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Machine Learning For Everyone

03 Nov 2026   01:30 AM CST

  • Free Webinar
  • Live Q&A
  • Free Participation Certificate
  • Learn from Industry Experts

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Webinar Overview
In this webinar, we will explore the fundamentals of Machine Learning (ML) and how it is transforming industries across the globe. Attendees will gain a clear understanding of the relationship between Artificial Intelligence (AI), Machine Learning, and Deep Learning, while discovering how data enables machines to learn, predict, and make intelligent decisions. Through real-world examples, practical use cases, and a live demonstration, participants will learn how organizations use Machine Learning to improve efficiency, automate processes, and create innovative solutions.
Key Points

  • Understand the fundamentals of Artificial Intelligence, Machine Learning, and Deep Learning, and how they differ from traditional programming.
  • Explore Supervised, Unsupervised, and Reinforcement Learning techniques, along with their real-world applications.
  • Discover how organizations use Machine Learning for recommendations, fraud detection, predictive analytics, customer segmentation, and process automation.
Meet The Trainer
Sharayoo Gaurav Dixit
Sharayoo Gaurav Dixit

Mentor | NIIT

Duration: March 2021 – May 2026

  • Delivered instructor-led training sessions on Microsoft Excel, SQL, Python, Machine Learning, Tableau, and Power BI for students and working professionals.
  • Designed and developed training content, assignments, exercises, and learning materials for SQL and Python for Data Science programs.
  • Conducted hands-on workshops and practical sessions focused on real-world data analytics and machine learning applications.
  • Guided learners through end-to-end industry projects, including:
    • Amazon Customer Feedback Analysis – Sentiment analysis and customer insights.
    • Football Data Analysis – Performance analysis and data visualization.
    • IMDb Movie Data Analysis – Exploratory data analysis, visualization, and predictive modeling.
  • Mentored students on data cleaning, data visualization, statistical analysis, and machine learning model development.
  • Assisted learners in applying analytical techniques using tools such as Python, SQL, Tableau, and Power BI to solve business problems.
  • Evaluated student performance through assignments, assessments, and project reviews while providing continuous feedback and guidance.

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