Machine Learning

Need help with Machine Learning?

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Below are the consultant we have available with Machine Learning and other expertise listed.

Chirag Manghani

Consultant
School of Information

Chirag is a 2nd year graduate at the I-School. Proficient in Python, Java, R, and SQL, he navigates software application development, machine learning and data science. His keen interest lies in data analysis and statistical methods, driving him to bridge theory and practice seamlessly. Chirag's dedication to excellence, adaptable mindset, and innate curiosity define him as a dynamic problem solver in the ever-evolving tech landscape.

Deibi Sibrian

Data Science for Social Justice Fellow 2024
Deibi is a Ph.D. student in the Department of Environmental Science, Policy, and Management, centering critical interdisciplinary ecology and multispecies justice. Deibi coined the term "Cryptonocene," an interdisciplinary framework, to study the socio-environmental health impacts of cryptocurrencies and related technologies, such as AI. With over two years of experience as a graduate instructor, Deibi now is a Graduate Student Researcher, NSF Digital Transformation Fellow, and Mentored Research Fellow. Before joining Berkeley, Deibi was the project manager for an interdisciplinary team...

Hellina Hailu Nigatu

Data Science for Social Justice Senior Fellow 2024
Electrical Engineering and Computer Science (EECS)

I am a PhD student at UC Berkeley in the EECS department co-advised by Prof. Sarah Chasins and Prof. John Canny. My research interest broadly lies in the intersection of AI and HCI, with a focus on making usable AI tools accessible to end users.

I am currently looking into making NLP tools usable and accessible for low-resourced languages. I am also interested in the impact of AI on society, specifically in how it affects Global Majority countries and communities. Outside of research, I like to read books, make and drink traditional Ethiopian coffee, knit,...

Violet Davis

Data Science for Social Justice Senior Fellow 2024
MIDS

I am a Masters student studying Data Science with the School of Information. My research involves computational social science projects focused on social justice and equity.

Python Deep Learning: Parts 1-2

June 12, 2023, 2:00pm
The goal of this workshop is to build intuition for deep learning by building, training, and testing models in Python. Rather than a theory-centered approach, we will evaluate deep learning models through empirical results.

Python Deep Learning: Parts 1-2

November 13, 2023, 2:00pm
The goal of this workshop is to build intuition for deep learning by building, training, and testing models in Python. Rather than a theory-centered approach, we will evaluate deep learning models through empirical results.

Python Deep Learning: Parts 1-2

June 7, 2022, 1:00pm
This workshop presents a brief history of Artificial Neural Networks (ANNs) and an explanation of the intuition behind them; a step-by-step reconstruction of a very basic ANN, and then how to use the scikit-learn library to implement an ANN for solving a classification problem.

Python Introduction to Machine Learning: Parts 1-2

February 23, 2022, 10:00am
This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets. No theory instruction will be provided.

Python Machine Learning Fundamentals: Parts 1-2

February 7, 2023, 2:00pm
This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets. No theory instruction will be provided.

Python Machine Learning Fundamentals: Parts 1-2

October 2, 2023, 2:00pm
This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets. No theory instruction will be provided.