Curriculum
Module 1:
Introduction to Python, Numpy Basics, Pandas Basics
Module 2:
Matplotlib basics, Seaborn basics
Module 3:
Introduction to Machine Learning, Data Preprocessing, Creating validation rules
Module 4:
Introduction to Regression, Regularized Regression, Auto selection of parameters, Evaluation of best models, Model representation
Module 5:
Introduction to Classification, Regularized Classification, Auto selection of parameters, Evaluation of best models, Model representation
Module 6:
Introduction to Decision Tree, Auto selection of parameters, Evaluation of best models, Model representation
Module 7:
Introduction to Random Forest, Auto selection of parameters, Bagging and Boosting Models, Evaluation of best models, Model representation
Module 8:
Introduction to SVM, Auto selection of parameters, Evaluation of best models, Model representation
Module 9:
Introduction to Neural Network, Auto selection of parameters, Evaluation of best models, Model representation
Module 10:
Introduction to Unsupervised Learning, Auto selection of parameters, Evaluation of best models, Model representation
Module 11:
Introduction to Dimension Reduction, Auto selection of parameters, Evaluation of best models, Model representation
Module 12:
Introduction to Nearest Neighbors, Auto selection of parameters, Evaluation of best models, Model representation
Learning Outcomes
Who Should Attend?
Job Prospects
Certification
After completing this course and successfully passing the certification examination, the student will be awarded the “Machine Learning” certification.
If a learner chooses not to take up the examination, they will still get a 'Participation Certificate'.