Complete Machine Learning

Dive into the future! This GDSC workshop unlocks the mysteries of Machine Learning: Learn how machines "learn" from data to make predictions. Explore powerful algorithms for solving real-world problems. Gain the knowledge to kickstart your journey in AI! Join us and unleash the power of Machine Learning!

Mar 17, 1:15 PM – Mar 28, 5:00 PM



Key Themes

Explore MLMachine Learning

About this event

🚀 Ever wondered how apps predict your movie preferences or online stores recommend the perfect product? It's the magic of Machine Learning (ML) at work!

This GDSC workshop is your gateway to this fascinating world. We'll unveil the fundamental concepts that empower machines to learn from data and make intelligent predictions.

📅 Date: 17.03. 2024 – 28.03.2024

🕒 Time: 7 pm – 8:30 pm

🌐 Virtual Venue

📌 Event Highlights:

• Day 1 - Introduction to ML, Simple Linear Regression, Cost/Loss Function

• Day 2 - Convergence Algorithm, Multiple Linear regression, Types of Cost Function

• Day 3 - R_Squared, Adj. R_Squared Correlation,Overfitting, Underfitting, Regularization,ElasticNet Regression

• Day 4 - Bias-varience Tradeoff, Logistic Regression and Performance Metrices

• Day 5 - Multicolinearity,Decision Tree Regressor, Decision Tree Classifier

• Day 6 - Cross Validation, Types of Cross Validation, Feature Transform(MinMax,Standard)

• Day 7 - Naïve Bayes, Bagging And Boosting Algorithm

• Day 8 - Random Forest Classifier, Random Forest Regressor, KNN Classifier, KNN Regressor

• Day 9 - Maintain Remaining Classes if any else Doubt Session/ Discussion

• Day 10 - K means, Heirarichal Means, dbscan

• Day 11 - SVM, PCA

📌 What you'll learn:

• Machine Learning Fundamentals: Grasp the core principles behind how machines can learn from data. We'll explore different types of data and how it fuels the learning process.

• Introduction to Machine Learning Algorithms: Dive into a few key algorithms that are used in Machine Learning, including:

• Simple Linear Regression: A powerful technique for uncovering relationships between variables and making predictions.

• Decision Trees: These are flowchart-like structures that allow for classification and prediction tasks.

• K-Means Clustering: This is an unsupervised learning technique for grouping data points into distinct clusters.

• Support Vector Machines (SVMs): Powerful algorithms used for classification and regression problems.

• Additional Topics: We'll also touch on other essential concepts like:

• Cost/Loss Function: How to measure how well a machine learning model is performing.

• Overfitting and Underfitting: Understanding how to avoid these common pitfalls in machine learning.



March 17 – 28, 2024
1:15 PM – 5:00 PM UTC


1:15 PMIntroduction to ML, Simple Linear Regression, Cost/Loss Function


  • Vishal Kumar Mahato


  • Dhruba Singha Roy

    GDSC Lead

  • Ashish kumar choudhary


    Web developer

  • Apurv Ajay Kumar

    Android developer

  • Sumit Kumar

    Project Manager


    Tech lead

  • Ishan Banerjee

    Digital management lead

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