Machine Learning- Page 7 of 13

3 Concepts Every Data Scientist Must Know Part – 3

1. What is the significance of sampling? Name some techniques for sampling? For analyzing the data, we cannot proceed with the whole volume at once for large datasets. We need to take some samples from the data which can represent the whole population. While making a sample out of complete data, we should take the…

3 Concepts Every Data Scientist Must Know Part – 2

1. Bagging and Boosting Bagging and Boosting are two different ways used in combining base estimators for ensemble learning (Like random forest combining decision trees). Bagging means aggregating the predictions of several weak learners. We can think of it combining weak learners is used in parallel. The average of the predictions of several weak learners…

3 Concepts Every Data Scientist Must Know Part – 1

Central Limit Theorem We first need to introduce the normal (gaussian) distribution for central limit theorem to make sense. Normal distribution is a probability distribution that look like a bell. X-axis represents the values and y-axis represents the probability of observing these values. The sigma values represent standard deviation normal distribution is used to represent…

Most Common Feature Scaling methods in Machine Learning

Definition Feature scaling is the process of normalizing the range of feature in a dataset. Real-world datasets often contain features that are varying in degrees of magnitude, range and units. Therefore, in order for machine learning models to interpret these features on the same scale, we need to perform scaling. Feature scaling makes the model…

Stress Detection Project using Machine Learning

Stress, tension, and misery are undermining the psychological well-being of individuals. Each individual has a justification behind having an unpleasant life. Individuals frequently discuss their thoughts via web-based entertainment stages like on Instagram as posts and stories, and on Reddit through requesting ideas about their life on subreddits. In the beyond couple of years, many…

Outlier Detection methods in Machine Learning

Objective An outlier is an individual point of data that is distant from other points in the dataset. It is an anomaly in the dataset that may be caused by a range of errors in capturing, processing or manipulating data. Outliers in the data may cause problem during model fitting as it may inflate the…

Missing Values Treatment methods in Machine Learning

Delete Missing Value Rows Missing values can be handled by deleting the rows or columns having null values. If columns have more than half of the rows as null then the entire columns can be dropped. The rows which are having one or more columns values as null can also dropped. Pros: A model trained…

Restaurant Recommendation System using Machine Learning

In this article we are going to discuss about the Restaurant Recommendation System. it is an application that recommends similar restaurants to a customer according to the customer’s taste. We will learn how to build a restaurant recommendation system. This article will take you through how to build a restaurant recommendation system using Machine Learning.…

Hierarchical clustering for Machine Learning

Hierarchical clustering is another unsupervised machine learning algorithm, which is used to group the unlabeled datasets into a cluster. Hierarchical Clustering creates clusters in a hierarchical tree-like structure (also called a Dendogram) as it creates a subset of similar data in a tree-like structure in which the root node corresponds to the entire data, and…

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…

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Category: Machine Learning