Course - Key highlights

Program Duration Program Duration

Program Duration

208 Hours

Accredited by Accredited by

Accredited by

NSDC

Practical Learning Practical Learning

Practical Learning

Hands-on experience

Fexible Learning Fexible Learning

Fexible Learning

In-center and online

Learn from Learn from

Learn from

Industry Experts

Learn – Machine Learning using R & Python

The global machine learning market, valued at $15.44 billion in 2021, is experiencing steady growth due to the widespread adoption of technological advancements. Embedded systems, crucial in various applications, heavily rely on microcontrollers as essential components. Acquiring skills in microcontroller programming can pave the way for career opportunities in the design of embedded systems, product development, and robotics

Course objective

Our Machine Learning Certification Courses will provide you with a solid foundation in machine learning as well as the ability to develop, train, and implement machine learning models in Python and R. Working on real-world projects will allow you to gain practical experience and prepare you for a rewarding career in the field of artificial intelligence.

Key Topics Covered

 

  • Introduction to Machine Learning
  • Standard Libraries
  • Statistics
  • Probability Theory
  • Normal Distribution
  • Pre-processing
  • Logistic Regression
  • Linear Regression
  • K-Means
  • Data Extraction
  • Deep Learning
  • RNN algorithm writing

Certification Course on Machine Learning

Certification Course on Machine Learning using R

Certification Course on Machine Learning Using Python

Who is this Course suitable for?
  • Aspiring data scientists
  • Software developers
  • IT professionals
  • Analytics professionals
  • Graduates looking for a career in AI
Scope & Career Opportunities
You will learn the basic as well as advanced concepts of machine learning, including data preparation, feature engineering, model selection, and optimization. You will learn to work with popular machine learning algorithms such as regression, clustering, and decision trees, and implement them in Python and R.