Machine Learning using Python Introductory Training

Machine Learning course will give you overview of machine learning, a form of artificial intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will grasp machine learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modelling to develop algorithms and prepare you for the role of Machine Learning Engineer. Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

  • 3 Days Workshop
  • Completion Certificate awarded by GKK

  • TO BE CONFIRMED SOON
  • Please contact us directly for more details

DAY1


Steps 1 and 2
Steps 3 and 4
How it Works
Steps 5 and 6
Supervised Learning Model Considerations
Knowledge Check
Scikit-Learn
Knowledge Check

DAY2


Supervised Learning Models – Linear Regression
Supervised Learning Models – Logistic Regression
Unsupervised Learning Models
Pipeline
Model Persistence and Evaluation
Knowledge Check
Natural Language processing with Scikit Learn
NLP Overview
NLP Applications
Knowledge check

DAY3


NLP Libraries-Scikit
Extraction Considerations
Scikit Learn-Model Training and Grid Search
Assignment 01
Demo Assignment 01
Assignment 02
Demo Assignment 02
Quiz

What are the course objectives?


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Who should take the course?


  • Software developers
  • IT managers
  • Service management professionals
  • Technology Managers

Who is your trainer for the program?


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We offer the following options:


  • Cash
  • HRDF Claimable
  • Maybank Ezpay (Up to 24 months @ 0% Interest)
  • CIMB Easy Pay (Up to 12 months @ 0% Interest)
  • Cash Installment (Case by case basis)

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