1. Foundations of Artificial Intelligence & Machine Learning
🇬🇧 English Definition
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines programmed to think, reason, and learn. Machine Learning (ML) is a subset of AI focused on algorithms that learn patterns from historical data to make data-driven predictions without explicit programming.
🇮🇳 Hinglish Definition
AI ek broad domain hai jahan machines ko human intelligence simulate karne ki capability di jaati hai. Machine Learning AI ka wo part hai jahan algorithms purane data se patterns seekhte hain aur bina explicit hardcoded rules ke future predictions karte hain.
// Simple Artificial Neuron / Decision Example in Python
def predict_house_price(sqft, bedrooms):
// Base weight learned from data
w_sqft = 250.0
w_bed = 15000.0
bias = 50000.0
price = (sqft * w_sqft) + (bedrooms * w_bed) + bias
return price
print("Predicted Price:", predict_house_price(1200, 3))
🔍 Line-by-Line Code Breakdown
- def predict_house_price(sqft, bedrooms): Defines a function simulating linear predictive model.
- w_sqft = 250.0: Weight parameter representing price per square foot.
- w_bed = 15000.0: Weight parameter representing price per bedroom.
- bias = 50000.0: Intercept parameter capturing base property valuation.
- price = (sqft * w_sqft) + (bedrooms * w_bed) + bias: Linear combination formula Y = WX + b.
🎯 Interview Focus & Questions
Q: What is the main difference between Rule-Based Programming and Machine Learning?
A: Rule-based programming takes Rules + Data to produce Answers. Machine Learning takes Data + Answers to learn Rules/Patterns automatically.
17. Scikit-Learn Pipelines & ColumnTransformers
🇬🇧 English Definition
Scikit-Learn Pipelines allow chaining sequential data transformation and model training steps together, avoiding data leakage during cross-validation. ColumnTransformers enable applying distinct transformations to specific numeric and categorical columns.
🇮🇳 Hinglish Definition
Pipeline ek robust container hota hai jo data preprocessing aur model training steps ko ek sath bind kar deta hai taaki data leakage na ho. ColumnTransformer numerical aur categorical features par alag-alag processing apply karne ki permission deta hai.
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.ensemble import RandomForestClassifier
num_features = ['age', 'income']
cat_features = ['city', 'education']
num_pipeline = Pipeline([
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler())
])
cat_pipeline = Pipeline([
('imputer', SimpleImputer(strategy='most_frequent')),
('encoder', OneHotEncoder(handle_unknown='ignore'))
])
preprocessor = ColumnTransformer([
('num', num_pipeline, num_features),
('cat', cat_pipeline, cat_features)
])
full_pipeline = Pipeline([
('preprocessor', preprocessor),
('model', RandomForestClassifier(n_estimators=100, random_state=42))
])
🔍 Line-by-Line Code Breakdown
- ColumnTransformer([...]): Combines column-specific feature transformations.
- SimpleImputer(strategy='median'): Replaces missing values with feature median.
- StandardScaler(): Scales numeric features to mean=0 and variance=1.
- OneHotEncoder(handle_unknown='ignore'): Converts categories into binary indicators safely.
- full_pipeline.fit(X_train, y_train): Fits all transformers and the classifier in a leak-free sequence.
🎯 Interview Focus & Questions
Q: Why should you use Pipelines during Cross-Validation?
A: If feature scaling or imputation is performed before splitting folds, statistics from validation folds leak into training folds. Pipelines ensure transformations are re-fitted strictly on training folds only.
45. Retrieval-Augmented Generation (RAG)
🇬🇧 English Definition
Retrieval-Augmented Generation (RAG) enhances Large Language Models by retrieving relevant authoritative document chunks from a vector database at query time and injecting them into the prompt context to prevent hallucination and provide domain-specific accuracy.
🇮🇳 Hinglish Definition
RAG ek powerful architecture hai jo LLM ko private / custom enterprise documents se connect karta hai. Jab user query karta hai, Vector DB se exact matching context retrieve karke prompt me add kar diya jaata hai taaki model accurate aur non-hallucinated answer de sake.
// High-Level RAG Workflow Concept in Python
from sentence_transformers import SentenceTransformer
import numpy as np
embedder = SentenceTransformer('all-MiniLM-L6-v2')
documents = [
"Antigravity AI is built by Google DeepMind team.",
"RAG prevents model hallucinations by retrieving verified facts.",
"Python is the leading programming language for Data Science."
]
doc_embeddings = embedder.encode(documents)
query = "How does RAG prevent hallucinations?"
query_embedding = embedder.encode([query])
scores = np.dot(doc_embeddings, query_embedding.T).flatten()
best_idx = np.argmax(scores)
retrieved_context = documents[best_idx]
print("Retrieved Document:", retrieved_context)
🔍 Line-by-Line Code Breakdown
- SentenceTransformer(...): Loads pre-trained embedding model for semantic similarity.
- embedder.encode(documents): Vectorizes document chunks into high-dimensional dense embeddings.
- np.dot(...): Computes cosine similarity scores between query vector and document vectors.
- np.argmax(scores): Selects top context document with highest semantic similarity.
🎯 Interview Focus & Questions
Q: What are the key components of a production RAG system?
A: Document Ingestion/Chunking, Embedding Model, Vector Database (e.g. Chroma/Faiss/Pinecone), Retriever, Prompt Template with Context, and LLM Generator.
53. 30+ Interview Q&A & 90-Day Study Plan
🇬🇧 English Definition
Structured career preparation framework providing conceptual interview solutions, debugging methodologies, coding templates, and a day-by-day 90-day learning roadmap to master Data Science & AI.
🇮🇳 Hinglish Definition
Complete job preparation roadmap jisme top 30+ technical interview questions, ML debugging strategies, production templates aur 90 days ka systematic study schedule shamil hai.
🎯 90-Day Master Study Plan Overview
Days 1-15: Python, NumPy, Pandas, Data Wrangling & Visualizations
Days 16-30: Linear Algebra, Calculus, Statistics, Probability & Preprocessing
Days 31-45: Supervised Learning (Regression, Trees, Random Forest, XGBoost)
Days 46-60: Unsupervised Learning (Clustering, PCA), Metrics, Pipelines & Tuning
Days 61-75: Deep Learning, TensorFlow/PyTorch, CNNs, RNNs & Computer Vision
Days 76-90: NLP, Transformers, LLMs, GenAI, RAG, FastAPI, Docker & Capstone Deployment