Top Q and A forums for AI and ML practitioners
Data scientists work on a variety of complex business problems, using statistical knowledge along with multiple tools. When stuck at something, a data scientist can’t afford to wait for days to figure out how to move forward. A forum where like-minded people from the same profession participate in helping each other out can come to the rescue. Popular forums have experts from across the world actively participating in healthy discussions, doubt-solving and spreading their knowledge to others. Let us look at a few of these Q-and-A forums that every practitioner in AI and ML should be a part of. Reddit: Machine learning Reddit is one of the most popular communities one can explore to solve their queries. This particular community on machine learning has around 2.4 million members. Posting on Reddit has the advantage of extremely quick responses as members are very active on the platform. The machine learning community discusses topics around the latest in machine learning, job posts, project-related doubts, etc. There are similar groups for deep learning, NLP, and computer vision as well. For more details, click here. MachineHack Image: MachineHack The MachineHack discussion forum is also a great place to post your doubts. It has a very active and engaging community, and users can get answers to any technical challenges in machine learning, artificial intelligence, data engineering, and data science, among others. It also has an assessment section where one can take tests to gauge their skills in cloud technologies, machine learning, programming languages, etc. There is another section in MachineHack to prepare you for your interviews for roles such as data engineer, ML engineer, data scientist, etc. For more details, click here. Kaggle Kaggle is usually a favourite among data scientists. It contains large datasets that data scientists can work with, build models and get a real-world feel. Kaggle also contains a very active forum to discuss all your analytics-related doubts. Highly recognised experts are also quite active on Kaggle, and one can get solutions to their queries from some of the highest-achieving professionals in the field. The discussions can range from basic to advanced topics in NLP, computer vision, neural networks, visualisation and more. For more details, click here. StackOverflow StackOverflow says that till now, over 21 million questions have been asked on its platform, with 13.6 seconds being the average time between new questions. StackOverflow is famous for its Q&A platform that sees a traffic of over 100 million people every month to ask questions, learn, and share technical knowledge. It has a vibrant AI, analytics and machine learning community. For more details, click here. IBM Global Data Science Forum Image: IBM IBM has different communities to share and discuss their know-how on IBM products. The Global Data Science Forum has around 21,000 members, 223 libraries and 556 blog posts. IBM also has a business analytics community where one can get product updates and analyse their AI skills. A DataOps community is also in place. Some areas of discussion include Data Replication, DataStage – Data Integration, Global DataOps, Master Data Management (MDM), Watson Knowledge Catalog (WKC), Data Governance and Quality, etc. For more details, click here. Discussion Forum – Data Science Central Data Science Central is a forum for big data practitioners. It features technical topics that deep dive into specialised knowledge of the technical aspects of data science and business topics with a focus on sector-based topics associated with a specific industry. It also has a section on programming languages which covers coding techniques in various languages. For more details, click here.
Reddit is one of the most popular communities one can explore to solve their queries.
Let us look at a few Q-and-A forums that every practitioner in AI and ML should be a part of.
["IBM", "Kaggle", "stackoverflow"]
["data science", "artificial intelligence", "Kaggle", "machine learning", "AI", "neural network", "ML", "computer vision", "stackoverflow", "NLP", "deep learning", "IBM", "analytics"]
["AI", "artificial intelligence", "machine learning", "ML", "deep learning", "neural network", "NLP", "computer vision", "data science", "analytics"]
I am a technology journalist at AIM. What gets me excited is deep-diving into new-age technologies and analysing how they impact us for the greater good.

Reach me at sreejani.bhattacharyya@analyticsindiamag.com