+ Chile Data Science School
+La Serena Data Science School.
+ This is a 10 day long course that I co-organize with Juan Pablo Cuevas.
+ It is offered in La Serena, Chile and it is tailored for students in the social sciences and humanities.
+ The course covers data collection, data wrangling, visualization, statistical inference, and basic machine learning.
+
+ Italy Data Science School
+Italy Data Science School.
+ This is a 10 day long course that I co-organize with Gianluca Bontempi.
+ It is offered in Milan, Italy and it is tailored for students in the social sciences and humanities.
+ The course covers data collection, data wrangling, visualization, statistical inference, and basic machine learning.
+
+ AC215
+In today’s AI-driven landscape, building a deep learning model is just the beginning; + the real challenge lies in making it scalable, maintainable, and deployment-ready. AC215: + Productionizing AI (Machine Learning Operations) focuses on the entire ML operations workflow, + particularly for Large Language Models (LLMs). This course covers essentials like containerization, + cloud functions, data pipelines, and advanced training techniques, with a special emphasis on LLM + applications. You’ll learn to use LLM APIs, fine-tune models for specific tasks, and build scalable + applications, gaining the skills to deploy AI in real-world scenarios effectively. + +
+
+ Bedrock Data Science
+The objective of this course is to provide fundamental understanding of math, + statistics & programming required to undertake a course in machine learning, data science or AI. + You will start with the basics of python, statistics, linear algebra, and calculus. + + By the end of the course, you will have the tools and know the concepts needed to successfully + undertake a rigorous course in machine learning. + +
Course Topics:
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+
- Basic Python: Data types, data structures, functions +
- Advanced Python: Python Classes +
- Probability & Statistics +
- Linear Algebra & Calculus +
+ The PINNS course
++ In this course, we will first review differential equations and traditional numerical methods + to solve them. We will then delve into neural network approaches for solving differential equations, initial and boundary condition problems, + optimization methods, sampling techniques, error characterization, and transfer learning. + At the end of the course, students will be able to understand the challenges and advantages of PINNs, + know different approaches and compare them, and be able to implement and use existing + libraries to solve linear and non-linear ODE, a system of ODEs, + a system of linear and not linear-PDEs. +
+ Introduction to Data Science with Python
++
This course guides learners through essential data science techniques using Python,
+ covering regression, classification, and libraries like sklearn and Pandas.
+ Key ML concepts such as overfitting, regularization, and model evaluation are introduced,
+ providing a strong foundation in Python for advanced study in Machine Learning and AI.
+
Machine Learning and AI with Python
+Focusing on decision-making through machine learning, this course introduces decision trees, + progressing to bagging, random forests, and gradient boosting. + Real-world cases help learners practice prediction, refine models, and address issues + like overfitting and bias, preparing them for complex decision-making using Python.
+ Know more here + + +