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pavlosprotopapas committed Nov 1, 2024
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Expand Up @@ -99,20 +99,18 @@ <h4 class="text-lh-sm" id='CS109B'>CS109B</h4>
<div class="card-body">

<h4 class="text-lh-sm" id='AC215'>AC215</h4>
<p class="mb-0"> Advanced Practical Data Science, <b>DLOps</b>.
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 <em>Development</em>, where you use
the models you trained incorporate them into real-world applications.
The second part, you will <em>Deploy</em> 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.
<p class="mb-0"> 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.

​ </p>
</div>
<div class="card-footer">
<a class="font-weight-bold" href="https://harvard-iacs.github.io/2021-AC215/">Know more here<i
<a class="font-weight-bold" href="https://harvard-iacs.github.io/2024-AC215/">Know more here<i
class="fas fa-angle-right fa-sm ml-1"></i></a>
</div>
</div>
Expand All @@ -132,18 +130,20 @@ <h4 class="text-lh-sm">Bedrock Data Science</h4>
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
<h4>Course Topics:</h4>
<ol>
<li>Basic Python: Data types, data structures, functions</li>
<li>Advanced Python: Python Classes</li>
<li>Probability & Statistics</li>
<li>Linear Algebra & Calculus</li>
</ol>
</p>
</div>
<div class="card-footer">
<!--a class="font-weight-bold" href="">This course is offered only asynchronous during the summer. <i
class="fas fa-angle-right fa-sm ml-1"></i></a-->
This course is offered only online and asynchronously during the summer.
A version of this tailored for high schoolers is offered via <a href="https://www.system3.company">system3</a>.
</div>
</div>
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Expand All @@ -169,7 +169,8 @@ <h4 class="text-lh-sm">The PINNS course</h4>
<div class="card-footer">
<!--a class="font-weight-bold" href=""><i
class="fas fa-angle-right fa-sm ml-1"></i></a-->
Coming spring of 2023
Offered Jan 2024. Check <a href="https://harvard-iacs.github.io/PINNS_course/">here</a> for info.
<br> I am currently working on publishing a book on Physics-Informed Neural Networks (PINNs), with an expected release in summer 2025.
</div>
</div>
<!-- End Card Info -->
Expand All @@ -182,19 +183,25 @@ <h4 class="text-lh-sm">The PINNS course</h4>

<h4 class="text-lh-sm" id='HarvardX'>Introduction to Data Science with Python</h4>
<p class="mb-0">
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.
</p>
<p >This course guides learners through essential data science techniques using Python,
covering regression, classification, and libraries like <code>sklearn</code> and <code>Pandas</code>.
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.</p>
</p>
<a class="font-weight-bold" href="https://www.edx.org/learn/data-science/harvard-university-introduction-to-data-science-with-python">Know more here
<i class="fas fa-angle-right fa-sm ml-1" ></i></a>
<br><br>
<h4 class="text-lh-sm" id='HarvardX'>Machine Learning and AI with Python</h4>
<p>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.</p>
<a class="font-weight-bold" href="https://www.edx.org/learn/machine-learning/harvard-university-machine-learning-and-ai-with-python?objectID=course-1dc2b7e4-21ab-4c39-9da0-1784d3321948&webview=false&campaign=Machine+Learning+and+AI+with+Python&source=edX&product_category=course&placement_url=https%3A%2F%2Fwww.edx.org%2Fbio%2Fpavlos-protopapas-3">Know more here
<i class="fas fa-angle-right fa-sm ml-1"></i></a>

</div>
<div class="card-footer">
<a class="font-weight-bold" href="https://courses.edx.org/courses/course-v1:HarvardX+CS109x+1T2022/6cb10b8ef27e486e89f823d877e13240/">Know more here<i
class="fas fa-angle-right fa-sm ml-1"></i></a>

</div>
</div>
<!-- End Card Info -->
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