mhddenno / legendary-ml

Trying some different ML techniques

Let's keep Track about what is happening in the World of AI

Classical Machine Learning

Topics

• Regression

  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Support Vector Regression (SVR)
  • Decision Tree Regression
  • Random Forest Regression

• Classification

  • Logistic Regression
  • K-Nearest Neighbors (K-NN)
  • Support Vector Machine (SVM)
  • Kernel SVM
  • Naive Bayes
  • Decision Tree Classification
  • Random Forest Classification

• Clustering

  • K-Means Clustering
  • Hierarchical Clustering

• Association Rule Learning

  • Apriori
  • Eclat

• Reinforcement Learning

  • Upper Confidence Bound (UCB)
  • Thompson Sampling

• Dimensionality Reduction

• Model Selection and Boosting

Tools

• scikit-learn

Deep Learning

Topics

• Neural Networks and Deep Neural Networks (DNNs)

• Convolutional Neural Networks (CNNs)

• Recurrent Neural Networks (RNNs)

• Long Short-Term Memory (LSTM)

• Gated Recurrent Unit (GRU)

• Autoencoders

• Generative Adversarial Networks (GANs)

• Transfer Learning

• Meta-Learning

• Attention Mechanisms

• Reinforcement Learning

• Explainable AI (XAI)

• Quantum Machine Learning

• Capsule Networks

• Neuroevolution

• Generative AI

  • LLMs

Tools

• Pytorch
• Tenserflow
• LangChain
• HuggingFace
• Lamini, Ollama

Use Cases

• Spaces in Hugging Face
• Langchain UCs

Data layer

mindmap
  root((Data Preprocessing))
    Scalling
    Spliting
    Encdoding
    Evaluation  
      Confusion Matrix
      R Square
    

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Statistics

mindmap
  root((Statistics))
    P_values
    T_test
    Variance
    Bias
    Backward Elimination

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ML Process

flowchart TD
    A(Load The Data) --> B(Clean the data)
    B --> C(Split into traning & test sets)
    C --> D(Build the model)
    D --> E(Train the model)
    E --> F(Make predictions)
    F --> G(Calculate performance metrics)
    G --> H(Make a verdict)

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