Open Source/Machine Learning (Data Science) with Python


Machine Learning (Data Science) with Python Certification Training Course Overview

The Machine Learning with Python for Beginner training course will give you a detailed overview on developing machine learning using python covering the topics like regression, Naive Bayes, Clustering, tensor flow etc.

Who should do Machine Learning with Python ( Beginner ) training?

  • Anyone interested in Machine learning

Machine Learning (Data Science) with Python (40 Hours) Download Course Contents

Course Details Schedule
Live Virtual Classroom (Instructor-Led)
Duration : 5 Days (10 Days for 4 Hours/Day)
Fee : 1,800 (Includes Taxes) 
9 AM - 5 PM (Flexible Time Slots for 4 hours option)






February
8 Hours/Day
4 Hours/Day
01-05
07-11
01-12
07-18
March
8 Hours/Day
4 Hours/Day
01-05
07-11
08-12
01-12
07-18
08-19
April
8 Hours/Day
4 Hours/Day
05-09
11-15
12-16
19-23
05-16
11-22
12-23
19-30
Client's Location
As per mutual convenience

Classroom training is available in select Cities
Classroom Training (Available: London, Dubai, India, Sydney, Vancouver)
Duration : On Request
Fee : On Request
On Request

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Course Prerequisites
  • Basic Computer Knowledge.

On completion of this training, you will know:

  • Overview of Python Programming Language
  • Regression
  • K-Nearest Neighbors
  • Naive Bayes
  • Neural Networks
  • Clustering
  • Network Analysis
  • Classification
  • Deep Learning using Tensor Flow

Student Feedback  (Check Koenig Feedback on Trustpilot)

Q1 Say something about the Trainer? Q2 How is Koenig different from other training Companies? Q3 Will you come back to Koenig for training ?
Student Name Country Month Feedback Rating
O O Ogunbayo United States Sep-2019 A1. Harsh has a deep understanding of the subject however, I struggled, initially, to understand what he was saying due to a strong accent but it got better over time. I would have also prefered to have course materials printed out as it would have made it easy for me to annotate them with my personal comments

FAQ's