Cognixia® - A Collabera Learning Solutions Company
  • Training
    • Applied AI TrainingApplied AI Training
    • Cyber Security TrainingCyber Security Training
    • AI Productivity & CopilotsAI Productivity & Copilots
    • Frontier AI Platforms (Claude, GPT-4, Gemini, Bedrock)Frontier AI Platforms (Claude, GPT-4, Gemini, Bedrock)
    • Quality Engineering AI TrainingQuality EngineeringAI Training
    • AI DevOps & MLOpsAI DevOps & MLOps
    • Experience AI TrainingExperienceAI Training
    • Data AI TrainingDataAI Training
  • Resources
    • Case StudyCase Study
    • EventsEvents
    • BlogBlog
    • PodcastPodcast
    • NewsletterNewsletter
  • About
    • AwardsAwards
    • Our CultureOur Culture
    • LocationsLocations
    • Refer Organizations for Emerging Technology UpskillingRefer Organizations for Emerging Technology Upskilling
  • Contact
Search Course
search Course
Generative AI Training for Enterprises Hire, Train & Deploy Enterprise Upskilling Programs Enterprise Upskilling Programs AI Partnerships AI Partnerships
Cybersecurity Governance in AI-Powered Organizations Latest Podcast : Cybersecurity Governance in AI-Powered Organizations
banner

Machine Learning in Action with Arun

HomeEventsMaster ClassMachine Learning in Action with Arun
Date
20th Jun, 2020
Time
12:30 AM - 9:30 AM IST
feature

Learn from industry experts about Cognixia Master Class : Machine Learning in Action with Arun

Pre-Requisite:

  • Familiarity with programming languages like Python or R is a nice to have

Lab Setup:

  • Stable Internet Connection
  • Python 3.6 OR Anaconda (Python 3.7) pre-installed on machines

This session will cover the following topics:

Introduction to ML
  • Artificial Intelligence & Machine Learning Introduction
  • Who uses AI?
  • Supervised & Unsupervised Learning
  • Regression & Classification Problems
  • What makes a Machine Learning Expert?
  • What to learn to become a Machine Learning Developer?
  • Overview of Machine Learning Algorithms
Linear Regression – Case Study & Project
  • Regression Problem Analysis
  • Mathematical modelling of Regression Model
  • Gradient Descent Algorithm
  • Use cases
  • Model Specification
  • Building simple Univariate Linear Regression Model
  • Multivariate Regression Model
Decision Trees
  • Forming a Decision Tree
  • Components of Decision Tree
  • Mathematics of Decision Tree
  • Decision Tree Evaluation for use cases
  • Ensemble of Decision Trees – Random forest, Bagging and Boosting
Clustering
  • K Means Clustering
  • Use Cases for K Means Clustering
  • Programming for K Means using Python
Machine Learning in Production
  • Machine Learning pipelines
  • Deploying Machine Learning models to production
  • Hyper parameter tuning
  • Accuracies, call backs and monitoring
Load More



  • Share
  • LinkedIn
  • WhatsApp
  • FaceBook
  • Email

Exclusive Invite-Only Workshop

Participation in this event is by invitation only.
For more details or to request an invite, please reach out to us at events@cognixia.com
×

FILL IN THE FORM BELOW TO GAIN FREE ACCESS

  • This field is for validation purposes and should be left unchanged.

Let’s build the workforce of the...

Talk to Our Team
×
Talk to Our Team

  • This field is for validation purposes and should be left unchanged.
Cognixia® - A Collabera Learning Solutions Company

Cognixia builds the Forward Deployed Engineers, AI practitioners, and enterprise-ready talent that frontier AI companies and enterprises depend on. A Collabera Company.

Global Industries

  • Aviation
  • Automobile
  • BFSI
  • E-commerce
  • Food-tech
  • Healthcare
  • Media and Entertainment
  • Oil and Gas
  • Pharmaceutical
  • Telecommunication

Contact Us

support@cognixia.com
United States - HQ
110 Allen Road,
Basking Ridge,
NJ 07920+1 877 264 6424
India - HQ
Floor 8, Tower C, Nilamber Corporate Park, Gotri-Bhayli Road, Vasna Rd, Vadodara, Gujarat 390007+91 635 892 2166

  • Refund Policy
  • Terms & Conditions
  • Privacy Policy
Copyright © 2026 Cognixia. All rights reserved
×

Cognixia Special Offer

  • This field is for validation purposes and should be left unchanged.