The Six Best AI and Machine Learning Forums in 2022

Data scientists use various statistical tools and knowledge to solve complex business problems. Therefore, data scientists cannot afford to wait days for a solution when they get stuck on something.

A forum where like-minded people from the same profession help each other out can be helpful. Experts around the world actively participate in civil discussions on popular platforms, clearing up misconceptions and passing on their knowledge to others.

Let’s look at some of these forums, which each AI and ML professional should attend.


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 are facing, and clever solutions to typical problems.

Data Science Center

Big Data practitioners can communicate on Data Science Center. It includes topics that deepen specialist knowledge of the technical aspects of data science and business topics focused on industry-specific sector issues. Additionally, it consists of a section on programming languages ​​that discusses coding methods for different languages.


Among data scientists, Kaggle is a popular platform. Data scientists can work with massive datasets to develop models and gain hands-on experience. Kaggle also offers a lively community forum where users can get their analytics-related questions answered. Kaggle also attracts many well-known experts, so you can ask some of the top performers in your field your questions and get answers from them. All levels of expertise in natural language processing, computer vision, neural networks, visualization and related fields are welcome.

development community

development community is where programmers can share ideas and encourage each other. This forum is the place for all machine learning discussions. Discuss current projects, GitHub issues, and machine learning predictions while getting tutorials and other ML how-tos.

IBM Global Data Science Forum

There are approximately 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 Data Science

Data Science Researchers, Engineers and Developers Gather on Russian Forum Open Data Science. An exciting setting where you can strengthen your bonds and learn from each other.

Harry L. Blanchard