Full Stack Data Science and AI Program Syllabus

Machine Learning

  • Introduction to Machine Learning
  • Exploratory Data Analysis(EDA)
  • Supervised Machine Learning (Regression)
  • Logistic Regression
  • Ordinal Regression
  • Na├»ve Bayes Classifier Algorithm
  • Support Vector Machine
  • Decision Tree
  • K-Nearest Neighbor
  • Random Forest
  • Bagging and Boosting
  • Dimensionality Reduction
  • Time Series Analysis
  • ARIMA, SARIMA and ARMA
  • Clustering
  • Hyper Parameter Optimization
  • Feature Engineering
  • Performance Evaluation
  • Flask