# Tag Archives: machine learning

## Feature Scaling in Machine Learning: Python Examples

In this post you will learn about a simple technique namely feature scaling with Python code examples using which you could improve machine learning models. The models will be trained using Perceptron (single-layer neural network) classifier. First and foremost, lets quickly understand what is feature scaling and why one needs it? What is Feature Scaling and Why does one need it? Feature scaling is a method used to standardize the range of independent variables or features of data. In data processing, it is also known as data normalization or standardization. Feature scaling is generally performed during the data pre-processing stage, before training models using machine learning algorithms. The goal is to …

## K-Nearest Neighbors (KNN) Python Examples

If you’re working with data analytics projects including building machine learning (ML) models, you’ve probably heard of the K-nearest neighbors (KNN) algorithm. But what is it, exactly? And more importantly, how can you use it in your own AI / ML projects? In this post, we’ll take a closer look at the KNN algorithm and walk through a simple Python example. You will learn about the K-nearest neighbors algorithm with Python Sklearn examples. K-nearest neighbors algorithm is used for solving both classification and regression machine learning problems. Stay tuned! Introduction to K-Nearest Neighbors (K-NN) Algorithm K-nearest neighbors is a supervised machine learning algorithm for classification and regression. In both cases, the input consists …

## What is Machine Learning? Concepts & Examples

Machine learning is a machine’s ability to learn from data. It has been around for decades, but machine learning is now being applied in nearly every industry and job function. In this blog post, we’ll cover a detailed introduction to what is machine learning (ML) including different definitions. We will also learn about different types of machine learning tasks, algorithms, etc along with real-world examples. What is machine learning & how does it work? Simply speaking, machine learning can be used to model our beliefs about real-world events. For example, let’s say a person came to a doctor with a certain blood report. A doctor based on his belief system …

## How to Identify Use Cases for AI / Machine Learning

As artificial intelligence (AI ) and machine learning (ML) solutions and technologies continue to evolve, more and more businesses are looking for ways to incorporate them into their operations to realize a greater business impact. But with so many potential applications, it can be difficult to know where to start. In this blog post, we’ll outline some tips for identifying AI / ML use cases. We’ll also provide a few examples of how AI & machine learning can be used in business settings. So if you’re thinking about adding AI or machine learning to your toolkit, read on! This blog post will be appropriate for product managers, business analysts, data science …

## Predicting Customer Churn with Machine Learning

Customer churn, also known as customer attrition, is a major problem for businesses that rely on recurring revenue. Customer churn costs businesses billions of dollars every year, and it’s only getting worse as customers become more and more fickle. In fact, it’s been estimated that the average company loses 10-15% of its customers each year. That number may seem small, but it can have a huge impact on a company’s bottom line. Fortunately, there’s a way to combat churn: by using machine learning to predict which customers are likely to churn. In this blog post, we’ll discuss how customer churn prediction works and why it’s so important. We’ll also provide …

## Stacking Classifier Sklearn Python Example

In this blog post, we will be going over a very simple example of how to train a stacking classifier machine learning model in Python using the Sklearn library and learn the concepts of stacking classifier. A stacking classifier is an ensemble learning method that combines multiple classification models to create one “super” model. This can often lead to improved performance, since the combined model can learn from the strengths of each individual model. What are Stacking Classifiers? Stacking is a machine learning ensemble technique that combines multiple models to form a single powerful model. The individual models are trained on different subsets of the data using some type of …

## Decision Tree Hyperparameter Tuning Grid Search Example

The output prints out grid search across different values of hyperparameters, the model score with best hyperparameters and the most optimal hyperparameters value. In the above code, the decision tree model is train and evaluate our for each value combination and choose the combination that results in the best performance. In this case, “best performance” could be defined as either accuracy or AUC (area under the curve). Once we’ve found the best performing combination of hyperparameters, we can then train our final model using those values and deploy it to production. Conclusion In this blog post, we explored how to use grid search to tune the hyperparameters of a Decision …

## Reinforcement Learning Real-world examples

In this blog post, we’ll learn about some real-world / real-life examples of Reinforcement learning, one of the different approaches to machine learning where other approaches are supervised and unsupervised learning. Reinforcement learning is a type of machine learning that enables a computer system to learn how to make choices by being rewarded for its successes. This can be an extremely powerful tool for optimization and decision-making. It’s one of the most popular machine learning methods used today. Before looking into the real-world examples of Reinforcement learning, let’s quickly understand what is reinforcement learning. Introduction to Reinforcement Learning (RL) Reinforcement learning is an approach to machine learning in which the agents …

## Passive Aggressive Classifier: Concepts & Examples

The passive aggressive classifier is a machine learning algorithm that is used for classification tasks. This algorithm is a modification of the standard Perceptron algorithm. The passive aggressive classifier was first proposed in 2006 by Crammer et al. as a way to improve the performance of the Perceptron algorithm on linearly separable data sets. In this blog, we will learn about the basic concepts and principles behind the passive aggressive classifier, as well as some examples of its use in real-world applications. What is the passive aggressive classifier and how does it work? The passive aggressive classifier algorithm falls under the category of online learning algorithms, can handle large datasets, …

## Generalized Linear Models Explained with Examples

Generalized linear models (GLMs) are a powerful tool for data scientists, providing a flexible way to model data. In this post, you will learn about the concepts of generalized linear models (GLM) with the help of Python examples. It is very important for data scientists to understand the concepts of generalized linear models and how are they different from general linear models such as regression or ANOVA models. What are Generalized Linear Models? Generalized linear models (GLM) are a type of statistical models that can be used to model data that is not normally distributed. It is a flexible general framework that can be used to build many types of regression models, including …

## Import or Upload Local File to Google Colab

Google Colab is a powerful tool that allows you to run Python code in the cloud. This can be useful for a variety of tasks, including data analysis and machine learning. One of the lesser known features of Google Colab is that you can also import or upload files stored on your local drive. In this article, we will show you how to read a file from your local drive in Google Colab using a quick code sample. There are a few reasons why you as a data scientist might need to learn how to read files from your local drive in Google Colab. One reason is that you may …

## Ridge Classification Concepts & Python Examples

In machine learning, ridge classification is a technique used to analyze linear discriminant models. It is a form of regularization that penalizes model coefficients to prevent overfitting. Overfitting is a common issue in machine learning that occurs when a model is too complex and captures noise in the data instead of the underlying signal. This can lead to poor generalization performance on new data. Ridge classification addresses this problem by adding a penalty term to the cost function that discourage complexity. This results in a model that is better able to generalize to new data. In this post, you will learn about Ridge classifier in detail with the help of …

## PCA vs LDA Differences, Plots, Examples

Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two of the most popular dimensionality reduction techniques. Both methods are used to reduce the number of features in a dataset while retaining as much information as possible. But how do they differ, and when should you use one method over the other? As data scientists, it is important to get a good understanding around this concept as it is used in building machine learning models. Keep reading to find out with the help of Python code & examples. How does PCA work? Principal Component Analysis (PCA) works by identifying the directions (components) that maximize the variance in a dataset. …

## Different Types of Probability Distributions: Examples

In this post, you will learn the definition of 25 different types of probability distributions. Probability distributions play an important role in statistics and in many other fields, such as economics, engineering, and finance. They are used to model all sorts of real-world phenomena, from the weather to stock market prices. Before we get into understanding different types of probability distributions, let’s understand some fundamentals. If you are a data scientist, you would like to go through these distributions. This page could also be seen as a cheat sheet for probability distributions. What are Probability Distributions? Probability distributions are a way of describing how likely it is for a random …

## How to Create Pandas Dataframe from Numpy Array

Pandas is a library for data analysis in Python. It offers a wide range of features, including working with missing data, handling time series data, and reading and writing data in different formats. Pandas also provides an efficient way to manipulate and calculate data. One of its key features is the Pandas DataFrame, which is a two-dimensional array with labeled rows and columns. A DataFrame is a table-like structure that contains columns and rows of data. Creating a Pandas DataFrame from a NumPy array is simple. In this post, you will get a code sample for creating a Pandas Dataframe using a Numpy array with Python programming. Step 1: Load …

## Ensemble Methods in Machine Learning: Examples

Machine learning models are often trained with a variety of different methods in order to create a more accurate prediction. Ensemble methods are one way to do this, and involve combining the predictions of several different models in order to get a more accurate result. When different models make predictions together, it can help create a more accurate result. Data scientists should care about this because it can help them create models that are more accurate. In this article, we will look at some of the common ensemble methods used in machine learning. Data scientists should care about this because it can help them create models that are more accurate. …