Introduction to Machine Learning – An Informative Webinar

On November 3, CloudxLab conducted a successful webinar on “Introduction to Machine Learning”.  It was a 3-hour session wherein the instructor shed some light on Machine Learning and its terminologies.

It was attended by more than 200 learners around the globe. The participants were from countries namely; United States, Canada, Australia, Indonesia, India, Thailand, Philippines, Malaysia, Macao, Japan, Hong Kong, Singapore, United Kingdom, Saudi Arabia, Nepal, & New Zealand.

Presented By

Sandeep Giri - Instructor for the Machine Learning webinar

Sandeep Giri

Topics Covered in The Webinar

  • What is Machine Learning?
  • Automating Mario Game
  • The Machine Learning Tsunami
  • Collecting Data
  • Processing Data
  • Spam filter Using Traditional and Machine Learning
  • What is AI?
  • Sub-objectives of AI
  • Different Type of Machine Learning
  • Artifical Neural Network
  • Introduction to Deep Learning
  • TensorFlow Demo
  • Machine Learning Frameworks
  • Deep Learning Frameworks

Questions & Answers

The questions asked by attendees were on the following themes

  • Prerequisites for learning Machine Learning.
  • How ML is used in AI field?
  • Level of mathematics one needs to know before learning Machine Learning.
  • Queries related to Machine Learning Course.
  • How one can learn statistics for Machine Learning.
  • Difference between ML, Data Science & AI.
  • Best programming language for a beginner to learn Machine Learning.
  • Introduction to Natural Language Processing.
  • The number of data points that are usually needed for a robust solution to most ML problems.
  • How modeling will be different for different use cases?
  • How Machine Learning algorithms correct for bias in terms of use cases such as criminal judgments in court cases?

Watch The Recorded Machine Learning Webinar Here

Check it out on Slideshare here

Feedback

We got a lot of feedback from the Machine Learning webinar attendees. We are sharing some of them here.

Feedbacks

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