Data Science and Machine Learning Trends Seminar Training

This Machine Learning (ML) course provides a comprehensive overview of the current trends in Data Science and ML. After this 1-day seminar, attendees have a thorough understanding of their current state and the future directions they may take.

Course Details


1 day


No prior knowledge is presumed.

Target Audience

  • Data Professionals (Data Scientists/Engineers/Analysts, ML Engineers, etc)
  • Data Science Managers and Product Managers
  • Anyone interested in learning more about Machine Learning

Skills Gained

  • The current state of Data Science and AI/ML
  • Current trends in AI/ML
  • Future developments in AI/ML
Course Outline
  • Data (Science) is King
    • Brief introduction to Data Science and Machine Learning
    • Speed of Changes in DS/ML Development
    • Importance of keeping up with current trends
  • Evolution of Data Science and Machine Learning
    • Brief history of Data Science and Machine Learning
    • The AI Winters and Breakthroughs
    • Origins in Web Search: Big Data and Data Science
    • Advances due to GPU’s and Compute Power
    • Influence of these fields on industry and society
  • Major Trends in Data Science
    • Automation in Data Science
    • The rise of Augmented Analytics
    • The impact and importance of Data Privacy
    • Data Storytelling and visualization
    • The role of Big Data in modern businesses
  • Major Trends in Machine Learning
    • AutoML and Neural Architecture Search (NAS)
    • Advances in Natural Language Processing (NLP) and Large Language Models (LLM’s)
    • Explainable AI (XAI)/Machine Learning Interpretability (ML)
    • Transfer Learning and Pre-Trained Models
    • AI Engineering and Rapid ML Application Development
  • Case Studies
    • Success stories in Data Science
    • Significant breakthroughs in Machine Learning
    • Demonstrations of real-world application of current trends
  • Current R&D and Upcoming Developments
    • Federated Learning and Edge AI
    • Multimodal Learning
    • Reinforcement Learning in Real-world Scenarios
    • Automated Bias and Fairness Detection in AI