This course covers an overview of Machine Learning system, components and functions. Introduction to supervised, non supervised and Deep learning algorithms. Basic mathematics around the algorithms, reference implementations, assignments that would enable implement customer solutions. Basics of python programming, libraries associated to ML like NUMPY, PANDAS, SciLearn. AWS Cloud and Tensor flow would be introduced, that would propel the value of the resume and boost employ-ability.
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- Components of Machine Learning system – Features and Functions.
- Significance of Data-set.
- Basics of the Algorithm design.
- Algorithm selections for real life solutions.
- Exposure to Library for accelerated solution development.
- Hands on assignment for each algorithm.
- Internship with hands-on real time project.
- Basics of Software engineering, enable modular code, test procedure and Debug steps, tools.
- Lectures 28
- Quizzes 0
- Duration 160 hours
- Skill level All levels
- Language English
- Students 0
- Assessments Yes
- Linear Regression – Algorithm, Math background, Hyper parameters and Hands on
- KNN – Algorithm, Math background, Hyper parameters and Hands on
- SVM – Algorithm, Math background, Hyper parameters and Hands on
- GD – Algorithm, Math background, Hyper parameters and Hands on
- Hands on Project using large data-set
- K Means – Algorithm, Math background, Hyper parameters and Hands on
- Neural Networks – Algorithm, Math background, Hyper parameters and Hands on
- Dimensional reduction – Curse of Dimensions, Features Extraction and methods to reduce dimensions
- Anomaly Detection – Use case, Demo and Hands on
- Recommendation System – Use case, Demo and Hands on
- NLP – Use case, Demo and Hands on
- Hands on project using Industry dataset
- Introduction to Deep Learning
- Software development methodologies – Steps, Significance and Tools
- Python Good Coding Practice – Hands on code rewrite exercise for efficiency, readability and maintenance
- Debug Tools, methods and Tricks
- Code review of Participants code for Improvements
- Test Methodologies, Types and Quality Assessment
- Agile Methodology – Live Project Execution / Condition Based Monitoring Industry Machines
- Algorithm Selection Guide – Exercise map problem statements to algorithms
- Emerging Technologies in Machine Learning and Introduction to Artificial Intelligence
- Selected Enterprise Problems – Discussion, Problem Scoping and Architect ting, preparation for Level 5