Data Science and ML Trends Training Overview
This Data Science and Machine Learning (ML) Trends course gives attendees an overview of the latest trends in Data Science and ML. By the end of this seminar, attendees have a thorough understanding of data science and ML and what the future may hold.
Location and Pricing
Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.
In addition, some courses are available as live, instructor-led training from one of our partners.
Objectives
- Understand the evolution of data science and machine learning and the major trends that are shaping these fields today
- Learn about the real-world applications of current trends in data science and machine learning
- Gain insights into the current R&D and upcoming developments in data science and machine learning
Prerequisites
No prior experience is presumed.
Outline
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Introduction
Data (Science) is King
- A 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
Conclusion
Training Materials
All ML seminar attendees received a copy of the instructor’s presentation.
Software Requirements
Online deliveries for this interactive training will use an online meeting platform (such as Zoom, WebEx, GoTo, or Teams.)
