Machine learning techniques nowadays play a vital role in many burning issues of real-world problems when it involves data. In addition, when the task is complex, people are in dilemma in choosing deep learning techniques or going without them. This paper is about whether we should always rely on deep learning techniques or it is really possible to overcome the performance of deep learning algorithms by simple statistical machine learning algorithms by understanding the application and processing the data so that it can help in increasing the performance of the algorithm by a notable amount. The paper mentions the importance of data preprocessing than that of the selection of the algorithm. It discusses the functions involving trigonometric, logarithmic, and exponential terms and also talks about functions that are purely trigonometric. Finally, we discuss regression analysis on music signals to justify our claim.

翻译：目前,在涉及数据时,机器学习技术在现实世界问题的许多紧迫问题中发挥着关键作用。此外,当任务复杂时,人们在选择深层次学习技术或没有深层次学习技术时处于两难境地。本文讨论的是,我们是否应该始终依靠深层次学习技术,还是真正有可能通过简单的统计机器学习算法,通过理解应用和处理数据来克服深层次学习算法的绩效,从而帮助提高算法的显著程度。本文提到数据预处理的重要性,而不是选择算法的重要性。本文讨论了涉及三角测量、对数和指数术语的功能,并讨论了纯三角测量的功能。最后,我们讨论了音乐信号的回归分析,以证明我们的主张是正确的。