Mathematical Background

• Algorithm Analysis and Asymptotic Notations
  • Complexities of machine learning algorithms, analysis and benchmarking
• Probability Distribution and Statistics
  • Probabilistic Models, Distributions (mainly Normal), Central limit theorem and sampling, Confusion Matrix and Hypotheses.
• Vectors, Matrices and Linear Algebra
  • Vector Algebra and products , Matrix normalization and reduction, Vector Spaces.

Theory

• Supervised Learning
  • Regression
    • Linear
    • Logistic
  • Classification
• Unsupervised Learning
• Reinforcement Learning

Algorithms

• Classification
  • Linear
    • Support Vector Machines
    • Logistic Regression
  • Non Linear
    • Naive Bayes
    • K Nearest Neighbors
    • Decision Trees
      • Random Forest
    • K Means Clustering
    • Neural Networks
• Linear Regression
  • OLS
  • SGD
• Backpropagation
  • Gradient Descent

Concepts

• Probabilistic Model
• Loss Function (Bias)
• Cross Validation
  • Train-Test split
• Evaluation Metrics
  • Accuracy
  • Log Loss
  • Gini Coefficient
  • MSE, RMSE
  • F1 score - Precision and Recall