Machine Learning Algorithms For Data Scientists An Overview
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Machine Learning Algorithms for Data Scientists: An Overview
Author: Vinaitheerthan Renganathan
language: en
Publisher: Vinaitheerthan Renganathan
Release Date: 2021-06-02
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.
Machine Learning Algorithms and Concepts
This book is for machine learning professional & aspiring data scientist who wanted to be established themselves as a machine learning engineer or data science professional. Machine Learning Algorithms & Concepts gives complete idea to begin the phase of machine learning professional. This can be referred as a great starting point to switch the career path from existing profession to a machine learning professional. The book covers all major algorithms, its concept, usage, and other miscellaneous concepts based on situation which helps to its reader to decide in which situation what to be used. This book serves as guide to prepare for interviews, exams, campus work as well as for industry professional. It also covers basic programming which gives fair idea to its reader to learn how to code for machine learning problem statement even if he is a beginner in coding.
MACHINE LEARNING FOR DATA SCIENCE - USING ML ALGORITHMS FOR PREDICTIVE MODELING
Author: Dr. Rachit Adhvaryu
language: en
Publisher: Xoffencerpublication
Release Date: 2024-05-14
Machine learning is an area of artificial intelligence that focuses on teaching computers how to learn without being explicitly instructed to do so. This ability allows computers to acquire knowledge and competence via experience rather than being taught to do so. In recent years, as a consequence of the many different applications it has in a broad variety of fields, it has become an increasingly important topic of debate as a result of the multiple practical uses it has. Throughout the course of this blog, we will discuss how machine learning is being utilized to address difficulties in the real world, as well as study the principles of machine learning and go into more advanced topics. Whether you are a newbie interested in learning about machine learning or an experienced data scientist wishing to keep up to speed on the latest breakthroughs in the field, we hope that you will find something here that is of interest to you. If you are a novice interested in learning about machine learning, go here. Machine learning is an application of artificial intelligence that makes use of statistical methods to teach computers how to learn on their own and make judgements without being expressly programmed to do so. This is accomplished via the use of statistical methods. It is predicated on the notion that computers are able to learn from data, spot patterns, and make decisions with relatively little input from human beings