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STARTS SOON ON MONDAY, DECEMBER 16th, 2024!

  This course covers essential techniques for creating insightful visualizations using Python. Over four days, you'll learn to use libraries like matplotlib, seaborn, plotly, altair, folium, and networkx to create line charts, bar charts, scatter plots, maps, and network graphs. You’ll also dive into advanced topics like interactive visualizations, the Grammar of Graphics, and recreating iconic visualizations. Hands-on labs and assignments ensure you practice each concept, preparing you to transform data into compelling visual stories for real-world applications.

STARTS SOON ON MONDAY, DECEMBER 9th, 2024!

  This data wrangling with Python course covers essential techniques for cleaning, transforming, and preparing data for analysis. Over four days, you'll learn how to use pandas and NumPy for data manipulation, master data assembly, and manipulate strings and DateTime. The course also delves into tidy data strategies, handling missing values, and data preprocessing for machine learning with Scikit-Learn. Hands-on labs and homework assignments ensure you practice each concept, making you proficient in preparing data for real-world projects and machine learning applications

STARTS SOON ON MONDAY, MAR 31, 2025!

  Elevate your fine-tuning expertise with our immersive hands-on course designed for AI practitioners. Begin with the foundational concepts of transfer learning and pre-trained models, then dive into fine-tuning methodologies for transformers and other state-of-the-art architectures. Explore open-source libraries such as Hugging Face, LoRA, and PEFT for scalable and efficient fine-tuning. Master techniques like prompt tuning, adapter tuning, and hyperparameter optimization to tailor models for domain-specific tasks. Learn strategies for low-resource fine-tuning, including few-shot and zero-shot learning, and address overfitting with advanced regularization methods. Discover fine-tuning approaches for diverse modalities, including text, images, and multimodal data, while exploring domain-adaptation strategies for out-of-distribution datasets. Implement advanced training strategies like quantization-aware training, curriculum learning, and differential privacy. By the end of the course, you’ll have the practical knowledge to fine-tune models for real-world applications, ensuring optimal performance and efficiency tailored to your unique datasets.