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Data Science Demystified

  • Data_Science_Demystified
  • 1. Mathematics Fundamentals
    • 1.1. Linear Algebra Fundamentals
      • 1.1.1. MindMap
      • 1.1.2. Linear Algebra Transformations
      • 1.1.3. Eigen Values and Vectors
      • 1.1.4. Decomposition Techniques
      • 1.1.5. Advanced Topics
      • 1.1.6. Applications
    • 1.2. Calculus
      • 1.2.1. Calculus Fundamentals
      • 1.2.2. Functions and Derivatives
      • 1.2.3. Maxima and Minima
      • 1.2.4. Function Chaining
      • 1.2.5. Applications in Data Science
    • 1.3. Optimization
  • 2. ML Problem Approach
    • 2.1. Hypothesis Generation
      • 2.1.1. Hypothesis Introduction
      • 2.1.2. Characteristics of a Good Hypothesis
      • 2.1.3. Significance of Hypothesis Generation
      • 2.1.4. Hypothesis Generation Frameworks
        • 2.1.4.1. Issue Tree
        • 2.1.4.2. Mind Map
        • 2.1.4.3. Fishbone Diagram
        • 2.1.4.4. Pyramid Principle
    • 2.2. Data Understanding
      • 2.2.1. Exploratory Analysis
        • 2.2.1.1. Comparitive Analysis
        • 2.2.1.2. Distribution Analysis
        • 2.2.1.3. Correlation Analysis
        • 2.2.1.4. Trend Analysis
        • 2.2.1.5. Variable Importance
        • 2.2.1.6. Principal Component Analysis
      • 2.2.2. Domain Driven Analysis
    • 2.3. Feature Engineering
      • 2.3.1. Feature Encoding
      • 2.3.2. Feature Transformation
      • 2.3.3. Feature Scaling
      • 2.3.4. Feature Discretization
    • 2.4. Feature Selection
      • 2.4.1. Filter Approach
      • 2.4.2. Wrapper Approach
      • 2.4.3. Embedded Approach
      • 2.4.4. Hybrid Approach
    • 2.5. Handling Missing Data
      • 2.5.1. Missing Data Pattern Analysis
      • 2.5.2. Missing Data Deletion
      • 2.5.3. Missing Data Imputation
        • 2.5.3.1. Simple Imputation
        • 2.5.3.2. Multiple Imputation
        • 2.5.3.3. Model Based Imputation
    • 2.6. Handling Outliers
      • 2.6.1. Detecting Outliers
      • 2.6.2. Outlier Treatment
    • 2.7. Hyper Parameter Optimization
      • 2.7.1. Grid Search
      • 2.7.2. Random Search
      • 2.7.3. Bayesian Search
    • 2.8. Model Explainability
      • 2.8.1. Local Explainability
      • 2.8.2. Global Explainability
    • 2.9. Model Validation
      • 2.9.1. Cross Validation
      • 2.9.2. Evaluation Metrics
      • 2.9.3. Learning Curves
      • 2.9.4. Model Validation
  • 3. Statistics
    • 3.1. Probability
      • 3.1.1. Key Ideas
      • 3.1.2. Probability Axioms
      • 3.1.3. Types of Probability
      • 3.1.4. Frequentist vs Bayesian Approach
    • 3.2. Sampling
      • 3.2.1. Types of Sampling
      • 3.2.2. Sampling
    • 3.3. Descriptive Statistics
      • 3.3.1. Concepts
      • 3.3.2. Types of Variables
      • 3.3.3. Data Distribution
      • 3.3.4. Measures of Central Tendency
    • 3.4. Inferential Statistics
      • 3.4.1. Core Ideas
      • 3.4.2. Hypothesis Testing
      • 3.4.3. Confidence Interval
      • 3.4.4. Central Limit Theorem
      • 3.4.5. Correlation vs Causation
    • 3.5. Hypothesis Testing
      • 3.5.1. Core Ideas
      • 3.5.2. Parametric Tests
      • 3.5.3. Non Parametric Tests
  • 4. Regression
    • 4.1. Core Ideas
    • 4.2. Model Metrics
    • 4.3. Model Selection
    • 4.4. Widely Used Techniques
      • 4.4.1. Linear Models
      • 4.4.2. Non Linear Models
  • 5. Classification Technique
    • 5.1. Classification Concept
    • 5.2. Classification Metrics
    • 5.3. Multi Class Classification
    • 5.4. Multi Label Classification
    • 5.5. Classification Model Selection
    • 5.6. Widely Used Methods
      • 5.6.1. Logistic Regression
      • 5.6.2. Naive Bayes
      • 5.6.3. SVM (Support Vector Machine)
      • 5.6.4. KNN (K-Nearest Neighbors Algorithm)
    • 5.7. DECISION TREE
      • 5.7.1. ID3
      • 5.7.2. CART
      • 5.7.3. CHAID
    • 5.8. Ensemble
      • 5.8.1. Bagging (Bootstraped Aggregation)
      • 5.8.2. Boosting
  • 6. Recommendation System
    • 6.1. Collaborative Based Filtering
    • 6.2. Content Based Filtering
    • 6.3. Evalation of Recommender Algorithms
    • 6.4. Singular Value Decomposition
  • 7. Unsupervised Learning
    • 7.1. Concepts
    • 7.2. Gaussian-Mix Model (EM)
    • 7.3. PCA (Principle Component Analysis)
    • 7.4. Self Organizing Maps (SOM)
    • 7.5. UL Evaluation Metrics
    • 7.6. Clustering
      • 7.6.1. Fuzzy Clustering
      • 7.6.2. Hierarchical Clusterr
      • 7.6.3. K-Means
      • 7.6.4. K-Medians
  • 8. Time Series Analysis
    • 8.1. Time Series Concepts
    • 8.2. AutoCorrelation And Partial AutoCorrelation
    • 8.3. Classical Time Series Models
      • 8.3.1. Univariate Time Series
        • 8.3.1.1. ARIMA
        • 8.3.1.2. SARIMA
        • 8.3.1.3. ETS
        • 8.3.1.4. Prophet
        • 8.3.1.5. UCM
        • 8.3.1.6. Croston
      • 8.3.2. Multivariate Time Series
        • 8.3.2.1. ARIMAX
        • 8.3.2.2. VAR
        • 8.3.2.3. VARMA & VARMAX
        • 8.3.2.4. VECM
    • 8.4. Stationarity Tests
    • 8.5. Stationary vs Non Stationary Time Series
    • 8.6. Time Series Decomposition
    • 8.7. Advanced Time Series
      • 8.7.1. FB Prophet
      • 8.7.2. Gluon TS
      • 8.7.3. GreyKite
    • 8.8. Time Series Forecasting using Neural Networks
      • 8.8.1. LSTM
      • 8.8.2. RNN
    • 8.9. TS Evaluation Metrics
  • 9. Deep Learning
    • 9.1. CNN
    • 9.2. RNN
    • 9.3. LSTM
  • 10. NLP
    • 10.1. Represenation Methods
      • 10.1.1. BOW
    • 10.2. Pre-Processing
    • 10.3. Word2Vec
    • 10.4. NLP Applications
  • 11. DS Project Management Framework
    • 11.1. CRISP DM
    • 11.2. SEMMA
    • 11.3. KDD
  • 12. MLOps
    • 12.1. MLOps Overview
    • 12.2. MLOps Landscape
    • 12.3. DevOps vs MLOps
    • 12.4. Feature Stores
    • 12.5. Model Governance
    • 12.6. Model Monitoring
    • 12.7. Model Deployment
  • Repository
  • Open issue
  • .md

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2.7.2. Random Search#

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2.7.1. Grid Search

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By Abenav S

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