New to machine learning and data science? No question is too basic or too simple. Feel free to start your own thread, or use this thread as a place to post any first-timer clarifying questions for the Kaggle community to help you with!
\nIf you would consider yourself a beginner but don't know where to get started, let other Kagglers help you take your first steps here!
\nNew to Kaggle? Take a look at a few videos to learn a bit more about site etiquette, Kaggle lingo, and how to enter a competition using Kaggle Notebooks.
\nRemember: Kaggle is for everyone. Whether you're teaming up or sharing tips in the competition forum, we expect everyone to follow our Kaggle community guidelines.
", "messages": [ { "id": 2405448, "postDate": "2023-08-23T21:57:14.077Z", "content": "New to machine learning and data science? No question is too basic or too simple. Feel free to start your own thread, or use this thread as a place to post any first-timer clarifying questions for the Kaggle community to help you with!
\nIf you would consider yourself a beginner but don't know where to get started, let other Kagglers help you take your first steps here!
\nNew to Kaggle? Take a look at a few videos to learn a bit more about site etiquette, Kaggle lingo, and how to enter a competition using Kaggle Notebooks.
\nRemember: Kaggle is for everyone. Whether you're teaming up or sharing tips in the competition forum, we expect everyone to follow our Kaggle community guidelines.
", "rawMarkdown": "New to machine learning and data science? No question is too basic or too simple. Feel free to start your own thread, or use this thread as a place to post any first-timer clarifying questions for the Kaggle community to help you with!\n\nIf you would consider yourself a beginner but don't know where to get started, let other Kagglers help you take your first steps here!\n\nNew to Kaggle? Take a look at a few videos to learn a bit more about [site etiquette](https://www.youtube.com/watch?v=aIus8si_Et0), [Kaggle lingo](https://www.youtube.com/watch?v=sEJHyuWKd-s), and [how to enter a competition using Kaggle Notebooks](https://www.youtube.com/watch?&v=GJBOMWpLpTQ).\n\n**Remember:** Kaggle is for everyone. Whether you're teaming up or sharing tips in the competition forum, we expect everyone to follow our [Kaggle community guidelines](https://www.kaggle.com/community-guidelines).", "votes": 11 }, { "id": 2441848, "postDate": "2023-09-16T13:50:57.983Z", "content": "Hey!!
\nCan you suggest resources to get my ML journey started?
Certainly! Embarking on a Machine Learning (ML) journey can be an exciting endeavor. Depending on your background and goals, there are various paths you can take. Here's a step-by-step guide to help you get started:
\nBefore diving into machine learning, it's beneficial to have a strong foundation in certain subjects:
\nMathematics:
\nProgramming: Familiarity with Python is highly recommended since it's the most widely used language in ML.
\nCourses:
\nBooks:
\nCourses:
\nBooks:
\nDatasets: Play around with datasets on platforms like:
\nProject Ideas: Start with simple projects like:
\nAs you grow in the field, you can explore various subfields like:
\nFor each subfield, there are specialized courses, papers, and resources available. You can dive deeper based on your interests.
\nThe field of ML is continuously evolving. Engage with the community and stay updated by:
\nRemember, the key is consistent practice and application. As you work through courses, try to implement projects simultaneously to solidify your understanding. Good luck on your ML journey!
", "rawMarkdown": "Certainly! Embarking on a Machine Learning (ML) journey can be an exciting endeavor. Depending on your background and goals, there are various paths you can take. Here's a step-by-step guide to help you get started:\n\n### 1. **Foundations:**\nBefore diving into machine learning, it's beneficial to have a strong foundation in certain subjects:\n\n- **Mathematics:**\n - **Linear Algebra:** MIT's [Linear Algebra course](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/)\n - **Statistics & Probability:** Stanford's [Stat110 course](http://www.stat110.net/)\n - **Calculus:** Khan Academy's [Calculus course](https://www.khanacademy.org/math/calculus-1)\n\n- **Programming:** Familiarity with Python is highly recommended since it's the most widely used language in ML.\n - [Python.org's Beginner's Guide](https://docs.python.org/3/tutorial/index.html)\n - [Codecademy's Python Course](https://www.codecademy.com/learn/learn-python-3)\n\n### 2. **Introductory Machine Learning:**\n- **Courses:**\n - **Coursera:** [Machine Learning by Andrew Ng](https://www.coursera.org/learn/machine-learning)\n - **Udacity:** [Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120)\n\n- **Books:**\n - \"Python Machine Learning\" by Sebastian Raschka\n - \"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow\" by Aurélien Géron\n\n### 3. **Deep Learning:**\n- **Courses:**\n - **Coursera:** [Deep Learning Specialization by Andrew Ng](https://www.coursera.org/specializations/deep-learning)\n - **Fast.ai:** [Practical Deep Learning for Coders](https://course.fast.ai/)\n\n- **Books:**\n - \"Deep Learning\" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville\n\n### 4. **Practice and Projects:**\n- **Datasets:** Play around with datasets on platforms like:\n - [Kaggle](https://www.kaggle.com/)\n - [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php)\n\n- **Project Ideas:** Start with simple projects like:\n - Spam email classification\n - Handwritten digit recognition\n - Movie recommendation system\n\n### 5. **Specializations:**\nAs you grow in the field, you can explore various subfields like:\n- Natural Language Processing (NLP)\n- Computer Vision\n- Reinforcement Learning\n- Generative Adversarial Networks (GANs)\n- Transfer Learning\n... and many more.\n\nFor each subfield, there are specialized courses, papers, and resources available. You can dive deeper based on your interests.\n\n### 6. **Stay Updated:**\nThe field of ML is continuously evolving. Engage with the community and stay updated by:\n- Reading new research papers on [arXiv](https://arxiv.org/) in the relevant category.\n- Following blogs like [Towards Data Science](https://towardsdatascience.com/), [Distill](https://distill.pub/), etc.\n- Attending conferences like NeurIPS, ICML, or CVPR.\n\nRemember, the key is consistent practice and application. As you work through courses, try to implement projects simultaneously to solidify your understanding. Good luck on your ML journey!", "votes": 10, "replies": [ { "id": 2447881, "postDate": "2023-09-20T09:47:47.077Z", "content": "Thank you @bluewall , it really helpful
", "rawMarkdown": "Thank you @bluewall , it really helpful\n", "votes": 1 }, { "id": 2509702, "postDate": "2023-11-02T14:15:51.647Z", "content": "really helpful
", "rawMarkdown": "really helpful" }, { "id": 2535993, "postDate": "2023-11-23T20:02:57.830Z", "content": "Helpful for me, thx
", "rawMarkdown": "Helpful for me, thx" } ] }, { "id": 2448516, "postDate": "2023-09-20T16:20:46.160Z", "content": "Also, edX and Udemy offer very interesting and high-level courses. Best of luck on your way!
", "rawMarkdown": "Also, [edX](https://www.edx.org) and [Udemy](https://www.udemy.com) offer very interesting and high-level courses. Best of luck on your way!", "votes": 1 } ] }, { "id": 2450479, "postDate": "2023-09-21T22:45:13.813Z", "content": "Hii Can you guide me how to deal with large datasets i am unable to read the parquet file in the first place
", "rawMarkdown": "Hii Can you guide me how to deal with large datasets i am unable to read the parquet file in the first place", "isDeleted": true, "replies": [ { "id": 2458895, "postDate": "2023-09-27T21:55:50.883Z", "content": "If you're working in Kaggle Notebooks, you can use this codeto read the file:
\nmultiome_train = pd.read_parquet('/kaggle/input/open-problems-single-cell-perturbations/multiome_train.parquet')
Hey!!
\nCan you suggest resources to get my ML journey started?
Certainly! Embarking on a Machine Learning (ML) journey can be an exciting endeavor. Depending on your background and goals, there are various paths you can take. Here's a step-by-step guide to help you get started:
\nBefore diving into machine learning, it's beneficial to have a strong foundation in certain subjects:
\nMathematics:
\nProgramming: Familiarity with Python is highly recommended since it's the most widely used language in ML.
\nCourses:
\nBooks:
\nCourses:
\nBooks:
\nDatasets: Play around with datasets on platforms like:
\nProject Ideas: Start with simple projects like:
\nAs you grow in the field, you can explore various subfields like:
\nFor each subfield, there are specialized courses, papers, and resources available. You can dive deeper based on your interests.
\nThe field of ML is continuously evolving. Engage with the community and stay updated by:
\nRemember, the key is consistent practice and application. As you work through courses, try to implement projects simultaneously to solidify your understanding. Good luck on your ML journey!
", "votes": 10, "replies": [ { "id": 2447881, "author_name": "Akshay Kumar", "author_url": "", "post_date": "2023-09-20T09:47:47.077000", "content": "Thank you @bluewall , it really helpful
", "votes": 1, "replies": [] }, { "id": 2509702, "author_name": "kaggler", "author_url": "", "post_date": "2023-11-02T14:15:51.647000", "content": "really helpful
", "votes": 0, "replies": [] }, { "id": 2535993, "author_name": "NetworkKnight", "author_url": "", "post_date": "2023-11-23T20:02:57.830000", "content": "Helpful for me, thx
", "votes": 0, "replies": [] } ] }, { "id": 2448516, "author_name": "Alex Garayoa", "author_url": "", "post_date": "2023-09-20T16:20:46.160000", "content": "Also, edX and Udemy offer very interesting and high-level courses. Best of luck on your way!
", "votes": 1, "replies": [] } ] }, { "id": 2450479, "author_name": "", "author_url": "", "post_date": "2023-09-21T22:45:13.813000", "content": "Hii Can you guide me how to deal with large datasets i am unable to read the parquet file in the first place
", "votes": 0, "replies": [ { "id": 2458895, "author_name": "John Scanlan", "author_url": "", "post_date": "2023-09-27T21:55:50.883000", "content": "If you're working in Kaggle Notebooks, you can use this codeto read the file:
\nmultiome_train = pd.read_parquet('/kaggle/input/open-problems-single-cell-perturbations/multiome_train.parquet')