Category: Machine Learning

Machine Learning, Tutorials

Machine Learning with Python: K Means Clustering

Introduction K Means clustering is a popular machine learning algorithm used for grouping data points into distinct clusters based on their similarities. This powerful technique is widely used in various fields such as finance, marketing, biology and many more. K Means clustering is an unsupervised learning algorithm which means it doesn’t require the input data […]

Data Science, Machine Learning, Tutorials

Machine Learning with Python: K Nearest Neighbors

Introduction K Nearest Neighbors (KNN) is a popular supervised machine learning algorithm that has been widely used in a variety of fields, including marketing, healthcare, and image recognition. It is a simple yet powerful algorithm that belongs to the category of instance-based learning or lazy learning. The KNN algorithm can be applied both for classification […]

Data Science, Machine Learning, Python Basics

Machine Learning with Python: Linear Regression

Introduction In this blog post, we’ll be exploring Linear Regression in Machine Learning with Python.  There are many potential applications for linear regression, especially for your business, including: Sales forecasting: Linear regression can be used to predict future sales based on historical data, such as product pricing, marketing expenses, and consumer demographics. Inventory management: Linear […]

Data Science, Machine Learning

7 Regression Algorithms Used in Python for Machine Learning

Regression analysis is a commonly used statistical technique for predicting the relationship between a dependent variable and one or more independent variables. In the field of machine learning, regression algorithms are used to make predictions about continuous variables, such as housing prices, student scores, or medical outcomes. Python, being one of the most widely used […]

Data Science, Machine Learning

Self Supervised Learning

    Deep Learning without labels – Self-Supervised Learning¶     In this blog post we’ll discuss Self-Supervised Learning!     Classical supervised learning suffers from four main problems: Fully labelled datasets are expensive or not available at all. There is a large amount of unlabeled datasets which cannot be leveraged by Supervised Learning. Difficuly […]

Machine Learning

How To Interpret The ROC Curve

The ROC curve is a valuable tool to measure the performance and then fine-tune classification models, as they show you the trade-off in sensitivity and specificity for a specific classifier at various thresholds. Despite this, some might find ROC curves difficult to understand. In this post, we’ll aim to eliminate this difficulty by providing a […]

Machine Learning

Understanding Random Forest Algorithm

The Random Forest algorithm is a machine learning technique that is used to predict the outcomes of events. It is a type of ensemble learning, which means that it combines the predictions of multiple models in order to produce a better prediction. A Random Forest algorithm is commonly used for classification and regression tasks. In […]

Data Science, Machine Learning

How to interpret box plots

Box plots are a great way to visualize your data. They can help you see the distribution of your data, as well as any outliers that may be present. In this article, we will show you how to interpret box plots, and give some examples of how they can be used in machine learning applications. […]