Numerous studies have demonstrated the manifold benefits of tennis, such as increasing overall physical and mental health. Unfortunately, many children and youth from low-income families are unable to engage in this sport mainly due to financial constraints such as private lesson expenses as well as logistical concerns to and back from such lessons and clinics. While several tennis self-training systems exist, they are often tailored for professionals and are prohibitively expensive. The present study aims to classify tennis players' skill levels and classify tennis strokes into phases characterized by motion attributes for a future development of an AI-based tennis self-training model for affordable and convenient applications running on devices used in daily life such as an iPhone or an Apple Watch for tennis skill improvement. We collected motion data, including Motion Yaw, Roll and Pitch from inertial measurement units (IMUs) worn by participating junior tennis players. For this pilot study, data from twelve participants were processed using Support Vector Machine (SVM) algorithms. The SVM models demonstrated an overall accuracy of 77% in classifying players as beginners or intermediates, with low rates of false positives and false negatives, effectively distinguishing skill levels. Additionally, the tennis swings were successfully classified into five phases based on the collected motion data. These findings indicate that SVM-based classification can be a reliable foundation for developing an equitable and accessible AI-driven tennis training system.