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CS109B

AC215

-

Advanced Practical Data Science, DLOps. - This course aims to use existing Deep Learning flow while applying it - to a real-world problem. We will build and deploy an application that uses - the deep learning model to understand how to productionize models. - Split into two parts; the first part will be Development, where you use - the models you trained incorporate them into real-world applications. - The second part, you will Deploy the application in Google Cloud Platform (GCP). - The two parts will cover in detail topics such as Transfer learning, Containerization using Docker, - and Scaling deployments using Kubernetes. +

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. ​

- Know more hereKnow more here
@@ -132,18 +130,20 @@

Bedrock Data Science

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: - - 1. Basic Python: Data types, data structures, functions - 2. Advanced Python: Python Classes - 3. Probability & Statistics - 4. Linear Algebra & Calculus +

Course Topics:

+
    +
  1. Basic Python: Data types, data structures, functions
    2. +
  2. Advanced Python: Python Classes
    3. +
  3. Probability & Statistics
    4. +
  4. Linear Algebra & Calculus
    5. +

This course is offered only online and asynchronously during the summer. + A version of this tailored for high schoolers is offered via system3.
@@ -169,7 +169,8 @@

The PINNS course

- Coming spring of 2023 + Offered Jan 2024. Check here for info. +
I am currently working on publishing a book on Physics-Informed Neural Networks (PINNs), with an expected release in summer 2025.
@@ -182,19 +183,25 @@

The PINNS course

Introduction to Data Science with Python

- Using Python, learners will study regression models and classification - models, utilizing standard libraries such as sklearn, Pandas, matplotlib, - and numPy. The course will cover key concepts of machine learning such as: - picking the right complexity, preventing overfitting, regularization, - assessing uncertainty, weighing trade-offs, and model evaluation. - Participation in this course will build your confidence in using Python, - preparing you for more advanced study in Machine Learning (ML) and - Artificial Intelligence (AI) and advancement in your career. -

+

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.

+

+ Know more here + +

+

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 + +
- Know more here +