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Short introduction to machine learning and data science. Project includes using scikit-learn for k-means clustering on the Iris dataset.

Home Page: https://scottcampit.github.io/intro-to-ml/

License: MIT License

Jupyter Notebook 44.56% HTML 55.44%
data-science data-visualization iris-dataset k-means-clustering machine-learning scikit-learn seaborn

intro-to-ml's Introduction

Howdy! ๐Ÿ‘‹

My name is Scott, and I'm a Data Scientist, Consultant, and AI Researcher developing Artificial Intelligence, Machine Learning, and Statistical solutions in several industries, such as biotechnology, medical devices, and healthcare.

I receieved my Ph.D. from the University of Michigan, where I developed new Systems Biology models to identify cancer drug candidates that target metabolism (Publications). My work has been acknowledged by institutions such as the National Cancer Institute, the Department of Education, and the Arnold and Mabel Beckman Foundation, where I have earned several fellowships and scholarships to conduct independent research.

Nowadays, I'm a Data Scientist at RaLytics, where I'm heads down in AI R&D for a wide variety of company use-cases. Additionally, I have worked with several U.S. government, research, and private institutions as a consultant to offer insights, technical expertise, and develop new solutions on projects that catch my interests.

Some of my current projects include (but are not limited to):

  • Training and developing Vision-Language Models for chart reasoning and understanding ๐Ÿ“ˆ
  • Medical NLP development for entity identification, extraction, and summarization ๐Ÿ“„
  • Deploying statistical learning models for inferring patterns of care from claims data ๐Ÿฅ
  • Pose estimation applications for improving athletic performance ๐Ÿƒโ€โ™€๏ธ

Some past projects I have worked on include:

  • Leveraging LLMs for developing a personalized marketing platform ๐Ÿ“ง
  • Performing data analysis and creating data pipelines for healthcare-based datasets (claims, financial, EMRs, etc) ๐Ÿ—‚๏ธ
  • Establishing DataOps and MLOps best practices for model deployment and data processing โ˜๏ธ
  • Vision models to identify anatomical structures during surgical procedures ๐Ÿ˜ท
  • Developing bioinformatics pipelines (gene set enrichment analysis, protein expression network graphs, etc) for drug discovery ๐Ÿ’Š

โ˜• I'm always interested in chatting, colabs, and consulting gigs. If you're in the Bay Area and want to grab a coffee or just say hi, you can reach me at my email address or LinkedIn. For my writings about all things nerdy, you can check out my blog, which is currently undergoing some re-vamping.

intro-to-ml's People

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intro-to-ml's Issues

numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl: 1 vulnerabilities (highest severity is: 5.3)

Vulnerable Library - numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl

NumPy is the fundamental package for array computing with Python.

Library home page: https://files.pythonhosted.org/packages/6d/ad/ff3b21ebfe79a4d25b4a4f8e5cf9fd44a204adb6b33c09010f566f51027a/numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl

Path to dependency file: /requirements.txt

Path to vulnerable library: /requirements.txt,/requirements.txt

Found in HEAD commit: e619790ccc840d898a4b2df0e68745924758a0dc

Vulnerabilities

CVE Severity CVSS Dependency Type Fixed in (numpy version) Remediation Available
CVE-2021-34141 Medium 5.3 numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl Direct 1.22.0 โŒ

Details

CVE-2021-34141

Vulnerable Library - numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl

NumPy is the fundamental package for array computing with Python.

Library home page: https://files.pythonhosted.org/packages/6d/ad/ff3b21ebfe79a4d25b4a4f8e5cf9fd44a204adb6b33c09010f566f51027a/numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl

Path to dependency file: /requirements.txt

Path to vulnerable library: /requirements.txt,/requirements.txt

Dependency Hierarchy:

  • โŒ numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (Vulnerable Library)

Found in HEAD commit: e619790ccc840d898a4b2df0e68745924758a0dc

Found in base branch: main

Vulnerability Details

An incomplete string comparison in the numpy.core component in NumPy before 1.22.0 allows attackers to trigger slightly incorrect copying by constructing specific string objects. NOTE: the vendor states that this reported code behavior is "completely harmless."
Mend Note: After conducting further research, Mend has determined that versions 1.12.0 through 1.21.6 of numpy are vulnerable to CVE-2021-34141

Publish Date: 2021-12-17

URL: CVE-2021-34141

CVSS 3 Score Details (5.3)

Base Score Metrics:

  • Exploitability Metrics:
    • Attack Vector: Network
    • Attack Complexity: Low
    • Privileges Required: None
    • User Interaction: None
    • Scope: Unchanged
  • Impact Metrics:
    • Confidentiality Impact: None
    • Integrity Impact: None
    • Availability Impact: Low

For more information on CVSS3 Scores, click here.

Suggested Fix

Type: Upgrade version

Origin: https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-34141

Release Date: 2021-12-17

Fix Resolution: 1.22.0

Step up your Open Source Security Game with Mend here

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