a one-page revision sheet, alongside small scikit-learn and Python examples.
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