Data Science and GenAi

This repository contains comprehensive notes and code written during my journey to learn Data Science and AI. It is organized into various sections covering essential topics and concepts, providing a valuable resource for anyone interested in mastering Data Science.

Table of Contents

1. Introduction
2. Python Basics
3. Data Analysis
4. Data Visualization
5. Machine Learning
6. Deep Learning
7. Natural Language Processing (NLP)
8. Statistics
9. Feature Engineering and Exploratory Data Analysis (EDA)
10. Resources

Introduction

Welcome to my Data Science repository! This collection includes all the notes and code I have accumulated while learning Data Science. The purpose of this repository is to serve as a reference for myself and others interested in this field.

Python Basics

This section covers the fundamental concepts of Python programming necessary for data science, including:

• Variables and Data Types
• Control Structures
• Functions
• Libraries: NumPy, Pandas
• Modules and Packages
• File Handling
• Multi-processing and Multi-threading
• Object-Oriented Programming (OOP)
• MongoDB
• Web Scraping

Data Analysis

In this section, you will find notes and code related to data analysis, including:

• Data Cleaning
• Data Manipulation
• Exploratory Data Analysis (EDA)

Data Visualization

This section includes techniques and code for data visualization using Python libraries such as:

• Matplotlib
• Seaborn
• Plotly

Machine Learning

This section covers various machine learning algorithms and their implementation, including:

• Supervised Learning
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Support Vector Machines (SVM)
  • Naive Bayes
  • K-Nearest Neighbors (K-NN)
• Unsupervised Learning
  • K-Means Clustering
  • DBSCAN
  • Hierarchical Clustering
• Ensemble Techniques
  • Bagging
  • Random Forest
  • Boosting (AdaBoost, Gradient Boosting, XGBoost, LightGBM, CatBoost)
  • Stacking
• Dimensionality Reduction
  • Principal Component Analysis (PCA)
• Time Series Analysis

Deep Learning

This section delves into deep learning concepts and their practical applications using frameworks like TensorFlow and Keras, including:

• Artificial Neural Networks (ANNs)
  • Activation Functions
  • Forward and Backward Propagation
  • Implementing ANN with Keras
  • Optimization Techniques
• Convolutional Neural Networks (CNNs)
  • Pooling, Padding, and various CNN architectures (VGG, LeNet, AlexNet, Inception, ResNet)
  • Transfer Learning
• Recurrent Neural Networks (RNNs)
  • LSTM, GRU
• Generative Adversarial Networks (GANs)
• Object Detection (YOLO, Custom Models)

Natural Language Processing (NLP)

This section includes notes and code for various NLP tasks, including:

• Text Preprocessing
• Text Representation (Word Embeddings: Word2Vec, Doc2Vec)
• Text Classification
• Sequence Models (RNN, LSTM, GRU)
• Transformers
• Text Generation
• Named Entity Recognition (NER)
• Sentiment Analysis

Statistics

In this section, you will find comprehensive notes and materials on statistics, including:

• Descriptive Statistics
  • Measures of Central Tendency
  • Measures of Dispersion
• Probability Distributions
  • Normal Distribution, Binomial Distribution, Poisson Distribution
• Inferential Statistics
  • Hypothesis Testing
  • Z-test, T-test, Chi-Square Test, ANOVA
  • Confidence Intervals

Feature Engineering and Exploratory Data Analysis (EDA)

This section covers techniques for handling and preprocessing data, including:

• Handling Missing Values
• Handling Imbalanced Datasets
• Data Interpolation
• Handling Outliers
• Feature Scaling
• Feature Extraction
• Data Encoding
• Covariance and Correlation Analysis
• Various EDA Projects

Resources

Here you will find a list of resources, including books, tutorials, and articles that have been instrumental in my learning journey.

Faculty

Krish Naik , Nitesh (campusX) mainly and few other

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Note: This repository is a work in progress and will be updated continuously as I learn more about data science.