Career Opportunities in Machine Learning in the UK, US, and Europe

MessageThis Webinar is over
Date Jul 17, 2021
Time 04:00 PM EDT
Cost Free

Webinar to Empower Future ML Engineers

Do you want to earn a six-figure salary? There has been an increase of 300% in the job openings for machine learning and artificial intelligence professionals. The average salary of a machine learning engineer in the UK is £52,000. This average salary can even go up to £170,000 if you get employed by the top companies.

About the Webinar
This interactive webinar aims to explain to attendees about the tremendous career opportunities in machine learning and top companies hiring ML professionals. “Career Opportunities in Machine Learning in the UK, US, and Europe” will begin on 17th July 2021, from 2:00 PM to 3:00 PM BST.

Why Should You Attend the LSET Webinar?

The Career Opportunities in Machine Learning in the UK, US, and Europe webinar organised by LSET aims to discuss the career opportunities, growth, and challenges faced in the ML industry. The industry experts will be helping the students to discuss their doubts and help them define a clear career path for their future.

Special Highlight: 10 selected Individuals will be chosen to go through a free career preparatory workshop on Machine Learning. These workshops will provide a proper career path and guidance to individuals to prepare individuals for a career in Machine Learning.   

Benefits of Attending the Webinar

  • Machine learning industry exposure
  • Career counselling to become an ML professional
  • Clearing doubts and questions
  • Path to learn machine learning
  • Learn about the best practical training resources
  • Understanding the connection between Artificial Intelligence, Machine Learning, and Deep Learning

Topics Discussed in the Webinar

  • The AI & machine learning industry
  • Tools and programming languages for machine learning
  • Applications in data science
  • Project cycle of ML and data science projects
  • Popular ML algorithms
  • Career opportunities in machine learning
  • Top companies hiring ML professionals
  • The exponential growth of the AI & ML industry

Questions Discussed in the Webinar:

  • Which programming languages to learn for ML?
  • What is the scope of career growth in machine learning?
  • How to learn machine learning?
  • How to increase your chances of getting employed?
  • Which topics to cover for an interview?
  • What are the key challenges in adapting ML to an organisation?
  • What are the latest tools and technologies used in ML?

Who Should Attend this Webinar?

  • Engineering students who want to start a career in machine learning
  • Professionals who are looking for a career change
  • Students who want to learn about machine learning and artificial intelligence

Why Should You Learn Machine Learning?

  • Machine Learning enables businesses to develop programs that help machines to become adaptable to different environments. Every top business is hiring machine learning professionals who can put them ahead of their competitors.
  • The future of artificial intelligence is amazingly bright, with new organisations building AI and ML-based applications. Thus, a career in ML is both high paying and secure.
  • There is a diverse range of career options available when you learn machine learning.
  • Machine learning is still in an early stage of development. Thus, there are exponential career growth opportunities for ML professionals.
  • Machine learning professionals with a decent skill set can also have the opportunity to move to data science. The shift could be very satisfying, exciting, rewarding and filled with opportunities.

Machine Learning Engineer
These professionals have to train machines to make predictions. They need to have practical knowledge of Java, Python, data modelling, machine learning algorithms, probability, statistics, and more.

Skills You Need to Become Machine Learning Professional

These are the important skills that you need to have to make the best out of opportunities:

  • Statistics and Probability
  • Data evaluation and modelling
  • ML algorithms
  • Programming languages
  • Signal processing techniques


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