The classification of data using machine learning involves different challenging tasks which depend on the learning method, selection of neurons, selection of dataset, selection of algorithm etc. This paper deals with classifying Escherichia Coli bacteria proteins from the amino acid sequence using Meta-cognitive learning. Different machine learning techniques are implied on the imbalanced dataset of E-Coli with 10 cross-fold validations to prove the performance of Meta-cognitive Neural Network (McNN) .McNN is capable of learning what –to-learn, when-to-learn and how-to-learn. Extreme Learning Machine (ELM) a batch learning algorithm, Self – adaptive Resource Allocation Network (SRAN) a sequential learning algorithm and Meta- cognitive Neural Network (McNN) which employs meta-cognition in sequential learning are applied for experimental study. This paper shows that classification of McNN performs well with respect to other machine learning algorithms.