Course curriculum

  • 1
    Module 1: Overview
  • 2
    Module 2: Machine Learning Models & Datasets
  • 3
    Module 3: Performance of ML Models
  • 4
    Module 4: Improving Performance of ML Models
  • 5
    Module 5: Feature Selection
  • 6
    Module 6: Feature Extraction
  • 7
    Module 7: Deep Learning
  • 8
    Module 8: Getting into Neural Networks
  • 9
    Module 9: Artificial Intelligence
  • 10
    Module 10: Project Work
  • Introduction
  • Models and their compatibility with the Dataset QUIZ-TIME
  • Confusion Matrix Accuracy, Precision, Recall and F1-Score AUC-ROC Curve Log-loss Function QUIZ-TIME
  • Cross-Validation Dimensionality Reduction Hyperparameter Tuning QUIZ-TIME
  • Feature selection using the Wrapper Method Feature selection using the Filter Method Feature selection using the Embedded Method QUIZ-TIME
  • Feature extraction Feature extraction: PCA Feature extraction: LDA Ensemble Modeling: Bagging Ensemble Modeling: Boosting QUIZ-TIME
  • Introduction to Deep Learning Introduction to Optimization Gradient Descent Activation Function QUIZ-TIME
  • Neural Networking Keras Tensorflow Computer Vision Backtracking QUIZ-TIME
  • Introduction to Artificial Intelligence Natural Language Processing Setbacks of Artificial Intelligence QUIZ-TIME
  • ProjectWork