Description

Machine learning is the science of getting computers to act without being explicitly programmed, Instead of writing code, you feed data to the generic algorithm, and it builds logic based on the data given. Machine learning algorithms allow for computers to train on data inputs and use statistical analysis in order to output values that fall within a specific range.

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'.