Machine Learning
16135
page-template-default,page,page-id-16135,bridge-core-3.2.0,qi-blocks-1.3.3,qodef-gutenberg--no-touch,tutor-lms,qode-page-transition-enabled,ajax_fade,page_not_loaded,,qode-child-theme-ver-1.0.0,qode-theme-ver-30.6.1,qode-theme-bridge,wpb-js-composer js-comp-ver-7.7.2,vc_responsive
 

Machine Learning

OBJECTIVES

The objective of the Introduction to Machine Learning training program is to equip participants with the foundational knowledge and practical skills needed to develop and apply machine learning algorithms in various contexts. The program will cover the theory and practice of machine learning, including data preprocessing, regression, classification, clustering, neural networks, model evaluation, and optimization, as well as ethical considerations.

 OUTCOMES

Understand the principles and types of machine learning.

Apply machine learning techniques to real-world problems.

Preprocess data, create machine learning models, and evaluate their performance.

Use various machine learning algorithms for regression, classification, clustering, and neural networks.

Optimize machine learning models to improve their accuracy and performance.

Apply ethical considerations to machine learning projects.

SCOPE

  • Machine Learning Engineer
  • Data Scientist
  • Data Analyst
  • AI Developer
  • Research Scientist
  • Business Intelligence Analyst

PROJECTS

To reinforce the knowledge and skills gained during the training program, participants will be assigned hands-on projects. These projects will require participants to apply their learning to real-world scenarios, such as predicting customer churn, classifying images, or clustering customer segments. Participants will have access to datasets and tools to complete these projects.

MACHINE LEARNING-ADVANCE
OBJECTIVES

The objective of this training program is to provide participants with a comprehensive understanding of Machine Learning techniques for IIOT Data. The program covers the essential concepts of Supervised and Unsupervised Learning techniques, Deep Learning techniques, Time Series Analysis, and their applications to IIOT data. The program aims to equip participants with the skills and knowledge required to apply Machine Learning techniques to real-world problems in the IIOT domain.

OUTCOMES

Ability to understand and apply scaling up machine learning techniques and associated computing techniques and technologies.

Ability to recognize and implement various ways of selecting suitable model parameters for different machine learning techniques.

Ability to integrate machine learning libraries and mathematical and statistical tools with modern technologies.

Understand the key concepts of Machine Learning and IIOT Data

Apply various data preprocessing techniques to handle IIOT Data

Apply Supervised and Unsupervised Learning techniques to analyze IIOT Data

Apply Deep Learning techniques for IIOT Data analysis

Apply Time Series Analysis techniques for IIOT Data analysis

Identify the applications of Machine Learning for IIOT Data

Develop and implement Machine Learning models for real-world IIOT problems

SCOPE
  • Data Scientist
  • Machine Learning Engineer
  • IIOT Analyst
  • Predictive Maintenance Engineer
  • Quality Control Engineer
  • Anomaly Detection Engineer
  • Research Scientist
PROJECTS

The training program includes several hands-on exercises and a project work to enable participants to apply the concepts learned in the program to real-world problems. The project work involves developing and implementing Machine Learning models for IIOT Data analysis.

  • Heart Attack Prediction
  • Rain prediction
  • Sensex prediction
  • Engine health prediction Vibration
  • Movie Recommendation System
  • Auto Correct Keyboard -NLP
  • Match prediction Sports Data