Vidit Sheth | Machine Learning Concepts
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Machine Learning Concepts

Machine Learning Concepts

Machine Learning Concepts
Machine Learning Concepts

Vidit Sheth

Home / Vidit Sheth
BySourabh Gupta March 18, 2022
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Vidit Sheth
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Backward Propagation in Artificial Neural Network
5 months ago in: gradient descent, ml models, ml-tutorials no comments
  • Links to Machine Learning Datasets
  • Data Preprocessing in Machine Learning
    • Handling Missing Data
    • Types of Missing Data
    • Handling Missing Data
  • Imputation Methods
  • Model-based methods
  • Outliers
    • Univariate Analysis
    • Bivariate and Multivariate outliers
  • Feature Selection
    • Basic Filter Methods
    • Correlation Filter Methods
    • Chi-squared Score                                                      
      • ANOVA
    • Dimensionality Reduction Method
    • Wrapper Methods
      • Forward selection
      • Step Backward Feature Selection
      • C. Recursive Feature Elimination
    • 3. Embedded Methods
  • 2. Data Integration
  • Data Transformation
  • Data Normalization
    • 1. Decimal Scaling
    • Min-Max Normalization
    • Z-Score Normalization
  • Dimensionality Reduction Method
    • I. Principal Component Analysis (PCA)
    •  II. Multicollinearity – VIF
  • MACHINE LEARNING MODELS
    • I. Supervised Learning
      • From zero to hero in Regression Analysis
      • 1.Decision Tree
      • 2. Ensemble Learning Methods
        • A. BAGGING
          • 1. Random forest
        • BOOSTING
          • AdaBoost
          • Gradient Boosting
          • XGBoost
    • II. Unsupervised Learning – Clustering
  • How to reduce Overfitting?
    • 1) Reduce Overfitting: Using Regularization
    • 2) Reduce overfitting: Feature reduction and Dropouts
    • 3) Pruning to Reduce Overfitting
    • 4) Cross-validation to reduce Overfitting
  • Confusion Matrix for Model Selection
  • Accuracy, Specificity, Precision, Recall, and F1 Score for Model Selection
  • A simple review of Term Frequency – Inverse Document Frequency
  • A review of MNIST Dataset and its variations
  • Everything you need to know about Reinforcement Learning
  • The statistical analysis t-test explained for beginners and experts
  • Types of Autoencoders
  • Everything about Autoencoders
  • Radial Basis Function
  • ResNet or Residual Network
  • Processing Textual Data – An introduction to Natural Language Processing
  • Collaborative Filtering
  • Everything you need to know about Model Fitting in Machine Learning
  • Cost Functions
  • Maximum likelihood estimation
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