Autism is a disorder characterized by difficulty in social interactions, communication challenges, and repetitive behaviors. In recent years, treatments for autism have been constantly evolving, therefore, it is essential to diagnose children at an early age to be able to control their symptoms. We have used information about signals that will help us in early detection of autism, to help affected children to integrate into society and live independently. For these reasons, this article focused on the use of data for only toddlers, and it compared deep learning and traditional classifiers for achieving efficient and accurate classification in the environment of machine learning, in contrast to previous research that focused on using traditional classifiers for different older ages. Among all applied algorithms, Support Vector Machine (SVM), Logistic Regression (LR) and Multilayer Perceptron (MLP) are perhaps worthy of further study on this problem in terms of only scores (Accuracy, Recall, Precision and F1), and only LR in terms of both scores and training runtime.