Machine Learning Algorithms

This chapter introduces the different types of algorithms that are used in machine learning to perform different operations. The chapter begins by discussing the necessary concepts of dataset handling, such as data wrangling, feature scaling, different data types, and data splitting. Next, we move on to the detailed classification of some prominent machine learning techniques—supervised learning (regression and classification), deep learning (artificial neural network, convolutional neural network, recurrent neural network, generative adversarial network, and transfer learning), time series forecasting (ARIMA, seasonal ARIMA, and LSTM), unsupervised learning (clustering, dimensionality reduction, and association learning), semi-supervised learning, and reinforcement learning. For each technique, the practical insights are covered with the Python coding implementation of problems relevant to the engineering field. By the end of this chapter, the readers will be familiar with all the popular algorithms and techniques used in machine learning and be able to use them in practical applications.