Category: Machine Learning
Difference between Data Science and Machine Learning
Data Science Data science is a field that studies data and how to extract meaning from it, using a series of methods, algorithms, systems, and tools to extract insights from structured and unstructured and unstructured data. That knowledge then gets applied to business, government, and other bodies to help drive profits, innovate products and services…
Read MoreDifference between Data Scientist and Data Analyst
What are their skills? Data Analyst Data Mining Data Warehousing Math, Statistics Tableau and data visualization SQL Business Intelligence Advanced Excel skills Data Scientist Data Mining Data Warehousing Math, Statistics, Computer Science Tableau and Data Visualization/Storytelling Python, R, JAVA, Scala, SQL, Matlab, Pig Economics Big Data/Hadoop Machine Learning Educational requirements Data Analyst Foundational math, statistics…
Read MoreDifference between Data Scientist and Data Engineer
What do they do? Data Engineers Data Engineers design, build, test, integrate, and optimize data collected from multiple sources. They use Big Data tools and technologies to construct free-flowing data pipelines that facilitate real-time analytics applications on complex data. Data Engineers also write complex queries to improve data accessibility. Data Scientist Data Scientists are more…
Read MoreDifference between Big Data and Data Science
Big Data Hugh volumes of data which cannot be handled using traditional database programming. Characterized by volume, variety, and velocity. Data Science A data-focused on scientific activity. Approaches to process big data. Harnesses the potential of big data for business decisions. Similar to data mining. Concept Big Data Diverse data types generated from multiple data…
Read MoreCredit Card Fraud Detection using Machine Learning
As we’re moving towards the digital world — cybersecurity is getting a critical part of our life. When we talk about security in digital life also the main challenge is to find the abnormal activity. When we make any transaction while buying any product online — a good amount of people prefer credit cards. The…
Read MoreK-Means algorithm for Machine Learning
K-Means Clustering is an Unsupervised Learning algorithm, which groups the unlabeled dataset into different clusters. It allows us to cluster the info into different groups and a convenient way to discover the categories of groups in the unlabeled dataset on its own without the need for any training. The k-means clustering algorithm mainly performs two…
Read MoreDBSCAN algorithm for Machine Learning
Density-based special clustering of applications with noise or DBSCAN is a density-based clustering method that calculates how dense the neighborhood of a data point is. the main idea behind DBSCAN is that a point belongs to a cluster if it is close to many from that cluster. It will measure the similarity between data points,…
Read MoreSupport Vector Machine algorithm for Machine Learning
Support vector Machine or SVM is a Supervised Learning algorithm, which is used for Classification and Regression problems. However, primarily, it is used for classification problems in Machine Learning. The goal of the SVM algorithm is to create the decision boundary that can segregate n-dimensional space into classes so that we can easily classify new…
Read MoreBreast Cancer Detection Project using ML
Breast cancer (BC) is one among the foremost common cancers among ladies worldwide, representing the bulk of recent cancer cases and cancer-related deaths in line with world statistics, creating it a major public ill health in today’s society. The early diagnosing of BC will improve the prognosis and probability of survival considerably, because it will promote timely clinical treatment to patients. any correct classification of benign tumors will stop patients undergoing supernumerary…
Read MoreRandom Forest algorithm for Machine Learning
Random Forest is better than Decision Tree as the greater number of trees in the forest leads to higher accuracy and prevents the problem of overfitting. Algorithm working Random Forest uses a Bagging technique with one modification, where subset of features are used for finding best split. Advantages & Disadvantages Disadvantages Popular Posts
Read More