Data scientists use various tools and statistical knowledge to solve complex business problems. As a result, data scientists cannot afford to wait days to find a solution when they are stuck on something. 

A forum where like-minded individuals from the same profession assist one another can be helpful. Experts worldwide actively participate in civil discussions on well-liked platforms, clearing misconceptions and imparting their knowledge to others. 

Let's examine a few of these forums, which every AI and ML professional ought to attend.

Reddit

Reddit is the most comprehensive source of information on machine learning, deep learning, and data science in general. You can find a variety of threads with exciting details, including websites, blogs, resources, issues people face, and clever solutions to typical problems.

Data Science Central

Big data practitioners can communicate on Data Science Central. It includes topics that delve deeply into specialized knowledge of the technical aspects of data science and business topics focusing on industry-specific sector-based issues. Additionally, it consists of a section on programming languages that discusses coding methods for various languages.

Kaggle

Among data scientists, Kaggle is a popular platform. Data scientists can work with massive datasets to develop models and gain practical experience. Kaggle also features a bustling community forum where users can get answers to their analytics-related questions. Kaggle also attracts a lot of well-known experts, so you can pose your questions to some of the most successful people in your field and get answers from them. All levels of expertise in natural language processing, computer vision, neural networks, visualization, and related fields are welcome.

DEV Community

DEV Community is where programmers can share ideas and encourage one another's growth. This forum is the place for all machine learning discussions. Discuss ongoing projects, GitHub issues, and machine learning predictions while getting tutorials and other ML how-tos.

IBM Global Data Science Forum

There are about 21,000 members, 223 libraries, and 600 blog posts in the Global Data Science Forum. Additionally, IBM has a business analytics community where users can learn about new products and assess their AI prowess. Additionally, there is a DataOps community. Data Replication, DataStage - Data Integration, Global DataOps, Master Data Management (MDM), Watson Knowledge Catalog (WKC), Data Governance and Quality, etc., are some topics.

Open DataScience

Researchers, engineers, and developers in data science come together on the Russian forum Open DataScience. An exciting setting where you can strengthen your connections and learn from one another.