Machine Learning

Teacher

Pradeep

COURSE DESCRIPTION

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.

CERTIFICATION

Receive an instructor-signed certificate with the institution’s logo to verify your achievement and increase your job prospects

LEARNING OUTCOMES

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.

Course Features

Lectures 28
Quizzes 0
Duration 160 hours
Skill level All levels
Language English
Students 0
Assessments Yes

Level 1 5
- Lecture1.1
  
  Introduction to Python
- Lecture1.2
  
  Data types – Hands on Program for each data type
- Lecture1.3
  
  Program Control – Hands on program for each Loop, condition statements
- Lecture1.4
  
  OOP Concepts, Multithread, Network access
- Lecture1.5
  
  Numpy, PANDAS, Plotting and Graphics – Hands on for Shaping, Operations, Using Data frames and displaying results
Level 2 5
- Lecture2.1
  
  Linear Regression – Algorithm, Math background, Hyper parameters and Hands on
- Lecture2.2
  
  KNN – Algorithm, Math background, Hyper parameters and Hands on
- Lecture2.3
  
  SVM – Algorithm, Math background, Hyper parameters and Hands on
- Lecture2.4
  
  GD – Algorithm, Math background, Hyper parameters and Hands on
- Lecture2.5
  
  Hands on Project using large data-set
Level 3 8
- Lecture3.1
  
  K Means – Algorithm, Math background, Hyper parameters and Hands on
- Lecture3.2
  
  Neural Networks – Algorithm, Math background, Hyper parameters and Hands on
- Lecture3.3
  
  Dimensional reduction – Curse of Dimensions, Features Extraction and methods to reduce dimensions
- Lecture3.4
  
  Anomaly Detection – Use case, Demo and Hands on
- Lecture3.5
  
  Recommendation System – Use case, Demo and Hands on
- Lecture3.6
  
  NLP – Use case, Demo and Hands on
- Lecture3.7
  
  Hands on project using Industry dataset
- Lecture3.8
  
  Introduction to Deep Learning
Level 4 9
- Lecture4.1
  
  Software development methodologies – Steps, Significance and Tools
- Lecture4.2
  
  Python Good Coding Practice – Hands on code rewrite exercise for efficiency, readability and maintenance
- Lecture4.3
  
  Debug Tools, methods and Tricks
- Lecture4.4
  
  Code review of Participants code for Improvements
- Lecture4.5
  
  Test Methodologies, Types and Quality Assessment
- Lecture4.6
  
  Agile Methodology – Live Project Execution / Condition Based Monitoring Industry Machines
- Lecture4.7
  
  Algorithm Selection Guide – Exercise map problem statements to algorithms
- Lecture4.8
  
  Emerging Technologies in Machine Learning and Introduction to Artificial Intelligence
- Lecture4.9
  
  Selected Enterprise Problems – Discussion, Problem Scoping and Architect ting, preparation for Level 5
Level 5 1
- Lecture5.1
  
  Opportunity to be part of RDL Machine Learning Team working on ongoing customer project

Pradeep

My experience, 25 years of Global IT experience working for large Telco and Networking Vendors, handling large multi-site Software Development projects. Equipped with Masters Degree and breadth of experience working for large corporate to startup, embarked on this journey to contribute to Student Community. I currently handle full time role of Solution Architect at RDL Technologies in AI, Machine Learning and IoT Domain. Look forward to leverage my experience to upskill students enabling faster employability.