This work presents a new multi-chemical experimental platform for molecular communication (MC) where the transmitter can release different chemicals. This platform is designed to be inexpensive and accessible, and it can be expanded to simulate different environments such as a portion of the body's cardiovascular system or a complex network of pipes in industrial complexes and city infrastructures. To demonstrate the capabilities of the platform, we implement a time-slotted binary communication system where information is carried via the pH of transmitted signals and, in particular, a 0-bit is represented by an acid pulse, and a 1-bit by a base pulse. The channel model for this system, which is nonlinear and has a long memory due to chemical reactions, is unknown. Therefore, we devise novel detection algorithms that use techniques from machine learning and deep learning to train a maximum-likelihood detector. Using these algorithms, the bit error rate (BER) improves by an order of magnitude relative to the approach used in previous works. Moreover, our system achieves a data rate that is an order of magnitude higher than any of the previous MC platforms.