Master Python for LLMs and AI! Explore foundational concepts, data structures, libraries, and advanced topics for building intelligent applications. Start learning now!

Python Programming Documentation

This document provides a comprehensive overview of Python programming concepts, data structures, libraries, and advanced topics.

Articles

Python Programming for Beginners

This section covers foundational Python concepts essential for beginners.

1. Python Variables & Data Types

1.1 Python Variables
1.2 Python Data Types
1.3 Python Numbers
1.4 Type Casting in Python
1.5 Python Strings
1.6 Python String Methods
1.7 Python Boolean

2. Python Control Statements

2.1 Python If else
2.2 Python Loops
2.3 Python For loop
2.4 Python While Loop
2.5 Python Continue
2.6 Python Break
2.7 Python Pass

3. Python Data Structures

3.1 Python Lists
3.2 Python List Methods
3.3 Python Tuples
3.4 Python Tuple Methods
3.5 Difference between List and Tuple
3.6 Python Sets
3.7 Python Set Methods
3.8 Python Dictionary
3.9 Python Dictionary Methods
3.10 Difference between List and Dictionary
3.11 Difference between List, Set, Tuple, and Dictionary
3.12 Difference between Sets and Dictionary

4. Python Functions

4.1 Python Built-in Functions
4.2 def Functions
4.3 Python Lambda Functions

5. Python Modules

5.1 Python List Comprehension
5.2 Python Collection Module
5.3 Python Math Module
5.4 Python OS Module
5.5 Python Random Module
5.6 Python Statistics Module
5.7 Python Sys Module

6. Python Object-Oriented Programming (OOPs)

6.1 Python OOPs Concepts
6.2 Python Classes and Objects
6.3 Constructors
6.4 Inheritance
6.5 Abstraction
6.6 Encapsulation
6.7 Access Modifiers

7. Exception Handling

7.1 Exception Handling Basics
7.2 Handling Multiple Exceptions
7.3 Raising an Exception
7.4 The finally Keyword
7.5 Built-in Exceptions

8. File Handling

8.1 Python Files I/O
8.2 Reading CSV Files
8.3 Writing CSV Files
8.4 Reading from Files
8.5 Writing to Files
8.6 JSON Handling
8.7 Context Managers in Python

9. Python Searching and Sorting

9.1 Searching Algorithms
- 9.2 Linear Search
- 9.3 Binary Search
9.4 Sorting Algorithms
- 9.5 Bubble Sort
- 9.6 Insertion Sort
- 9.7 Selection Sort
- 9.8 Merge Sort
- 9.9 Quick Sort
- 9.10 Heap Sort
- 9.11 Tim Sort

Advanced Topics

Arrays
Assertions
Command Line Arguments
Data Structures (Stacks and Queues)
Decorators
Generators
Grid Search in Python
High-Order Functions
IDEs
Iterator Tools
Magic Methods
Multiprocessing
NSE Tools in Python
Pyspark MLLib
Regular Expressions (Regex)
Sending Email
Web Scraping

Libraries – NumPy

NumPy is a fundamental library for numerical computing in Python.

Introduction
Ndarray Object
Array Creation
- Array From Existing Data
Array Attributes
Data Types
Indexing
- Slicing
- Slicing with Boolean Arrays
- Advanced Indexing
Array Manipulation
- Splitting Arrays
- Stacking Arrays
Arithmetic Operations
Binary Operations
Byte Swapping
Element-Wise Array Comparisons
Filtering and Joining Arrays
Functions
- Exponential Functions
- Hyperbolic Functions
- Logarithmic Functions
- Statistical Functions
- String Functions
Distributions
- Chi-Square Distribution
- Logistic Distribution
Polynomial Operations
- Polynomial Representation
Environment Setup
Visualizing Distributions with Seaborn

Python Interview Questions

A collection of questions and programs for interview preparation.

For Experienced Professionals
For Freshers
Programs for Interview Preparation

Libraries – Matplotlib

Matplotlib is a powerful plotting library for creating static, interactive, and animated visualizations in Python.

Introduction
Pyplot API
Simple Plot
Markers and Figures
Color Maps and their Normalization
Fonts
- Font Indexing
- Font Properties
Working with Text
- Subplot Titles
- Text Properties
- Mathematical Expressions
- LaTeX Text Formatting in Annotations
Images
- Image Masking
Scales
Plotting with Keywords
Matplotlib vs. Seaborn
Interactivity
- Annotated Cursor
- Cursor Widget
- Mouse Cursor
- Multi-cursor
- Ribbon Box
- Buttons Widget
- Menu Widget
- Radio Buttons
- Range Slider
- Polygon Selector
Object-Oriented Interface
Subplots
- Subplots() Function
- Subplot2Grid() Function
3D Plotting
- 3D Bar Plots
- 3D Scatter Plots
Plot Types
- Area Plot, Bar Plot
- Box Plot
- Heat Map
- Histogram
- Line Plot
- Pie Chart
- Scatter Plot
Utility
- Anaconda Distribution
- Jupyter Notebook
- Print Stdout
- Toolkits

Libraries – Pandas

Pandas is a fast, powerful, flexible, and easy-to-use open-source data analysis and manipulation tool.

Introduction
Index Objects
Series and Attributes of Series
- Slicing a Series Object
DataFrame
- Accessing DataFrame
- Arithmetic Operations on DataFrame
- Modifying DataFrame
- Removing Rows from a DataFrame
- Sorting and Reindexing
Indexing and Selecting Data
I/O Tools
- Reading and Writing Data to Excel
Iteration & Concatenation
Multi-Index
- Basics of Multi-Index
- Indexing with MultiIndex
Boolean Indexing
Boolean Masking
Categorical Data
- Categorical Data Handling
- Comparing Categorical Data
- Computing Dummy Variables
- Ordering and Sorting Categorical Data
Pivoting
Stacking and Unstacking
Missing Data
- Calculations in Missing Data
- Dropping Missing Data
- Filling Missing Data
- Interpolation of Missing Values
Duplicate Data Handling
Unique Elements
- Counting and Retrieving Unique Elements
Binary Comparison Operation

Library – SciPy

SciPy is a library used for scientific and technical computing, built on the NumPy array object.

Introduction and Basic Functionalities
Relationship with NumPy
Constants
- Mathematical Constants
- Physical Constants
- Unit Conversion
FFT Pack
- Discrete Fourier Transform
- Fast Fourier Transform
Integration
- Single Integration
- Double Integration
- Multiple Integration
- Integration of Ordinary Differential Equations
- Integration of Stochastic Differential Equations
- Discontinuous Functions
- Oscillatory Functions
Interpolation
- Linear 1-D Interpolation
- Polynomial 1-D Interpolation
- Linear Curve and Non-Linear Curve Fitting
Statistics
- Continous Probability Distribution
- Discrete Probability Distribution
- Generating Random Survival Analysis
- Statistical Tests and Inference
Clustering
- K-Means Clustering
- Hierarchical Clustering
- Clusters

Statistics

This section covers fundamental statistical concepts relevant to data science.

The Foundation of Data Science

Understanding different types of data and their properties.

Qualitative Data or Categorical Data:
- 1.1.1 Nominal Data
- 1.1.2 Ordinal Data
1.2 Quantitative Data
1.3 Univariate Data
1.4 Bivariate Data
1.5 Multivariate Data
1.6 Time Series Data
1.7 Cross-Section Data
Binomial Data
Box Plot
Population and Sample
Sampling Techniques

Scales of Measurement in Business Statistics

2.1 Nominal Scale
2.2 Ordinal Scale
2.3 Interval Scale
2.4 Ratio Scale

3. Relationship between AM, GM, and HM

3.1 Arithmetic Mean (AM)
3.2 Geometric Mean (GM)
3.3 Harmonic Mean (HM)
3.4 Relationship between AM, GM, and HM

4. Skewness – Measures and Interpretation

4.1 What is Skewness?
4.8 Measurement of Skewness
- 4.3 Skewness of Karl Pearson’s Measure
- 4.9 Karl Pearson’s Measure
- 4.10 Bowley’s Measure
- 4.11 Kelly’s Measure
4.4 Positive and Negative Skewness
- 4.5 Positive Skewness (Right Skew)
- 4.6 Negative Skewness (Left Skew)
- 4.7 Zero Skewness (Symmetrical Distribution)
4.2 Tests of Skewness
4.12 Interpretation of Skewness
4.13 Difference between Dispersion and Skewness

5. What is a Regression Line?

5.1 What is a Regression Line?
5.2 Equation of Regression Line
5.3 Graphical Representation of Regression Line
5.4 Examples of Regression Line

6. Types of Regression Lines

6.1 Linear Regression Line
6.2 Logistic Regression Line
6.3 Polynomial Regression Line
6.4 Ridge and Lasso Regression
6.5 Non-Linear Regression Line
6.6 Multiple Regression Line
6.7 Exponential Regression Line
6.8 Piecewise Regression Line
6.9 Time Series Regression Line
6.10 Power Regression Line
6.11 Applications of Regression Line
6.12 Importance of Regression Line
6.13 Statistical Significance of Regression Line
6.14 Practice Questions on Regression Line

7. Probability Theorems | Theorems and Examples

7.1 What is Probability?
7.2 Probability Theorems
7.3 Theorem of Complementary Events
7.4 Theorem of Addition
7.5 Theorem of Multiplication (Statistical Independence)
7.6 Theorem of Total Probability

8. Tree Diagram: Meaning, Features, Conditional Probability, and Examples

8.1 What is a Tree Diagram?
8.2 Features of Tree Diagram
8.3 How to Draw a Tree Diagram?
8.4 Tree Diagram for Conditional Probability
8.5 Tree Diagram in Probability Theory
8.6 Examples of Tree Diagram

9. Joint Probability | Concept, Formula, and Examples

9.1 What is Joint Probability in Business Statistics?
9.2 Difference between Joint Probability and Conditional Probability
9.3 Probability Density Function (PDF): Meaning, Formula, and Graph
- 9.4 What is the Probability Density Function?
- 9.5 Probability Density Function Formula
- 9.6 Properties of Probability Density Function
- 9.7 Probability Distribution Function of Discrete Distribution
- 9.8 Probability Distribution Function of Continuous Distribution

10. Bivariate Frequency Distribution | Calculation, Advantages, and Disadvantages

10.1 Definition: Tabular representation showing frequencies for combinations of two variables.
10.2 Components: Includes joint, marginal, and conditional frequencies.
10.3 Construction: Prepare class intervals for both variables and fill the frequency table.
10.4 Graphical Representation: Use scatter plots or heatmaps to visualize relationships.
10.5 Calculation: Compute joint frequencies and use them to find correlation or association.
10.6 Advantages
10.7 Disadvantages

11. Bernoulli Distribution in Business Statistics – Mean and Variance

11.1 Terminologies Associated with Bernoulli Distribution
11.2 Formula of Bernoulli Distribution
11.3 Mean and Variance of Bernoulli Distribution
11.4 Properties of Bernoulli Distribution
11.5 Bernoulli Distribution Graph
11.6 Bernoulli Trial
11.7 Examples of Bernoulli Distribution
11.8 Applications of Bernoulli Distribution in Business Statistics
11.9 Bernoulli Distribution and Binomial Distribution

12. Binomial Distribution in Business Statistics – Definition, Formula & Examples

12.1 Formula of Binomial Distribution
12.2 Properties of Binomial Distribution
12.3 Negative Binomial Distribution
12.4 Mean and Variance of Binomial Distribution
12.5 Shape of Binomial Distribution
12.6 Solved Examples of Binomial Distribution
12.7 Uses of Binomial Distribution in Business Statistics
12.8 Real-Life Scenarios of Binomial Distribution
12.9 Difference Between Binomial Distribution and Normal Distribution

13. Geometric Mean in Business Statistics | Concept, Properties, and Uses

13.1 Weighted Geometric Mean
13.2 Properties of Geometric Mean
13.3 Uses of Geometric Mean

14. Negative Binomial Distribution: Properties, Applications, and Examples

14.1 Properties of Negative Binomial Distribution
14.2 Probability Density Function (PDF) of Negative Binomial Distribution
14.3 Mean and Variance of Negative Binomial Distribution
14.4 Applications of Negative Binomial Distribution in Business Statistics
14.5 Examples of Negative Binomial Distribution

15. Hypergeometric Distribution in Business Statistics: Meaning, Examples & Uses

15.1 Probability Density Function (PDF)
15.2 Mean and Variance
15.3 Examples of Hypergeometric Distribution
15.4 When to Use the Hypergeometric Distribution?
15.5 Difference Between Hypergeometric Distribution and Binomial Distribution
15.6 Conclusion

16. Poisson Distribution: Meaning, Characteristics, Shape, Mean, and Variance

16.1 Probability Distribution Function (PDF) of Poisson Distribution
16.2 Characteristics of Poisson Distribution
16.3 Shape of Poisson Distribution
16.4 Mean and Variance of Poisson Distribution
16.5 Fitting a Poisson Distribution
16.6 Poisson Distribution as an Approximation to Binomial Distribution
16.7 Examples of Poisson Distribution

17. Gamma Distribution in Statistics

17.1 What is Gamma Distribution?
17.2 Gamma Distribution Function
17.3 Gamma Distribution Formula – Probability Density Function (PDF)
17.4 Gamma Distribution Mean and Variance
17.5 Special Case 1: Exponential Distribution
- 17.6 Examples of Exponential Distribution
17.7 Special Case 2: Chi-Square Distribution with Parameter “Degrees of Freedom”
- 17.8 Examples of Chi-Square Distribution

18. Normal Distribution in Business Statistics

18.1 Probability Density Function (PDF) of Normal Distribution
18.2 Standard Normal Distribution
18.3 Properties of Normal Distribution
18.4 The Empirical Rule
18.5 Parameters of Normal Distribution
18.6 Curve of Normal Distribution
18.7 Examples of Normal Distribution
18.8 Applications of Normal Distribution in Business Statistics

19. Lognormal Distribution in Business Statistics

19.1 Probability Density Function (PDF) of Lognormal Distribution
19.2 Lognormal Distribution Curve
19.3 Mean and Variance of Lognormal Distribution
19.4 Applications of Lognormal Distribution
19.5 Examples of Lognormal Distribution
19.6 Difference Between Normal Distribution and Lognormal Distribution

20. Inferential Statistics

20.1 Overview of Inferential Statistics
20.2 Degrees of Freedom
20.3 Central Limit Theorem
20.4 Parameters vs. Test Statistics
20.5 Test Statistics
20.6 Estimation
20.7 Standard Error
20.8 Confidence Interval

21. Hypothesis Testing

21.1 Hypothesis Testing Guide
21.2 Null and Alternative Hypothesis
21.3 Statistical Significance
21.4 P-Value
21.5 Type I & Type II Errors
21.6 Statistical Power
Decisions

22. Choosing the Right Test

22.1 Assumptions of Hypothesis Testing
- 22.1.1 Skewness
- 22.1.2 Kurtosis
22.2 Correlation
- Correlation Coefficient
- Correlation vs. Causation
- Pearson Correlation
- Covariance vs. Correlation
22.3 Regression Analysis
- 22.3.1 t-Test
- 22.3.2 ANOVA (Analysis of Variance)
  - 22.3.2.1 One-Way ANOVA
  - 22.3.21 Two-Way ANOVA
  - 22.3.2.3 ANOVA in R
22.4 Chi-Square Test
- 22.4.1 Overview of Chi-Square Test
- 22.4.2 Chi-Square Goodness of Fit Test
- 22.4.3 Chi-Square Test of Independence

Graphical Representation of Variables

Graphs and Tables

Measures of Central Tendency

Mean
Median
Mode

Machine Learning

This section provides an introduction to machine learning concepts and algorithms.

1. Introduction to Machine Learning

What is Machine Learning?
Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Semi-Supervised Learning
Note on Self-Supervised Learning

2. Machine Learning Pipeline

ML Workflow Overview
Data Cleaning
Data Preprocessing in Python
Feature Scaling

3. Supervised Learning

Classification vs. Regression
Decision Trees
Ensemble Learning
k-Nearest Neighbors (k-NN)
Linear Regression
Logistic Regression
Naïve Bayes
Random Forest
Support Vector Machines (SVM)

4. Unsupervised Learning

Categories
Clustering Algorithms
- DBSCAN (Density-Based Clustering)
- Hierarchical Clustering
- k-Means Clustering
Dimensionality Reduction
- Principal Component Analysis (PCA)
- t-SNE (t-distributed Stochastic Neighbor Embedding)
Association Rule Mining
Autoencoders (Neural Networks)

5. Reinforcement Learning

Model-Based Methods
Model-Free Methods
Actor-Critic Methods
Deep Q-Networks (DQN)
Monte Carlo Methods
Proximal Policy Optimization (PPO)
Q-Learning
SARSA (State-Action-Reward-State-Action)
Bayesian Networks
Hidden Markov Models

6. Semi-Supervised Learning

Overview and Use Cases
Techniques
- Self-training
- Co-training
- Graph-based Methods
- Semi-Supervised Support Vector Machines (S3VM)
Generative Adversarial Networks (GANs)
Generative Models like Variational Autoencoders (VAEs)

7. Deployment of ML Models

Flask & FastAPI for APIs
Gradio UIs for Prototyping
Heroku Deployment
MLOps & CI/CD Integration
Streamlit Deployment

Computer Vision

This section explores the fundamentals and advanced topics in computer vision.

Chapter 1: Introduction to Computer Vision

What is Computer Vision? Applications & History
A Quick Overview of Computer Vision
Applications of Computer Vision
Fundamentals of Image Formation
Satellite Image Processing

Chapter 2: Image Basics

RGB, Grayscale, Binary Images
Difference Between RGB, CMYK, HSV, and YIQ Color Models
Image I/O using OpenCV and PIL
Hands-on: Load, Display, and Save Images

Chapter 3: Image Processing Fundamentals

Smoothing Techniques (Gaussian, Median)
Edge Detection (Sobel, Canny)
Morphological Operations
Thresholding and Histograms
Convolution and Filtering
Hands-on: Build Your Own Image Filters in Python

Chapter 4: Feature Extraction & Matching

Feature Detection and Matching with OpenCV-Python
Harris Corner Detector, FAST, SIFT, ORB
Create Local Binary Pattern of an Image using OpenCV-Python
Feature Descriptors and Matching
Feature Matching using Brute Force in OpenCV
Feature Matching using ORB Algorithm in Python-OpenCV
Image Stitching (Panorama)
Mahotas – Speeded-Up Robust Features
Hands-on: Keypoint Detection and Feature Matching

Chapter 5: Geometric Vision

Camera Calibration (Intrinsic/Extrinsic Parameters)
Homography, Affine, and Projective Transforms
Epipolar Geometry, Stereo Vision
Depth Estimation Basics
Camera Calibration with Python – OpenCV
Hands-on: Perspective Correction and Camera Calibration
Python OpenCV – Depth Map from Stereo Images
Python OpenCV – Pose Estimation

Chapter 6: Motion & Tracking

Background Subtraction, Frame Differencing
Kalman Filter, Optical Flow (Lucas-Kanade, Farneback)
Hands-on: Build a Basic Tracking System on Video

Chapter 7: Classical Object Detection

HOG + SVM (Face, Pedestrian Detection)
Sliding Window + Image Pyramid Approach
Viola-Jones for Face Detection

Chapter 8: Introduction to CNNs

What are CNNs? Layers, Kernels, Activation
Convolutional Neural Network (CNN) Architectures
Continuous Kernel Convolution
ML | Introduction to Strided Convolutions
CNN | Introduction to Padding
What is the Difference Between ‘SAME’ and ‘VALID’ Padding in tf.nn.max_pool of TensorFlow?
CNN | Introduction to Pooling Layer
Dilated Convolution
Hands-on: Image Classification Using Pre-trained CNN (e.g., ResNet18)

Chapter 9: CNN Architectures & Applications

Architectures: LeNet, AlexNet, VGG, ResNet
Introduction to Residual Networks
Residual Networks (ResNet) – Deep Learning
ML | Inception Network V1
VGG-16 | CNN Model
Image Recognition with MobileNet
Deep Transfer Learning – Introduction
What is Transfer Learning?
Top 5 PreTrained Models in Natural Language Processing (NLP)

Chapter 10: Representation Learning & Generative Models

Autoencoders in Machine Learning
How Autoencoders Work?
Difference Between Encoder and Decoder
Generative Adversarial Network (GAN)
Deep Convolutional GAN with Keras
StyleGAN – Style Generative Adversarial Networks
Implementing an Autoencoder in PyTorch

Chapter 11: Object Detection with Deep Learning

CNN-based Detectors: R-CNN, Fast R-CNN, Faster R-CNN
YOLO (v5/v8), SSD, RetinaNet
Data Annotation, Bounding Boxes
Hands-on: Object Detection with YOLOv5

Chapter 12: Semantic & Instance Segmentation

FCN, U-Net, DeepLab
Mask R-CNN
Use-Cases: Medical Imaging, Autonomous Driving
Hands-on: Segmentation Using U-Net on Biomedical Images

Chapter 13: OCR Fundamentals

Tesseract OCR, EasyOCR
Text Localization: EAST/CRAFT Detectors
Table Detection and Structure Recognition
LayoutLM, Donut for Document Understanding
Hands-on: Extract Text and Tables from Invoices or Forms

Chapter 14: Vision Transformers

Attention Mechanism Recap
Vision Transformer (ViT), DeiT
DETR for Object Detection
SAM (Segment Anything Model)
Hands-on: Try ViT and DETR on Custom Datasets

Chapter 15: Model Optimization & Edge Deployment

Quantization, Pruning
TensorRT, ONNX, OpenVINO
Real-time Webcam Inference
Hands-on: Deploy YOLO on a Webcam Using ONNX/TensorRT

Chapter 16: Capstone Projects

Defect Detection in Manufacturing
Document Workflow Automation
License Plate Recognition
Retail Analytics (People Counting, Shelf Monitoring)

Chapter 17: Advanced Topics

3D Reconstruction
Augmented Reality
SLAM (Simultaneous Localization and Mapping)
Face Recognition (Using FaceNet / Dlib)
Dataset Creation Tools: CVAT, LabelImg, Roboflow

Tools & Libraries

Deep Learning Frameworks: PyTorch or TensorFlow/Keras
Computer Vision Libraries: OpenCV, scikit-image, PIL
Deployment & Prototyping: Streamlit / Flask (for Basic Demos)
OCR & NLP Integration: Tesseract, EasyOCR, LayoutLM, Hugging Face Transformers
Optimization & Inference: Detectron2, Ultralytics YOLO, ONNX, TensorRT, OpenVINO

OpenCV

Introduction
OPenCV-Python Bindings
Image Processing in OpenCV
Core Operations
Feature Detection and Description
Object Detection
GUI Features in OPenCV
Computational Photography

Natural Language Processing (NLP)

This section covers the fundamentals and applications of Natural Language Processing.

1. Introduction to NLP

What is NLP?
Applications of NLP
Who Should Use This Guide?

2. Components of NLP

Natural Language Understanding (NLU)
Natural Language Generation (NLG)

3. NLP Libraries

NLTK (Natural Language Toolkit)
spaCy
Gensim
Transformers (Hugging Face)

4. Text Normalization

Tokenization
Stopword Removal
Stemming
Lemmatization
Parts of Speech (POS) Tagging
Regular Expressions (RE)

5. Text Representation Techniques

Bag of Words (BoW)
TF-IDF (Term Frequency–Inverse Document Frequency)
N-Grams
N-Gram Language Modeling
One-Hot Encoding

6. Text Embedding Techniques

Word Embeddings
Document Embedding
Pre-Trained Embeddings

7. Deep Learning Techniques for NLP

Recurrent Neural Networks (RNN)
Long Short-Term Memory (LSTM)
Gated Recurrent Unit (GRU)
Artificial Neural Networks (ANN)
Seq2Seq Models
Transformer Models

8. Pre-Trained Language Models

GPT (Generative Pre-trained Transformer)
BERT (Bidirectional Encoder Representations from Transformers) - Implied, often discussed with Transformers
RoBERTa
T5 (Text-To-Text Transfer Transformer)
Transformer XL
Fine-Tuning Pre-Trained Models