# Category Archives: Machine Learning

## Gradient Descent in Machine Learning: Python Examples

Last updated: 26th Nov, 2023 In this post, you will learn about gradient descent algorithm and its importance in training machine learning models. For a data scientist, it is of utmost importance to get a good grasp on the concepts of gradient descent algorithm as it is widely used for optimizing / minimizing the objective function / loss function related to various machine learning models such as regression, neural network etc. in terms of learning optimal weights / parameters. This algorithm is essential because it underpins many machine learning models, enabling them to learn from data by optimizing their performance. By understanding gradient descent, one gains insight into how algorithms …

## Learning Curves Python Sklearn Example

Last updated: 26th Nov, 2023 In this post, you will learn about how to use learning curves to assess the improvement in learning performance (accuracy, error rate, etc.) of a machine learning model while implementing using Python (Sklearn) packages. Knowing how to use learning curves will help you assess/diagnose whether the model is suffering from high bias (underfitting) or high variance (overfitting) and whether increasing training data samples could help solve the bias or variance problem. You may want to check some of the following posts in order to get a better understanding of bias-variance and underfitting-overfitting. Bias-variance concepts and interview questions Overfitting/Underfitting concepts and interview questions What are learning curves? …

## Procurement Analytics Use Cases Examples

Last updated: 26th Nov, 2023 The procurement analytics applications is seeing tremendous growth in last few years. With so much data available, advancement in data analytics and related technology field, and the need for digital transformation across procurement organizations, it’s important to know how procurement analytics can help you make better business decisions. This blog will cover procurement analytics and key use cases examples from advanced analytics field such as machine learning, AI, generative AI that will be useful for business stakeholders such as category managers, sourcing managers, supplier relationship managers, business analysts/product managers, and data scientists to implement different use cases using machine learning. The use cases around data-driven decision …

## XGBoost Classifier Explained with Python Example

Among the myriad of machine learning algorithms and techniques available with data scientists, one stands out for its exceptional performance in classification problems: XGBoost, short for eXtreme Gradient Boosting. This algorithm has established itself as a force to reckon with in the data science community, as evidenced by its frequent use and high placements in Kaggle competitions, a platform where data scientists and machine learning practitioners worldwide compete to solve complex data problems. The following plot is taken from Francois Chollet tweet. Above demonstrates the prominence of XGBoost as one of the primary machine learning software tools used by the top-5 teams across 120 Kaggle competitions. The data points in …

## Data Science Explained: Framework, Methods, Examples

What is data science? This is a question that many people who are planning to start learning data science are asking, and for good reason. Data science is increasingly being applied to solve real-world issues, encompassing a broad range of areas. In this blog post, we’re going to explore data science: what it is, the methods it employs, and how it’s applied to solve various problems, with relevant examples. Stick with us, and by the end of this post, you’ll gain a comprehensive understanding of data science and its significance! What is Data Science? Before understanding what is data science, let’s understand what is science? Science can be defined as …

## Bagging Classifier Python Code Example

Last updated: 25th Nov, 2023 Bagging is a type of an ensemble machine learning approach that combines the outputs from many learner to improve performance. The bagging algorithm works by dividing the training set into smaller subsets. These subsets are then processed through different machine-learning models. After processing, the predictions from each model are combined. This combination of predictions is used to generate an overall prediction for each instance in the original data. In this blog post, you will learn about the concept of Bagging along with Bagging Classifier Python code example. Bagging can be used in machine learning for both classification and regression problem. The bagging classifier technique is utilized across a …

## Activation Functions in Neural Networks: Concepts, Examples

Last updated: 24th Nov, 2023 The activation functions are critical to understanding neural networks. There are many activation functions available for data scientists to choose from, when training neural networks. So, it can be difficult to choose which activation function will work best for their needs. In this blog post, we look at different activation functions and provide examples of when they should be used in different types of neural networks. If you are starting on deep learning and wanted to know about different types of activation functions, you may want to bookmark this page for quicker access in the future. What are activation functions in neural networks? In a …

## PCA Explained Variance Concepts with Python Example

Last updated: 24th Nov, 2023 Dimensionality reduction is an important technique in data analysis and machine learning that allows us to reduce the number of variables in a dataset while retaining the most important information. By reducing the number of variables, we can simplify the problem, improve computational efficiency, and avoid overfitting. Principal Component Analysis (PCA) is a popular dimensionality reduction technique that aims to transform a high-dimensional dataset into a lower-dimensional space while retaining most of the information. PCA works by identifying the directions that capture the most variation in the data and projecting the data onto those directions, which are called principal components. However, when we apply PCA, …

## R-squared & Adjusted R-squared: Differences, Examples

There are two measures of the strength of linear regression models: adjusted r-squared and r-squared. While they are both important, they measure different aspects of model fit. In this blog post, we will discuss the differences between adjusted r-squared and r-squared, as well as provide some examples to help illustrate their meanings. As a data scientist, it is of utmost importance to understand the differences between adjusted r-squared and r-squared in order to select the most appropriate linear regression model out of different regression models. What is R-squared? R-squared, also known as the coefficient of determination, is a measure of what proportion of the variance in the value of the …

## Feature Scaling in Machine Learning: Python Examples

While training machine learning models, we come across the need for scaling features in order to have different features contribute to the predictions in an appropriate manner. Without scaling, features with larger numerical ranges can dominate those with smaller ranges, leading to biased or inefficient learning. In this post you will learn about this feature engineering technique namely feature scaling with Python code examples using which you could significantly improve performance of machine learning models. To demonstrate the technique, the models will be trained using Perceptron (single-layer neural network) classifier. What is Feature Scaling? Why is it needed? Feature scaling is a method used to standardize the range of independent variables …

## Machine Learning – Sensitivity vs Specificity Differences, Examples

Last updated: 18th Nov, 2023 Machine learning (ML) models are increasingly being used to learn from data and make decisions or predictions based on that learning. When it comes to evaluating the performance of these ML models, there are several important metrics to consider. One of the most important metrics is the accuracy of the model, which is typically measured using sensitivity and specificity. Sensitivity and specificity are two important concepts often used in the context of classification tasks in machine learning. They help to evaluate the performance of a classification model. In this post, we will try and understand the concepts behind machine learning model evaluation metrics such as …

## PCA vs LDA Differences, Plots, Examples

Last updated: 18th Nov, 2023 Dimensionality reduction is an important technique in data analysis and machine learning that allows us to reduce the number of variables in a dataset while retaining the most important information. By reducing the number of variables, we can simplify the problem, improve computational efficiency, and avoid overfitting. Two popular dimensionality reduction techniques are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Both techniques aim to reduce the dimensionality of the dataset, but they differ in their objectives, assumptions, and outputs. But how do they differ, and when should you use one method over the other? As data scientists, it is important to get a …

## Confusion Matrix Concepts, Python Code Examples

The confusion matrix is an essential tool in the field of machine learning and statistics for evaluating the performance of a classification model. It’s particularly useful when dealing with binary or multi-class classification problems. In this post, you will learn about the confusion matrix with examples and how it could be used as performance metrics for classification models in machine learning. What is Confusion Matrix? A confusion matrix is a table used to describe the performance of a classification model on a set of test data for which the true values are known. It’s most useful when you need to know more about the accuracy of the model than just …

## Maximum Likelihood Estimation: Concepts, Examples

Maximum Likelihood Estimation (MLE) is a fundamental statistical method for estimating the parameters of a statistical model that make the observed data most probable. MLE is grounded in probability theory, providing a strong theoretical basis for parameter estimation. This is becoming more so important to learn fundamentals of MLE concepts as it is at the core of generative modeling (generative AI). Many models used in machine learning and statistics are based on MLE, including logistic regression, survival models, and various types of machine learning algorithms. MLE is particularly important for data scientists because it underpins many of the probabilistic machine learning models that are used today. These models, which are …

## R-squared in Linear Regression Models: Concepts, Examples

In linear regression, R-squared (R2) is a measure of how close the data points are to the fitted line. It is also known as the coefficient of determination. Understanding the concept of R-squared is crucial for data scientists as it helps in evaluating the goodness of fit in linear regression models, compare the explanatory power of different models on the same dataset and communicate the performance of their models to stakeholders. In this post, you will learn about the concept of R-Squared in relation to assessing the performance of multilinear regression machine learning model with the help of some real-world examples explained in a simple manner. Before doing a deep dive, …

## Hierarchical Clustering: Concepts, Python Example

Hierarchical clustering a type of unsupervised machine learning algorithm that stands out for its unique approach to grouping data points. Unlike its counterparts, such as k-means, it doesn’t require the predetermined number of clusters. This feature alone makes it an invaluable method for exploratory data analysis, where the true nature of data is often hidden and waiting to be discovered. But the capabilities of hierarchical clustering go far beyond just flexibility. It builds a tree-like structure, a dendrogram, offering insights into the data’s relationships and similarities, which is more than just clustering—it’s about understanding the story your data wants to tell. In this blog, we’ll explore the key features that …

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