The ML Study Jam is a collaborative learning program designed to help community members grow as machine learning practitioners. It focuses on building foundational ML skills through shared knowledge and hands-on practice. By mastering the basics, beginners can kick-start their journey to becoming ML experts. 🤖 🚀

This program follows the Kaggle Learn track, covering essential skills like using TensorFlow, Pandas, and building your first ML model. You'll also learn how to participate in Kaggle competitions 👏 🎉.

Kaggle, the world's largest data science community, provides a beginner-friendly environment with pre-configured Jupyter Notebooks, free GPUs, and a wealth of datasets and code.

ML Study Jam: Session Series Overview

Session 1: Python Fundamentals

Session Schedule

📅 Day 1: Monday, 2nd December 2024

Topics:

• Data Structures: Lists, dictionaries, tuples, sets
• Loops: For and while loops for efficient iteration
• Conditionals: If-else statements for smarter code

📅 Day 2: Wednesday, 4th December 2024

Topics:

• Classes: Introduction to object-oriented programming
• List Comprehension: Writing concise, efficient loops
• NumPy: The basics of numerical computations

📅 Day 3: Friday, 6th December 2024

Topics:

• Pandas: Organizing, cleaning, and analyzing data
• Matplotlib: Visualizing insights and trends
• Project: A mini hands-on Python project to apply all concepts

Resources to Power Your Learning:

• Intro to Programming: https://www.kaggle.com/learn/intro-to-programming
• Pandas: https://courses.mooc.fi/org/uh-cs/courses/data-analysis-with-python-2024-2025/chapter-3/pandas-part-1
• Pandas: https://www.kaggle.com/learn/pandas
• NumPy: https://courses.mooc.fi/org/uh-cs/courses/data-analysis-with-python-2024-2025/chapter-2/numpy
• Matplotlib: https://courses.mooc.fi/org/uh-cs/courses/data-analysis-with-python-2024-2025/chapter-3/matplotlib

Session 2: Introduction to Machine Learning

Topics Covered

• Overview of ML Concepts
• How Models Work
• Basic Data Exploration
• Underfitting and Overfitting

Resources

Intro to Machine Learning: https://www.kaggle.com/learn/intro-to-machine-learning)

Session 3: Introduction to Deep Learning

Topics Covered

• Single Neuron
• Deep Neural Networks
• Stochastic Gradient Descent
• Binary Classification

Resources:

Intro to Deep Learning: https://www.kaggle.com/learn/intro-to-deep-learning

Session 4: Computer Vision

Topics Covered

• The Convolutional Classifier

Resources:

Computer Vision: https://www.kaggle.com/learn/computer-vision)

Session 5: Time Series Analysis

Topics Covered

• Explore Time-Series Data
• Forecasting Methods
• Practical Applications

Resources:

Time Series: https://www.kaggle.com/learn/time-series