vjhgh

All_ML_session

🌳 AI, ML, and Neural Networks – Detailed Family Tree

Artificial Intelligence (AI)
│
├── Machine Learning (ML)
│   │
│   ├── Supervised Learning
│   │   ├── Regression
│   │   │   ├── Linear Regression
│   │   │   ├── Polynomial Regression
│   │   │   └── Ridge/Lasso Regression
│   │   └── Classification
│   │       ├── Logistic Regression
│   │       ├── Decision Tree Classifier
│   │       ├── Random Forest
│   │       ├── K-Nearest Neighbors (KNN)
│   │       ├── Support Vector Machine (SVM)
│   │       └── Naive Bayes
│   │
│   ├── Unsupervised Learning
│   │   ├── Clustering
│   │   │   ├── K-Means Clustering
│   │   │   ├── DBSCAN
│   │   │   └── Hierarchical Clustering
│   │   ├── Dimensionality Reduction
│   │   │   ├── PCA (Principal Component Analysis)
│   │   │   ├── t-SNE
│   │   │   └── Autoencoders (Neural Net based)
│   │   └── Association Rule Learning
│   │       ├── Apriori
│   │       └── Eclat
│   │
│   └── Reinforcement Learning
│   |   ├── Model-Free Methods
│   |   │   ├── Q-Learning
│   |   │   └── SARSA
│   |   └── Deep Reinforcement Learning
│   |       ├── Deep Q-Network (DQN)
│   |       ├── Proximal Policy Optimization (PPO)
│   |       └── A3C (Asynchronous Advantage Actor-Critic)
│   |
|   |
|   ├── Federated Learning
│   │   └── ML on decentralized data (e.g., smartphones)
│   │
│   ├── Transfer Learning
│   │   └── Uses a pre-trained model on new tasks
│   │       └── e.g., ResNet, BERT, GPT fine-tuning
│   │
│   └── Meta Learning
│       └── "Learning to learn" (e.g., few-shot learning)
|
|
|
└── Neural Networks (Subset of ML)
    │
    ├── Shallow Neural Networks
    │   └── Single Hidden Layer Perceptron
    │
    └── Deep Learning (Deep Neural Networks)
        │
        ├── Feedforward Neural Network (FNN)
        │   └── Also called MLP (Multilayer Perceptron)
        │
        ├── Convolutional Neural Network (CNN)
        │   ├── Image Classification
        │   ├── Object Detection
        │   └── Image Segmentation
        │
        ├── Recurrent Neural Network (RNN)
        │   ├── LSTM (Long Short-Term Memory)
        │   ├── GRU (Gated Recurrent Unit)
        │   └── Applications: time series, speech, NLP
        │
        ├── Transformer Networks
        │   ├── BERT
        │   ├── GPT (like ChatGPT)
        │   └── Used in: NLP, translation, summarization
        │
        ├── Autoencoders
        │   ├── Denoising Autoencoder
        │   └── Variational Autoencoder (VAE)
        │
        └── Generative Adversarial Networks (GANs)
            ├── Generator
            └── Discriminator

🔄 Encoders in Machine Learning

Encoders
│
├── 1. Categorical Encoders
│   │
│   ├── 1.1 Label Encoding
│   │   └── Assigns a unique integer to each category
│   │       Example: red=0, green=1, blue=2
│   │
│   ├── 1.2 One-Hot Encoding
│   │   └── Creates binary columns for each category
│   │       Example: red = [1, 0, 0], green = [0, 1, 0]
│   │
│   ├── 1.3 Ordinal Encoding
│   │   └── Assigns ordered integers based on rank/priority
│   │       Example: small=1, medium=2, large=3
│   │
│   ├── 1.4 Binary Encoding
│   │   └── Converts categories to binary code
│   │       More compact than One-Hot for high-cardinality data
│   │
│   ├── 1.5 Frequency Encoding
│   │   └── Replaces category with frequency count
│   │       Example: red=30, green=20 (based on occurrence)
│   │
│   ├── 1.6 Count Encoding
│   │   └── Replaces each category with number of times it appears
│   │
│   ├── 1.7 Target Encoding (Mean Encoding)
│   │   └── Replace category with average target value
│   │       Example: average sales per city
│   │
│   ├── 1.8 Hash Encoding (Feature Hashing)
│   │   └── Uses hash function to encode category into fixed-length vector
│   │
│   └── 1.9 Leave-One-Out Encoding
│       └── Like target encoding but leaves out current row's target
│
├── 2. Text Encoders (for NLP)
│   │
│   ├── 2.1 Bag of Words (BoW)
│   │   └── Vector of word counts across document
│   │
│   ├── 2.2 TF-IDF (Term Frequency-Inverse Document Frequency)
│   │   └── Weights words by frequency and uniqueness
│   │
│   ├── 2.3 Word Embeddings
│   │   ├── Word2Vec
│   │   ├── GloVe
│   │   └── FastText
│   │
│   ├── 2.4 Sentence Embeddings
│   │   ├── Universal Sentence Encoder (USE)
│   │   ├── BERT Embeddings
│   │   └── SBERT (Sentence-BERT)
│   │
│   └── 2.5 Tokenizer-based Encoders
│       ├── Byte Pair Encoding (BPE)
│       ├── WordPiece
│       └── SentencePiece
│
├── 3. Image Encoders (in Deep Learning)
│   │
│   ├── 3.1 CNN-based Encoders
│   │   └── Encodes image into feature maps
│   │
│   ├── 3.2 Pre-trained CNN Encoders
│   │   ├── VGG
│   │   ├── ResNet
│   │   └── EfficientNet
│   │
│   └── 3.3 Vision Transformer (ViT) Encoders
│       └── Tokenizes and encodes image patches using attention
│
├── 4. Sequence Encoders (for sequential/time-series data)
│   │
│   ├── 4.1 RNN Encoder
│   ├── 4.2 LSTM Encoder
│   ├── 4.3 GRU Encoder
│   └── 4.4 Transformer Encoder
│       └── Used in BERT, GPT, T5, etc.
│
└── 5. Autoencoders (Unsupervised Feature Learning)
    │
    ├── 5.1 Vanilla Autoencoder
    │   └── Compress and reconstruct input
    │
    ├── 5.2 Denoising Autoencoder
    │   └── Learns to reconstruct input from noisy version
    │
    ├── 5.3 Sparse Autoencoder
    │   └── Enforces sparsity constraint in hidden layer
    │
    ├── 5.4 Variational Autoencoder (VAE)
    │   └── Learns probabilistic latent space
    │
    └── 5.5 Contractive Autoencoder
        └── Penalizes sensitivity to input changes

🧠 ENCODERS in Machine Learning & Deep Learning

ENCODING
├── 1. CATEGORICAL ENCODING
│   ├── 1.1. Nominal (No Order)
│   │   ├── One-Hot Encoding
│   │   ├── Binary Encoding
│   │   ├── Count/Frequency Encoding
│   │   ├── Hash Encoding
│   │   └── Mean Encoding
│   │
│   └── 1.2. Ordinal (With Order)
│       ├── Label Encoding
│       ├── Ordinal Integer Mapping
│       └── Target-Guided Ordinal Encoding
│
├── 2. TEXT ENCODING (NLP)
│   ├── Bag of Words (BoW)
│   ├── TF-IDF (Term Frequency - Inverse Document Frequency)
│   ├── Word Embeddings
│   │   ├── Word2Vec
│   │   ├── GloVe
│   │   └── FastText
│   └── Transformer-based
│       ├── BERT Embeddings
│       └── GPT-style Embeddings
│
├── 3. IMAGE ENCODING (CV)
│   ├── CNN Encoders
│   │   ├── ResNet
│   │   ├── VGG
│   │   └── EfficientNet
│   └── Vision Transformers (ViT)
│
├── 4. SEQUENCE ENCODING (Time-series / Speech / NLP)
│   ├── RNN-based
│   │   ├── Simple RNN
│   │   ├── LSTM
│   │   └── GRU
│   └── Transformer-based
│       ├── Positional Encoding
│       └── Attention Mechanisms
│
├── 5. AUTOENCODERS (Unsupervised Encoding)
│   ├── Vanilla Autoencoder
│   ├── Denoising Autoencoder
│   └── Variational Autoencoder (VAE)
│
└── 6. SPECIALIZED ENCODING
    ├── Embedding Layers (for DL models)
    ├── Learned Embeddings (e.g., TabTransformer)
    ├── Contrastive Encoders (e.g., SimCLR, BYOL)
    └── Self-Supervised Encoders (e.g., MAE, MoCo)

Introduction to Data Science & AI

Overview

This course provides a comprehensive introduction to Data Science, Artificial Intelligence (AI), and Machine Learning (ML). It covers foundational concepts, practical tools, and real-world applications, with a focus on Python programming. By the end of the course, you will be equipped to build, deploy, and interpret AI models.

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Course Structure

1. Introduction to Data Science & AI

   • Overview of Data Science, AI, and ML.
   • Real-world applications.
   • Role of Python in Data Science & AI.
   • Setting up the Python environment (Anaconda, Jupyter, VS Code).

2. Python for Data Science & AI

   • Python basics: Variables, Data Types, Operators.
   • Control Structures: Loops and Conditional Statements.
   • Functions, Modules, and File Handling.
   • Exception Handling & Best Practices.

3. Data Handling with NumPy & Pandas

   • Introduction to NumPy: Arrays, Operations, Broadcasting.
   • Pandas for Data Manipulation: Series, DataFrames.
   • Data Cleaning: Handling missing values, duplicates.
   • Data Transformation: Merging, Grouping, Pivoting.

4. Data Visualization

   • Matplotlib for Basic Plots (Line, Bar, Scatter, Pie).
   • Seaborn for Statistical Data Visualization.
   • Interactive Visualization with Plotly.

5. Exploratory Data Analysis (EDA)

   • Understanding Data Distributions.
   • Outlier Detection & Handling.
   • Feature Engineering & Scaling Techniques.
   • Correlation Analysis & Insights Extraction.

6. Introduction to Machine Learning

   • Supervised vs Unsupervised Learning.
   • ML Workflow: Problem Statement, Data Processing, Model Building.
   • Bias-Variance Tradeoff & Performance Metrics.
   • Overview of ML Libraries (Scikit-Learn, TensorFlow, PyTorch).

7. Regression Analysis

   • Linear Regression: Model, Assumptions, Implementation.
   • Multiple Linear Regression & Polynomial Regression.
   • Regularization Techniques: Ridge & Lasso.
   • Evaluating Regression Models.

8. Classification Techniques

   • Logistic Regression & Decision Boundaries.
   • k-Nearest Neighbors (k-NN) Algorithm.
   • Decision Trees & Random Forests.
   • Performance Metrics: Accuracy, Precision, Recall, AUC-ROC.

9. Feature Engineering & Selection

   • Handling Categorical Variables: Encoding Techniques.
   • Feature Scaling: Normalization & Standardization.
   • Feature Selection: PCA, LDA, Feature Importance.
   • Handling Imbalanced Data.

10. Ensemble Learning & Model Stacking

    • Bagging: Random Forest.
    • Boosting: AdaBoost, Gradient Boosting, XGBoost.
    • Stacking & Blending Techniques.
    • Hyperparameter Tuning with GridSearchCV & RandomizedSearchCV.

11. Unsupervised Learning

    • Clustering: k-Means, Hierarchical, DBSCAN.
    • Dimensionality Reduction: PCA, t-SNE, Autoencoders.

12. Natural Language Processing (NLP)

    • Text Processing: Tokenization, Lemmatization, Stemming.
    • Bag-of-Words & TF-IDF.
    • Sentiment Analysis & Text Classification.
    • Advanced NLP: Transformers, BERT, GPT.

13. Deep Learning

    • Neural Networks Fundamentals.
    • Convolutional Neural Networks (CNNs) for Image Processing.
    • Recurrent Neural Networks (RNNs) & LSTMs for Time-Series Data.
    • Generative AI & GANs.

14. Model Deployment & MLOps

    • Saving & Loading Models.
    • Deployment with Flask & FastAPI.
    • CI/CD Pipelines for ML Models.
    • Monitoring & Maintaining ML Models.

15. Advanced Topics

    • Time Series Analysis & Forecasting.
    • Reinforcement Learning (RL) Basics.
    • AI for Business Decision-Making.
    • Edge AI & IoT Applications.

16. Ethics & Compliance

    • Explainable AI: SHAP & LIME.
    • Ethical AI & Bias in Machine Learning.
    • GDPR, HIPAA, and AI Compliance.

17. Capstone Project

    • Hands-on Real-world Project.
    • Model Deployment & Performance Evaluation.
    • Presentation & Peer Review.
    • Certification & Career Guidance.

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Tools & Libraries

• Python Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, TensorFlow, PyTorch.
• NLP Libraries: NLTK, SpaCy, Hugging Face Transformers.
• Deployment Tools: Flask, FastAPI, Docker.
• Cloud Platforms: AWS, Azure, Google Cloud.

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Prerequisites

• Basic programming knowledge (preferably Python).
• Familiarity with high school-level mathematics (linear algebra, probability).

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Learning Outcomes

By the end of this course, you will:

• Understand the fundamentals of Data Science, AI, and ML.
• Be proficient in Python for data analysis and machine learning.
• Build, evaluate, and deploy machine learning models.
• Gain hands-on experience with real-world projects.
• Be prepared for a career in Data Science & AI.

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Certification

Upon successful completion of the course and capstone project, you will receive a certificate of completion.

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Contact

For inquiries, please contact [Your Name] at [Your Email].

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License

This course material is licensed under the MIT License.