MACHINE LEARNING

• Supervised ML Models:
  i) Classification:
  • Linear Classification
  • Multiclass Prediction
  • Support Vector Machines (SVM)
  • Decision Trees
  • Random Forests
• ii) Regression:
  • Linear Regression
  • Logistic Regression
  • Support Vector Machines (SVM)
  • Polynomial Regression
  • Multiple Regression
• Unsupervised ML Models:
  i) Clustering Problems:
  • K-Means Clustering
  • K-Nearest Neighbors (KNN)
  • Decision Trees
  • Hierarchical Clustering
• ii) Dimensionality Reduction:
  • Principal Component Analysis (PCA)
  • t-SNE (t-distributed Stochastic Neighbor Embedding)
  • Autoencoders
• Reinforcement Learning Problems
  • Q-Learning
  • Deep Q-Networks (DQN)

Machine Learning Algorithms:

• Naive Bayes
• Gradient Boosting (XGBoost, CatBoost, LightGBM)
• Ensemble Learning (Bagging, Boosting)
• Neural Networks (Deep Learning Models)

• Cloud Platforms for Machine Learning

  • Amazon Web Services (AWS):
    • AWS SageMaker (for training and deploying ML models)
    • AWS Lambda (for serverless computing)
  • Google Cloud Platform (GCP):
    • Google Cloud AI Platform
    • Google BigQuery (for SQL-based data querying at scale)
  • Microsoft Azure:
    • Azure Machine Learning
    • Azure Data Lake

    • API Development for Machine Learning Models

      • Frameworks:
        • Flask
        • FastAPI
        • Django (with Django Rest Framework)
      • Deployment:
        • Model Deployment via REST APIs
        • Cloud-based Model Hosting (Heroku, AWS, Google Cloud)