1. AI/ML Foundations, Python, NumPy & Pandas
Sections 1-6: Artificial Intelligence vs ML vs Deep Learning, Supervised/Unsupervised types, Python venv, NumPy ndarray & Pandas DataFrames
Module 1: AI, ML, Deep Learning & Paradigm Types
Understanding the hierarchy of Artificial Intelligence, Machine Learning, Deep Learning, and learning paradigms.
🇬🇧 English Definition
Artificial Intelligence is the broad field of building systems that perform intelligent tasks. Machine Learning learns patterns from data to make predictions. Deep Learning uses multilayer neural networks for complex high-dimensional representations.
🇮🇳 Hinglish Definition
AI sabse broad field hai. ML data se patterns learn karta hai predictions ke liye. Deep Learning multilayer neural networks par depend karta hai.
Core Concepts & Principles
- Supervised Learning: Learns mapping from labeled input features X to target target y.
- Unsupervised Learning: Discovers hidden patterns, structures, or clusters in unlabeled data without target labels.
- Reinforcement Learning: Learns optimal policies through action-reward interactions with an environment.
🔍 Line-by-Line Code Breakdown
- Line 1-2: Import LinearRegression model from scikit-learn library.
- Line 3: m.fit(X_train, y_train) trains parameters on feature-target dataset.
- Line 4: pred = m.predict(X_test) applies learned parameters onto unseen test features.
🎯 Interview Focus & Questions
- Q: AI vs ML vs Deep Learning?
A: AI is the broad field of intelligent systems; ML is pattern learning from data; Deep Learning is ML via multi-layer neural networks. - Q: Supervised vs Unsupervised learning?
A: Supervised uses labeled targets (X -> y); Unsupervised finds structures without target labels.
from sklearn.linear_model import LinearRegression
m = LinearRegression()
m.fit(X_train, y_train)
pred = m.predict(X_test)
print("Model trained and predictions generated.")Module 2: Python, NumPy Arrays & Pandas DataFrames
High-performance vector operations with NumPy ndarray and tabular data manipulation with Pandas.
🇬🇧 English Definition
NumPy provides multidimensional ndarray structures for vectorized matrix calculations. Pandas provides labeled 2D DataFrames for loading, filtering, grouping, and cleaning datasets.
🇮🇳 Hinglish Definition
NumPy fast matrix calculations aur vectorization ke liye base hai. Pandas real-world tabular data handle karne ke liye read_csv, loc/iloc aur groupby offer karta hai.
Core Concepts & Principles
- Vectorization: Operations apply element-wise across arrays without slow Python for-loops.
- DataFrame Operations: df = pd.read_csv('data.csv'); df.groupby('category')['salary'].mean()
- Missing Values: df.fillna(df['col'].median()) imputes missing numerical entries safely.
🔍 Line-by-Line Code Breakdown
- Line 1-2: Import numpy and pandas libraries with standard aliases np and pd.
- Line 3-4: Create DataFrame from dictionary data and print summary info.
🎯 Interview Focus & Questions
- Q: Why is NumPy faster than standard Python lists?
A: NumPy ndarrays store data in contiguous memory blocks and execute compiled C-level vectorized instructions.
import numpy as np
import pandas as pd
df = pd.DataFrame({"age": [21, 25, 30], "salary": [30000, 45000, 60000]})
print("NumPy Mean Age:", np.mean(df["age"]))
print(df.head())