Machine Learning Algorithms for Data Scientists: An Overview
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About this ebook
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.
Vinaitheerthan Renganathan
Statistician and Data Scientist with 26 years of experience in the field of Clincal,Manufacturing, Quality Assurance and Marketing Research.
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Machine Learning Algorithms for Data Scientists - Vinaitheerthan Renganathan
Machine Learning Algorithms for Data Scientists: An Overview
Machine Learning Algorithms for Data Scientists: An Overview
Preface
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.
Vinaitheerthan Renganathan
Title: Machine Learning Algorithms for Data Scientists: An Overview
Author: Vinaitheerthan Renganathan
Publisher: Self Published
Edition: 1st Edition
Copyright: © 2021 Vinaitheerthan Renganathan
Contents
Chapter 1: Introduction
Chapter 2: R Software
Chapter 3: Data Preprocessing
Chapter 3: Decision Tree Algorithm
Chapter 4: Random Forest Algorithm
Chapter 5: Support Vector Machine Algorithm
Chapter 6: Naïve Bayes Algorithm
Chapter 7: Artificial Neural Network Algorithm
Chapter 8: Clustering Algorithm
Chapter 9: Text mining algorithms
Chapter 10: Image processing and classification
Chapter 1: Introduction
Machine learning term became a buzz word in recent times due to its capabilities, easy to implement characteristics and high computing power. Machine learning models uses data to learn and take decision related to classification, pattern recognition and prediction. Machine Models improve the task under consideration through learning from the data and evaluating the results based on the performance measures.
Machine learning models are being applied in different fields to carry out the following tasks (not limited to)
Speech Recognition
Pattern Recognition
Computer Vision
Robotic Control
Disease progression
Fraud detection
Spam filtering
Network intrusion detection
Self-driving cars
Recommender systems
There are different types of Machine Learning algorithms are available. Supervised, Unsupervised and Reinforcement Learning Algorithms
Supervised learning algorithms involve training and test data sets. The algorithm is trained on the training data set which contains outcome variable which needs to be classified or predicted i.e. Class label in case of classification problem or predicted value in case of regression problem. In some cases test data set used in the ML algorithms will not contain the outcome variable i.e. class label or predicted values which will be used to test the predictive accuracy of the machine learning algorithms. One of the issues with the supervised learning algorithm is to get the class label for the training data set and it might not be available for a large dataset.
Unsupervised learning algorithm