AI for Everyone: Complete Introduction

02Where AI appears daily

• Search ranking and recommendations.
• Fraud detection in financial systems.
• Voice assistants and translation.
• Medical image triage and diagnostics support.

03Differences at a glance

• Machine Learning: broader set of algorithms, often with engineered features.
• Deep Learning: neural networks with many layers learning features automatically.
• Deep learning usually needs more data and compute, but performs strongly in unstructured domains.

04Classic ML pipeline sketch

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)
pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, pred))

05How a neural network learns

Neural networks are layers of weighted sums + non-linear activations. During training, the model adjusts weights to reduce prediction error using gradient descent and backpropagation.

• Input layer -> hidden layers -> output layer.
• Loss function measures how wrong predictions are.
• Optimizer updates parameters iteratively.

06Minimal dense network

from tensorflow import keras

model = keras.Sequential([
    keras.layers.Dense(32, activation="relu", input_shape=(20,)),
    keras.layers.Dense(16, activation="relu"),
    keras.layers.Dense(1, activation="sigmoid"),
])

model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
model.fit(X_train, y_train, epochs=10, batch_size=32)

07Natural Language Processing (NLP)

• Text classification: spam, sentiment, intent.
• Entity extraction: names, dates, amounts.
• Question answering and summarization.

08Computer Vision

• Image classification predicts a label for an image.
• Object detection finds and localizes multiple objects.
• Segmentation predicts per-pixel categories.

import torch
from torchvision import models, transforms
from PIL import Image

model = models.resnet18(weights="IMAGENET1K_V1")
model.eval()
img = Image.open("sample.jpg")
tensor = transforms.ToTensor()(img).unsqueeze(0)
with torch.no_grad():
    logits = model(tensor)
pred_class = logits.argmax(dim=1).item()

09What makes LLMs useful

Large Language Models are trained on massive text corpora to predict next tokens. This enables drafting, coding assistance, retrieval-augmented Q&A, and workflow automation.

• Prompt quality strongly affects output quality.
• Grounding with trusted data reduces hallucinations.
• Tool-using agents can execute tasks beyond text generation.

10Prompt structuring pattern

You are a support assistant for Rishtaara.
Context: User asks about refund policy.
Constraints: Use only policy docs provided below.
Output format:
1) Direct answer
2) Policy citation
3) Next actions

11Common AI risks

• Bias from unrepresentative training data.
• Privacy leakage and sensitive data exposure.
• Lack of transparency and explainability.
• Automation without accountability.

12Responsible AI checklist

• Define acceptable error and harm thresholds.
• Track fairness metrics across demographic groups.
• Use human review for high-impact decisions.
• Document model limits and failure modes.

13Career directions

• Data Analyst -> Data Scientist.
• Backend Engineer -> ML Engineer.
• Product Manager -> AI Product Manager.
• Security/Policy -> AI Governance and Risk.

14Foundational skill stack

• Math basics: probability, linear algebra, optimization intuition.
• Python + SQL + data workflows.
• Experiment tracking and model evaluation.
• Communication: framing business impact of model decisions.

15Project objective

Pick a real domain problem and design an AI solution blueprint: objective, data sources, model approach, evaluation metrics, and risk controls.

• Use case examples: student support bot, churn prediction, content tagging.
• Define one primary success metric and two safety metrics.
• Describe deployment and monitoring plan.

16Blueprint template

## Problem
What business/user problem are we solving?

## Data
What sources, quality checks, and privacy constraints exist?

## Model and Baseline
What model family and what non-AI baseline will we compare against?

## Evaluation
Primary metric, error analysis slices, and fairness checks.

## Deployment and Monitoring
Rollout plan, fallback path, and alert thresholds.