Answers.Body: <p>Yes, definitely it is.</p>
  
  <p>It puts you in front of real-world problems, theoretical questions and practical issues that are not necessarily what you deal with in college/university tasks and projects.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-06-21T08:00:48.660Z
Answers.LastActivityDate: 2017-06-21T08:00:48.660Z
Answers.UserDisplayName: Ale
Answers.Body: <p>Kaggle is a great website. The website provides challenges that allow you to see how various components of ML interplay with each other. For example, large data sets, standard debugging of ML code (much like a "standard" computer programmer), and seeing the breadth of applications of ML to everyday problems are just a few of these components.  </p>
  
  <p>Coupling working on Kaggle projects with theoretical study of AI/ML is an effective way to learn ML quickly. </p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-06-25T03:02:59.903Z
Answers.LastActivityDate: 2017-06-25T03:02:59.903Z
Answers.UserDisplayName: Kernel_Dirichlet
Answers.Body: <p>Yes, Kaggle is a great place to start working on ML projects.
  You will start using ML algorithms (at first, using scikit-learn or any other ML library of your choice), learn the concepts of cross-validation (very important concept). You will cope with imbalanced classes, various size of datasets. Moreover, you will be able to ask questions and receive feedback from the community.</p>
  
  <p>It's a great place to start and keep learning, but do not limit yourself at using ML algorithms blindly. Try to understand them, even re-write them if you want.</p>
  
  <p>But it's definitely a lot of fun and there are always several competitions running so you can choose whatever dataset you prefer.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-06-20T10:04:01.247Z
Answers.LastActivityDate: 2017-06-20T10:04:01.247Z
Answers.UserDisplayName: Pierre L.
Body: <p>I am starting to learn machine learning (ML). I'm thinking about starting to take part in some projects in ML. </p>
  
  <p>Is <a href="https://www.kaggle.com/competitions" rel="nofollow noreferrer">Kaggle</a> is a good place to find projects in ML to work on?</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-06-19T18:16:21.543Z
Comments.Text: The site has a good Alexa ranking, which is an indicator it is widely visited and utilized.  They do advise starting with the Titanic is "you're new to data science and machine learning, or looking for a simple intro to the Kaggle prediction competitions" so it seems like a good place to wade in for someone new to the field.  *(Hopefully this question will yield an answer from a kaggle user)*  Good luck and welcome to AI!
Comments.UserDisplayName: DukeZhou
Tags: <machine-learning><kaggle>
Title: Is Kaggle a good place to find projects in ML to work on?
UserDisplayName: Artem
ThreadText: # Is Kaggle a good place to find projects in ML to work on?
  
  (Asked by: username_0 on 2017-06-19)
  
  I am starting to learn machine learning (ML). I'm thinking about starting to take part in some projects in ML.
  
  Is [Kaggle](https://www.kaggle.com/competitions) is a good place to find projects in ML to work on?
  
  **Tags:** machine-learning, kaggle
  
  > **Comments:**
  > **username_1**: The site has a good Alexa ranking, which is an indicator it is widely visited and utilized.  They do advise starting with the Titanic is "you're new to data science and machine learning, or looking for a simple intro to the Kaggle prediction competitions" so it seems like a good place to wade in for someone new to the field.  *(Hopefully this question will yield an answer from a kaggle user)*  Good luck and welcome to AI!  
  
  ---
  
  ## 3 answers
  
  ### Answer by username_4 on 2017-06-20. Score: 4 | Accepted answer
  
  Yes, Kaggle is a great place to start working on ML projects.
  You will start using ML algorithms (at first, using scikit-learn or any other ML library of your choice), learn the concepts of cross-validation (very important concept). You will cope with imbalanced classes, various size of datasets. Moreover, you will be able to ask questions and receive feedback from the community.
  
  It's a great place to start and keep learning, but do not limit yourself at using ML algorithms blindly. Try to understand them, even re-write them if you want.
  
  But it's definitely a lot of fun and there are always several competitions running so you can choose whatever dataset you prefer.
  
  
  ---
  
  ### Answer by username_2 on 2017-06-21. Score: 3
  
  Yes, definitely it is.
  
  It puts you in front of real-world problems, theoretical questions and practical issues that are not necessarily what you deal with in college/university tasks and projects.
  
  
  ---
  
  ### Answer by username_3 on 2017-06-25. Score: 2
  
  Kaggle is a great website. The website provides challenges that allow you to see how various components of ML interplay with each other. For example, large data sets, standard debugging of ML code (much like a "standard" computer programmer), and seeing the breadth of applications of ML to everyday problems are just a few of these components.
  
  Coupling working on Kaggle projects with theoretical study of AI/ML is an effective way to learn ML quickly.
  
  
  ---
  
  