With the growth of capabilities of and accessibility to cloud computing platforms, few research fields based on computer science have benefited as these capabilities allow many more researchers to implement their algorithmic proposals all around the world; this is particularly true for neuroscience and neurotechnology where the research field of neurosignal classification has experienced a considerable increase in scientific publications thanks to this rise in cloud computing. Nonetheless, this new framework does not always result in a satisfactory experience and, in many cases, the learning curve may be very slow even when the right tools are used. Therefore, this paper aims to introduce new researchers in neuroscience and neurotechnology by implementing and assessing classical and modern machine learning techniques for neurosignal classification using Google Colaboratory. Firstly, the basic operation of the Colaboratory platform is described, as well as the most appropriate open source Python libraries for neurosignal research. Then four (two classic and two modern) neurosignal classification techniques are described and carefully cited, and finally, by editing a Python Notebook (created on Google Colaboratory). Finally, we implement and assess performance on a public dataset. In the following, we show results on the classification of electroencephalographic signals associated to motor imagery of the right hand and feet in fourteen experimental subjects.