Machine Learning

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

• Understand the different machine learning techniques and its application
  
• Understand the importance of assumptions in estimating the parameters in simple linear regression analysis.
  
• Understand the importance of simple linear regression in predicting new observations
  
• Understand the important multiple linear regression in predictive techniques and its assumptions.
  
• Apply the non-linear model for the new observation predictions and its importance in business.
  
• Understand the effect of model assumptions in estimating the coefficients in multiple linear regression analysis.
  

Who Should Attend?

• Engineering and IT students
  
• Graduates with a programming background
  

Job Prospects

• Data Architect
  
• Data Scientist
  
• Data Mining Specialists
  
• Cloud Architect
  
• Cyber Security Analysts
  

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'.