In order to implement the advantages of machine learning in the cybersecurity ecosystem, various anomaly detection-based models are being developed owing to their ability to flag zero-day attacks over their signature-based counterparts. The development of these anomaly detection-based models depends heavily on the dataset being employed in terms of factors such as wide attack pool or diversity. The CICIDS 2017 stands out as a relevant dataset in this regard. This work involves an analytical comparison of the performances by selected shallow machine learning algorithms as well as a deep learning algorithm leveraging the CICIDS 2017 dataset. The dataset was imported, pre-processed and necessary feature selection and engineering carried out for the shallow learning and deep learning scenarios respectively. Outcomes from the study show that the deep learning model presented the highest performance of all with respect to accuracy score, having percentage value as high as 99.71% but took the longest time to process with 550 seconds. Furthermore, some shallow learning classifiers such as Decision Tree and Random Forest took less processing time (4.567 and 3.95 seconds respectively) but had slightly less accuracy scores than the deep learning model with the CICIDS 2017 dataset. Results from our study show that Deep Neural Network is a viable model for intrusion detection with the CICIDS 2017 dataset. Furthermore, the results of this study are to provide information that may influence choices while developing machine learning based intrusion detection systems with the CICIDS 2017 dataset.