AI Engineer

Kaggle Plan: From Beginner to Pro

Phase *: Getting Started (Weeks 1-4)

Week 1: Account Setup and Language Selection

● Create Your Kaggle Account: Sign up at Kaggle.

● Choose a Programming Language:

● Python is recommended due to its extensive libraries for data science (e.g., Pandas, Scikit-Learn,

TensorFlow).

● Familiarize yourself with Python basics through

Kaggle’s free Python Course.

Week 2: Learn Data Exploration

● Explore Data:

● Understand how to load and visualize data using

libraries like Matplotlib and Seaborn.

● Start with standard datasets such as the Iris

Dataset or Titanic Dataset available on Kaggle.

Week 3: Basic Machine Learning Concepts

● Train Your First Model:

● Implement basic models using Scikit-Learn. Focus on classification and regression tasks.

● Complete the Intro to Machine Learning course on Kaggle.

Week 4: Analyze Winning Notebooks

● Daily Analysis:

● Review at least 5 winning notebooks from previous competitions. Focus on understanding their

methodologies and techniques.

● Engage with the community by asking questions in discussion forums.

Phase 2: Experimentation (Weeks 5-8)

Week 5: Model Development

● Experiment with Models:

● Work on at least 2-3 different models each week. Try out decision trees, random forests, and logistic

regression.

Week 6: Participate in Getting Started

Competitions

● Join Beginner Competitions:

● Participate in competitions labeled as “Getting

Started,” such as:

● Titanic: Machine Learning from Disaster

● House Prices: Advanced Regression Techniques

Week 7: Document Your Learning

● Create Notebooks:

● Start documenting your experiments in Kaggle

Notebooks. Share insights and results to engage with the community.

Week 8: Feedback Loop

● Iterate Based on Feedback:

● Analyze the results of your submissions. Understand why certain models performed better and adjust your strategies accordingly.

Phase 3: Compete (Weeks 9-12)

Week 9: Advanced Competitions

● Submit to Active Competitions:

● Aim for at least 1 submission per competition you participate in. Consider competitions like:

● Digit Recognizer

● Plant Seedlings Classification

Week 10: Collaborate with Others

● Build a Team:

● Form a team with other Kagglers to share knowledge. Collaborating can enhance learning and improve

performance.

Week 11: Continuous Learning

● Review Public Notebooks:

● Study other public notebooks for new techniques and insights. Participate in discussions to deepen

understanding.

Week 12: Reflect and Set New Goals

● Evaluate Your Progress:

● Reflect on what you’ve learned and set new goals for upcoming competitions or projects.

Understanding Medals and Rankings

Medal System Overview

Kaggle awards medals based on performance in various categories:

● Competition Medals:

● Bronze, Silver, Gold based on placement in

competitions.

● Notebook Medals:

● Awarded for popular notebooks based on upvotes

received.

● Dataset Medals:

● Given for datasets that gain popularity through

downloads and upvotes.

● Discussion Medals:

● Earned through insightful comments that receive

upvotes.

Performance Tiers

Kaggle has a tiered ranking system based on contributions:

● Novice

● Contributor

● Expert

● Master

● Grandmaster

Each tier reflects your achievements across competitions, notebooks, datasets, and discussions.

Continuous Improvement

Engage with the Community

● Participate actively in discussions, comment on others' work, and seek feedback to enhance your learning

experience.

Set Incremental Goals

● Establish small, achievable milestones to maintain motivation and track progress throughout your Kaggle journey.

Made with love

Tensor Boy

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