Modeling and Optimization of Signals Using Machine Learning Techniques

11Unlocking Machine Learning: 10 Innovative Avenues to Grasp Complex Concepts

K. Vidhyalakshmi^1* and S. Thanga Ramya²

¹Veltech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Tamilnadu, India

²RMK Engineering College, Chennai, Tamilnadu, India

Abstract

The mathematical statement that expresses facts in the context of a problem is a model often known as a machine learning (ML) algorithm. Let us have a look at 10 alternative approaches for demystifying ML and offering a knowledge route used by individuals unfamiliar with fundamental ideas, each with clear descriptions, images, and examples. The objective is to move from facts and information to knowledge. For illustration, if an online business needs to forecast profit sales in the coming part, it can use ML techniques that forecast sale relation with prior sales with related data. For example, a windmill maker may utilize video surveillance to monitor critical equipment and send data to algorithms that might spot faults. The 10 methods listed below provide an overview — as well as a foundation upon which we can construct as your ML acquaintance and experience grows:

1. Regression
2. The classification system
3. The clustering
4. Adaptive learning
5. The ensemble methods
6. The reinforcement
7. The dimensionality reduction
8. Neural networks and deep learning
9. Natural language processing (NLP)
10. The word embeddings

Let us first learn the difference between supervised and unsupervised ML techniques. We employ the supervised (with ...