Empowering Change Through Evidence, Innovation, and Partnership.
Empowering Change Through Evidence, Innovation, and Partnership.

Apply ADVANCED MACHINE LEARNING 2 Course

TOOLS/FRAMEWORKS: Python

Duration: 2 Weeks

Cost: $100

MODULE 6
BOOSTING: INTRODUCTION, ADABOOST,
GRADIENT BOOSTING, XGBOOST

  • Introduction to boosting
  • Weak learners
  • Adaboost algorithm
  • Adaboost distribution and
    parameter calculation
  • Adaboost lab
  • Understanding gradient boosting
  • Gradient in gradient boosting
  • Gradient boosting algorithm
  • XGBoost
  • Kaggle practice exercise

MODULE 7
SUPPORT VECTOR MACHINE
SUPPORT VECTOR MACHINE: THEORY

  • Introduction to SVM
  • Concept of a hyperplane in 2D
  • Concept of a hyperplane in 3D
  • Maximal margin classifier
  • The soft margin classifier
  • The slack variable
  • Notion of slack variables
  • Cost of misclassification

SVM: IMPLEMENTING SVM IN SKLEARN,
CASE STUDY

  • Mapping non-linear data to linear
    data
  • Feature transformation
  • The kernel trick
  • Modeling SVM Python Sklearn
  • Model evaluation

MODULE 8
ARTIFICIAL NEURAL NETWORK
(ANN)

  • Introduction to ANN
  • Simple ANN network
  • How it works: backprop
    algorithm
  • Implementing ANN with Python
    sklearn
  • ANN modeling and evaluation
  • Comprehension

MODULE 9
MACHINE LEARNING ALGORITHMS

  • Advanced evaluation metrics:
    ROC_AUC, R2 theory, precision,
    recall, F1 score, RMSE
  • K-fold cross-validation
  • Grid and randomized search CV
    in sklearn
  • Imbalanced data set: SMOTE
    technique
  • Feature selection techniques
  • Choosing right algorithms