Category: competition
Question: How to get started with competitions?
Answer: ## Getting Started

- The Getting Started Competitions are specifically targeted at new users getting their feet wet with Kaggle and/or machine learning:
    - Binary classification: [Titanic: Machine Learning from Disaster](https://www.kaggle.com/c/titanic)
    - Regression: [House Prices: Advanced Regression Techniques](https://www.kaggle.com/c/house-prices-advanced-regression-techniques)

- The [Kaggle Learn](https://www.kaggle.com/learn/overview) platform has several tracks for beginners interested in free hands-on data science learning from pandas to deep learning. Lessons within a track are separated into easily digestible chunks and contain Notebook exercises for you to practise building models and new techniques hands-on. It is a great way to start deep diving into data science and quickly get familiar with the field!

- What Kaggle has learned from almost 2MM machine learning models on [Youtube](https://www.youtube.com/watch?v=oYNKc_u9Os8). This [data.bythebay.io](http://data.bythebay.io/) talk by Kaggle founder Anthony Goldbloom lays out what Kaggle competitions are all about.

- How to (almost) win at Kaggle on [Youtube](https://www.youtube.com/watch?v=JyEm3m7AzkE). In this talk competitor Kiri Nichols summarizes the appeal of Competitions as a data science learner.

## Discussion

- [General Discussion](https://www.kaggle.com/discussion): There are six general site Discussion Forums:
    - Kaggle Forum: Events and topics specific to the Kaggle community
    - Getting Started: The first stop for questions and discussion for new Kagglers
    - Product Feedback: Tell us what you love, hate, or wish for
    - Questions & Answers: Technical advice from other data scientists
    - Datasets: Requests for and discussion of open data
    - Learn: Questions, answers, and requests related to Kaggle Learn courses

- Competition Discussion Forums: No matter the competition you are participating in, you can count on plenty of active community members making posts to the forums. If you get stuck on a particular aspect of the problem, Discussions are a great place to ask questions.

- Competition Notebooks: Similar to Discussions, Notebooks shared within a competition are an excellent source of Exploratory Data Analyses (EDAs) & basic starter models which can be forked and built upon for applied learning.

- The Kaggle Noobs Slack channel: This Slack channel is a popular watering hole for general banter among Kaggle ML practitioners from Novice to Grandmaster.

## Techniques

- Public, reproducible code examples in Notebooks are a great way to learn and put to practice new techniques. Search for techniques in Notebooks by tag using the search syntax |tag:classification|. Fork Notebooks to make a copy of the code to modify and experiment with.

- The [No Free Hunch](http://blog.kaggle.com/) blog. No Free Hunch is a great way of keeping up with goings-on on Kaggle. Many past Competitions winners have been interviewed about and presented their winning models on No Free Hunch. Here are some examples of past winner’s interviews:
    - NOAA Right Whale Identification
    - Instacart Market Basket Analysis, Winner’s Interview: 2nd place, Kazuki Onodera
    - Two Sigma Financial Modeling Code Competition

- Various tutorials have been published on No Free Hunch:
    - An Intuitive Introduction to Generative Adversarial Networks
    - Introduction To Neural Networks
    - A Kaggle Master Explains Gradient Boosting
    - A Kaggler’s Guide to Model Stacking in Practice

- Marios Michailidis: How to become a Kaggle #1: An introduction to model stacking: In this Data Science Festival talk top Kaggler Marios Michailidis (Kasanova) explains model stacking, a key feature of winning competition models, in great detail.

- Kaggle Grandmaster Panel: A panel Q&A from H2O World 2017 featuring some top Kagglers.

- How to Win A Kaggle Competition - Learn From Top Kagglers: This Coursera course, put together by high-ranking Kagglers, going into great detail on the tools and techniques used by winning Competitions models.