Creating testing environments to help progress AI research out of the lab and into the real world is immensely challenging. Given AI's long association with games, it is perhaps no surprise that sports presents an exciting opportunity, offering researchers a testbed in which an AI-enabled system can assist humans in making complex, real-time decisions in a multiagent environment with dozens of dynamic, interacting individuals. The rapid growth of sports data collection means we are in the midst of a remarkably important era for sports analytics. The availability of sports data is increasing in both quantity and granularity, transitioning from the days of aggregate high-level statistics and sabermetrics to more refined data such as event stream information (e.g., annotated passes or shots), high-fidelity player positional information, and on-body sensors. However, the field of sports analytics has only recently started to harness machine learning and AI for both understanding and advising human decision-makers in sports.

The understanding of emotions from voice by a human brain are normal instincts of human beings, but automating the process of emotion recognition from speech without referring any language or linguistic information remains an uphill grind. In the research work presented based on the input speech, I am trying to predict one of the six types of emotions (sad, neutral, happy, fear, angry, disgust). The diagram given below explain how emotion recognition from speech works. The audio features are extracted from input speech, then those features are passed to the emotion recognition model which predicts one of the six emotions for the given input speech. Most of the smart devices or voice assistants or robots present in the world are not smart enough to understand the emotions.

Last month, I shared a short list of dataset repositories that I planned to recommend to students as inspiration for their class projects. Thanks to all the great suggestions via the Twitter thread above, this list has grown quite a bit! Now, with the semester being in full swing, I recently shared this set of dataset repositories with my deep learning class. However, beyond using this list to find inspiration for interesting student class projects, these are also good places to look for additional benchmark datasets for your model, so I am putting it out here, hoping you find it useful! It is hard to sort by priority or to pick favorites, so the following list is sorted alphabetically.

In this article, we'll be discussing 15 machine learning and data science projects for beginners as well for intermediate level. Projects are some of the best investments of your time. You'll enjoy learning, stay motivated, and make faster progress. For machine learning or data science projects finding a dataset is a quite difficult task. And, to build accurate models, you need a huge amount of data.