Learn Using Machine Learning To Predict Breast Cancer

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Learn Using Machine Learning To Predict Breast Cancer  


Machine Learning is the branch of computer science that deals with the development of computer programs that teach and grow themselves. According to Arthur Samuel, an American pioneer in computer gaming, Machine Learning is the subfield of computer science that "gives the computer ability to learn without being explicitly programmed." Machine Learning allows developers to build algorithms that automatically improve themselves by finding patterns in the existing data without explicit instructions from a human or developer. Machine Learning relies entirely on the data; the more the data, the more efficient Machine Learning is. The Evolution of Machine Learning Eduonix Learning Solutions


1. Supervised learning:  Computer is presented with inputs and their desired outputs. The goal is to learn a general rule to map inputs to the output. 2. Unsupervised learning:  Computer is presented with inputs without desired outputs, the goal is to find structure in inputs. 3. Reinforcement learning:   Computer program interacts with a dynamic environment, and it must perform a certain goal without guide or teacher. Machine learning classification Eduonix Learning Solutions


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Involvement of Machine Learning for Breast Cancer Image  Breast cancer is one of the largest causes of women’s death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors’ and physicians’ time. Eduonix Learning Solutions


Anatomy of the female breast images Eduonix Learning Solutions


Breast Image Classification A general breast image classifier consists of four stages Selection of a breast database Feature extraction and selection Classifier model Performance measuring parameter Classifier output. Eduonix Learning Solutions


Logistic Regression (LR) Linear Discriminant Analysis (LDA) Quadratic Discriminant Analysis (QDA) Random Forest (RF) Support Vector Machine (SVM) ML predictions are used to detect breast cancer biopsy Eduonix Learning Solutions


Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. The outcome is measured with a dichotomous variable (in which there are only two possible outcomes). Logistic Regression (LR) Eduonix Learning Solutions


Linear Discriminant Analysis Linear Discriminant Analysis (LDA) is most commonly used as dimensionality reduction technique in the pre-processing step for pattern-classification and machine learning applications. Eduonix Learning Solutions


Quadratic discriminant analysis (QDA) is closely related to linear discriminant analysis (LDA), where it is assumed that the measurements from each class are normally distributed.  Quadratic Discriminant Analysis Eduonix Learning Solutions


Random Forest (RF) Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Eduonix Learning Solutions


Support Vector Machine (SVM) Support vector machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.  Eduonix Learning Solutions


No. of people facing cancer in Australia from 2007 to 2018 Eduonix Learning Solutions


No. of people dying due to cancer in Australia from 2007 to 2018 Eduonix Learning Solutions


A complete course where you will learn to implement cutting edge machine learning algorithms to solve real world problems. We have carefully selected the projects which will cover important aspect of Machine learning such as Supervised Learning, Unsupervised learning and Neural network with deep learning. You will start with real world data available publicly to create these Machine Learnings Projects. It will be a course for serious developers but will be fun and engaging. You will learn step by step implementation and can be a professional ML developer after completing this course. Eduonix Learning Solutions Course Overview


Building real world projects in this course Stock Market Clustering Breast cancer malignancies Diabetes onset detection Credit card fraud detection Predicting board game reviews Eduonix Learning Solutions


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