What to read after Machine Learning Algorithms for Data Scientists: An Overview?

Machine Learning models are widely used in different fields such as Artificial Intelligence, Business, Clinical and Biological Sciences which includes self-driving cars, predictive models, disease prediction, genome sequencing, spam filtering, product recommendation, fraud detection and image recognition . It has gained importance due to its capabilities of handling large volume of data, prediction and classification accuracy and validation procedures.

Machine Learning models are built on the basis of statistical and mathematical algorithms. One important aspect of machine learning is it does not stick to standard algorithm throughout modeling process instead it learns from the data over a period of time and improves the accuracy of the model.  Classification and prediction tasks are carried out based on the characteristics, patterns and relationship of the features present in the data set. Machine learning model also forms the basis of Deep Learning models.

Machine Learning models involve supervised learning, unsupervised learning, semi supervised learning and reinforcement learning algorithms.

Data Scientists analyze, model and visualize data and provide actionable insights to the decision makers. Machine learning algorithms and tools help the data scientist to carry out these tasks with the help of software such R and Python.

This book provides an overview of Machine Learning models, algorithms and its application in different fields through the use of R Software. It also provides short introduction to R software for the benefit of users.

Author assumes the users have basic descriptive and inferential statistical knowledge which is essential for building Machine Learning models.

Data sets used in the books can be downloaded from the author’s website.