AI/ML for Classification - Logistic Regression

The second AI/ML workshop explores Classification through Logistic Regression, offering participants a deep understanding of model components distinct from linear regression. The hands-on session covers key concepts, ensuring practical implementation skills. By completing a step-by-step project, attendees emerge adept at crafting impactful classification models,

Jan 30, 1:30 PM – Jan 31, 2:30 PM



Key Themes

Career DevelopmentML Study JamMachine LearningOpen Source

About this event

Participants in the second workshop, focusing on AI/ML for Classification through Logistic Regression, will engage in an immersive hands-on learning experience, creating their inaugural Classification project. The workshop prioritizes interactivity and practicality, ensuring participants not only grasp theoretical concepts but also acquire the skills to effectively apply them.

Workshop Highlights:

Step-by-Step Guidance: Participants will receive meticulous step-by-step guidance, unraveling the intricacies of logistic regression. Instructors will demystify the activation function, emphasizing its role in contrast to linear regression without an activation function.

Project-Based Learning: The workshop centers on project-based learning. Participants will embark on their logistic regression project, commencing with data collection, progressing through model development, and concluding with comprehensive performance evaluation.

Code Implementation: Utilizing Python, Numpy, and Scikit-learn, participants will translate their conceptual understanding into practical code. Emphasis will be placed on coding best practices, ensuring functional, well-structured, and efficient projects.

Problem-Solving Skills: Deliberate challenges and roadblocks will be introduced to cultivate problem-solving skills. These hurdles serve as invaluable learning opportunities, preparing participants for the problem-rich landscape of AI/ML.

Collaborative Learning: Fostering a collaborative environment, the workshop encourages participants to share insights and collaborate on problem-solving. Collective learning enhances the overall experience, enabling participants to glean knowledge from diverse perspectives.

Best Practices: The workshop underscores industry best practices at each stage, from understanding the activation function and cost function nuances to evaluating performance metrics. Participants will grasp how to create robust and efficient AI/ML solutions.

Key Topics Covered:

Activation Function (Sigmoid): Understanding the role of the sigmoid activation function and its deviation from linear regression without an activation function.

Cost Function: Distinguishing the cost function in logistic regression and its disparities from linear regression, highlighting the critical differences.

Accuracy Metrics (F1 Score, Precision, Recall): In-depth exploration of F1 score, precision, and recall as vital accuracy metrics in classification projects, ensuring a comprehensive evaluation approach.

By the end of the workshop, participants will not only have completed a practical logistic regression project but will also possess a profound understanding of classification principles. Equipped with practical skills and knowledge, participants will be well-prepared for success in the dynamic realm of Artificial Intelligence and Machine Learning.


  • Daksh Oza


    AI/ML Chapter Lead


  • Daksh Oza


    AI/ML Chapter Lead

  • Aditya Kamarouthu


    AI/ML Chapter Member

  • Aniruddh Pandey


    AI/ML Chapter Member

  • Khurram Rashid


    AI/ML Chapter Member

  • Parikshit Sahu


    AI/ML Chapter Member


  • Asbaa Thakur

    GDSC Lead

  • Saad Shaikh

    Game Development

  • Daksh Oza

    AI & ML

  • Vivek Pillai

    Competitive Programming


    Amity University

    Web Development

  • Samarth Kulkarni

    Public Relations


    Management Committee

  • Dhananjay Joshi


  • Dylan Moraes

    Cloud Development

  • Nandini Nirmal

    Social Media and Marketing

  • Runa Kapuganti

    Android Development

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