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Skin Cancer Detection with Deep Learning
Sehaj Grover
Sehaj Grover
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Skin Cancer Detection with Deep Learning

Build and train deep learning models to classify skin cancer images accurately using Convolutional Neural Networks (CNNs).

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Apply now
Tuesdays
 at
7:00
P.M.
 ET /
4:00
P.M.
PT
8 weeks, 2-3 hours per week
Intermediate
No experience required
No experience required
Some experience required
Degree and experience required

Description

This project combines artificial intelligence with healthcare, equipping you to solve real-world challenges through machine learning. You will gain foundational knowledge in machine learning and explore deep learning methodologies for image classification. Using Convolutional Neural Networks (CNNs), you will develop models capable of analyzing and categorizing skin cancer images with precision. The hands-on nature of this project provides you with experience in data preprocessing, model training, and evaluation, ensuring they are prepared for entry-level roles in AI and machine learning.

Session timeline

  • Applications open
    December 1, 2024
  • Application deadline
    January 15, 2025
  • Project start date
    Week of July 8, 2024
    Week of
    February 3, 2025
  • Project end date
    Week of

What you will learn

  • Execute Python code effectively on Google Colab.
  • Build foundational machine learning models using linear and logistic regression.
  • Develop neural networks and apply deep learning techniques for image classification.
  • Design and implement Convolutional Neural Networks (CNNs) for detecting skin cancer.
  • Work with advanced architectures like ResNet-50 for higher classification accuracy.

Project workshops

1
Introduction to ML and Google Colab
2
Linear Algebra and Introduction to NumPy & Pandas
3
Data Preprocessing and Visualization with Matplotlib
4
Linear Regression
5
Logistic Regression
6
Building Neural Networks
7
Implementing CNNs and Introduction to ResNet-50
8
Final Presentation

Prerequisites

  • Basic Python programming knowledge, including syntax and functions.
  • Familiarity with linear algebra concepts like matrices and vector operations.
  • A basic understanding of data preprocessing and visualization techniques.

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About the expert

Sehaj Grover

Software Development Fellow
Open Avenues Foundation

Sehaj Grover is a Software Development Build Fellow at Open Avenues Foundation, where he works with students leading projects in software development, backend engineering, and applied machine learning.

Sehaj is a Senior Full Stack Engineer at Attend (formerly Season Share), where he focuses on designing and implementing scalable web applications, developing advanced algorithms for optimized ticketing solutions, and mentoring junior engineers to foster a collaborative development environment. He also integrates machine learning techniques to enhance system capabilities and user insights.

Sehaj has over 5 years of professional experience in the software development field. He has led several high-impact projects, enhancing product efficiency, user experience, and system performance through modern technologies, innovative design approaches, and machine learning applications.

He holds a Master’s in Computer Science from the University at Buffalo (SUNY).

A fun fact about Sehaj is that he enjoys exploring mystery and thriller movies and audiobooks in his free time.

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