Index

I. Types of data
    1. Qualitative or Categorical data
         a) Nominal data
         b) Ordinal data
    2. Quantitative or Numerical data
         a) Discrete data
         b) Continuous data

II. Data distributions
    1. Fundamentals
         a. Fundamentals of distribution
         b. Measure of central tendency: mean, median, mode
         c. arithmetic mean, geometric. mean, harmonic mean
         d. Measure of spread: range, midrange, IQR, variance and standard deviation
         e. How shifting and scaling impacts mean, median, IQR & standard deviation
    2. When to use mode over mean ?
    3. Arithmetic, Geometric and Harmonic mean
    4. Marginal, conditional and joint distribution
         a. Marginal Distribution
         b. Conditional distribution
         c. joint distribution or Multivariate distribution
         d. Univariate Vs. Multivariate Distribution
         e. Multimodal distribution
    5. Normal Distribution
         a. Why normal distribution is so important ?
         b. Characteristics of normal distribution
         c. Standard Normal Distribution
         d. Z-score
         e. Use cases on Z-score
         f. Some problems on Z-score
         g. Probabilities corresponding to Z-score and areas
         h. Normal distribution Equation
         i. Questions
    6. Sampling Distribution

III. Hypothesis Testing
    1. Understanding Hypothesis Testing
         a. Why do we need hypothesis testing ?
         b. Understanding hypothesis testing
         c. Problem on hypothesis testing
    2. Power in Hypothesis testing
         a. Types of error in hypothesis testing
         b. Power in hypothesis testing
         c. Problem on power

IV. Linear Regression
    1. Fundamentals of Linear Regression
         a. Simple VS Multiple Linear Regression
         b. Correlation coefficient
         c. Residuals
         d. Coefficient of determination
    2. Ordinary Least Square Estimation
         a. Intuition behind equation
         b. Derive slope and intercept
    3. Gradient descent Estimation

V. Types of optimizers
    1. Batch Gradient Descent
    2. Stochastic Gradient Descent
    3. Mini-batch Gradient Descent
    4. Adagrad
    5. RMSprop (similar to Adadelta but uses RMS)
    6. Adam

VI. Vanishing and Exploding Gradients

VII. Bias and Variance
    1. Bias
    2. Variance
    3. Bias Variance Tradeoff

VIII. Regularization in Machine Learning
    1. Role Of Regularization
    2. Lasso Regularization – L1 Regularization
    3. Ridge Regularization – L2 Regularization
    4. Elastic Net Regression
    5. Difference between the L1 and L2 regularization

IX. Bagging vs Boosting in Machine Learning
    1. Bagging
    2. Boosting
    3. Differences Between Bagging and Boosting

X. Cross Validation in Machine Learning
    1. Holdout Validation
    2. LOOCV (Leave One Out Cross Validation)
    3. K-Fold Cross Validation
    4. Stratified Cross-Validation