Speaker:This is a five-day (free) course that takes an intuitive, hands-on approach to give a wider technical audience an appreciation and understanding of data science, and in particular, the four core machine learning algorithmic families (classifiers, regressors, clusterers & dimensionality reduction techniques).
Day 1 Session Highlights:
- Historical and Cognitive Foundations of AI and Data Visualization: The lecture highlights the evolution of AI, the innate human ability to process visual data quickly, and how visualization enhances data comprehension by leveraging our brain's pattern recognition skills.
- Core Data Science Workflow: The speaker emphasizes the importance of data wrangling, analysis, and visualization as foundational pillars leading to predictive modeling in machine learning.
Clustering and Applications: Real-world examples of clustering are provided, such as news aggregation, customer segmentation, and gene expression analysis, showcasing how clustering reveals hidden patterns in data.
- Reinforcement Learning and Emergent Strategies: Insights are shared on OpenAI's experiments, illustrating how trial-and-error learning enables machines to develop unexpected, intelligent behaviors.
- Machine Learning Basics: The session covers foundational concepts, including regression, classification, supervised learning, overfitting, and dimensionality reduction techniques like PCA, t-SNE, and autoencoders.
- Setting Up AI Development Environments: Participants are introduced to UV, a modern alternative to Anaconda, for initializing and managing Python projects with simpler workflows and enhanced dependency management.
- Practical Q&A and Resources: The session concludes with practical advice, a step-by-step guide for transitioning to UV, and additional resources like video tutorials to support hands-on learning.
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