With the rising popularity of machine learning in the past decade, a stronger urgency has been placed on drastically improving computational technology. Despite recent advancements in this industry, the speed at which our technologies can complete machine learning tasks continues to be its most significant bottleneck. Modern machine learning algorithms are notorious for requiring a substantial amount of computational power. As the demand for computational power increases, so does the demand for new ways to improve the speed of these algorithms. Machine learning researchers have turned to leverage quantum computation to significantly improve their algorithms' time complexities. This counteracts the physical limitations that come with the chips used in our technology today. This paper questions current classical machine learning practices by comparing them to their quantum alternatives and addressing the applications and limitations of this new approach.