Tag Archives: Data Science
Types of Probability Distributions: Codes, 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 …
Cross Entropy Loss Explained with Python Examples
In this post, you will learn the concepts related to the cross-entropy loss function along with Python code examples and which machine learning algorithms use the cross-entropy loss function as an objective function for training the models. Cross-entropy loss is used as a loss function for models which predict the probability value as output (probability distribution as output). Logistic regression is one such algorithm whose output is a probability distribution. You may want to check out the details on how cross-entropy loss is related to information theory and entropy concepts – Information theory & machine learning: Concepts What’s Cross-Entropy Loss? Cross-entropy loss, also known as negative log likelihood loss, is …
Data Variables Types & Uses in Data Science
In data science, variables are the building blocks of any analysis. They allow us to group, compare, and contrast data points to uncover trends and draw conclusions. But not all variables are created equal; there are different types of variables that have specific uses in data science. In this blog post, we’ll explore the different variable types and their uses in data science. The picture below represents different types of variables one can find when working on statistics / data science projects: Lets understand each types of variables in the following sections. Categorical / Qualitative Variables Categorical variables are a type of data that can be grouped into categories, based …
Procurement Advanced Analytics Use Cases
The procurement analytics applications are poised to grow exponentially in the next few years. With so much data available and the need for digital transformation across procurement organization, 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 of advanced analytics that will be useful for business stakeholders such as category managers, sourcing managers, supplier relationship managers, business analysts / product managers, and data scientists implement different use cases using machine learning. Procurement analytics will allow you to use data very effectively in achieving data-driven decision making. One can get started with procurement analytics with dashboards …
Instance-based vs Model-based Learning: Differences
Machine learning is a field of artificial intelligence that deals with giving machines the ability to learn without being explicitly programmed. In this context, instance-based learning and model-based learning are two different approaches used to create machine learning models. While both approaches can be effective, they also have distinct differences that must be taken into account when building a machine learning system. Let’s explore the differences between these two types of machine learning. What is instance-based learning & how does it work? Instance-based learning (also known as memory-based learning or lazy learning) involves memorizing training data in order to make predictions about future data points. This approach doesn’t require any …
Data Storytelling Explained with Examples
Have you ever told a story to someone, but they just didn’t seem to understand it? They might have been confused about the plot or why the characters acted in certain ways. If this has happened to you before, then you are not alone. Many people struggle with data storytelling because they do not know how to communicate their data effectively. Data storytelling is a powerful tool that can be used to educate, inform or persuade an audience. By using charts, graphs, images and other visuals, data can be made more interesting and engaging. Data storytelling involves taking data and presenting it in a way that is easy to understand and …
Different types of Clustering in Machine Learning
Clustering is a type of unsupervised machine learning technique that is used to group data points into distinct categories or clusters. It is one of the most widely used techniques in machine learning and can be used for various tasks such as grouping customers by their buying habits, creating groups of similar documents, or finding groups of related genes. In this blog post, we will explore different types / categories of clustering methods and discuss why they are so important in the field of machine learning. Prototype-based Clustering Prototype based clustering represents one of the categories of clustering algorithms that are used to identify groups within a larger dataset. This …
Python Pickle Example: What, Why, How
Have you ever heard of the term “Python Pickle“? If not, don’t feel bad—it can be a confusing concept. However, it is a powerful tool that all data scientists, Python programmers, and web application developers should understand. In this article, we’ll break down what exactly pickling is, why it’s so important, and how to use it in your projects. What is Python Pickle? In its simplest form, pickling is the process of converting any object into a byte stream (a sequence of bytes). This byte stream can then be transmitted over a network or stored in a file for later use. It’s like putting the object into an envelope and …
Feature Importance & Random Forest – Python
In this post, you will learn about how to use Random Forest Classifier (RandomForestClassifier) for determining feature importance using Sklearn Python code example. This will be useful in feature selection by finding most important features when solving classification machine learning problem. It is very important to understand feature importance and feature selection techniques for data scientists to use most appropriate features for training machine learning models. Recall that other feature selection techniques includes L-norm regularization techniques, greedy search algorithms techniques such as sequential backward / sequential forward selection etc. What & Why of Feature Importance? Feature importance is a key concept in machine learning that refers to the relative importance of each feature …
Free Datasets for Machine Learning & Deep Learning
Are you looking for free / popular datasets to use for your machine learning or deep learning project? Look no further! In this blog post, we will provide an overview of some of the best free datasets available for machine learning and deep learning. These datasets can be used to train and evaluate your models, and many of them contain a wealth of valuable information that can be used to address a wide range of real-world problems. So, let’s dive in and take a look at some of the top free datasets for machine learning and deep learning! Here is the list of free data sets for machine learning & …
Difference between Online & Batch Learning
In this post, you will learn about the concepts and differences between online and batch or offline learning in relation to how machine learning models in production learn incrementally from the stream of incoming data or otherwise. It is one of the most important aspects of designing machine learning systems. Data science architects would require to get a good understanding of when to go for online learning and when to go for batch or offline learning. Why online learning vs batch or offline learning? Before we get into learning the concepts of batch and on-line or online learning, let’s understand why we need different types of models training or learning …
Moving Average Method for Time-series forecasting
In this post, you will learn about the concepts of the moving average method in relation to time-series forecasting. You will get to learn Python examples in relation to training a moving average machine learning model. The following are some of the topics which will get covered in this post: What is the moving average method? Why use the moving average method? Python code example for the moving average methods What is Moving Average method? The moving average is a statistical method used for forecasting long-term trends. The technique represents taking an average of a set of numbers in a given range while moving the range. For example, let’s say …
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 …
Drivetrain Approach for Machine Learning
In this post, you will learn about a very popular approach or methodology called as Drivetrain approach coined by Jeremy Howard. The approach provides you steps to design data products that provide you with actionable outcomes while using one or more machine learning models. The approach is indeed very useful for data scientists/machine learning enthusiasts at all levels. However, this would prove to be a great guide for data science architects whose key responsibility includes designing the data products. Without further ado, let’s do a deep dive. Why Drivetrain Approach? Before getting into the drivetrain approach and understands the basic concepts, Lets understand why drivetrain approach in the first place? …
Machine Learning Models Evaluation Techniques
Machine learning is a powerful machine intelligence technique that can be used to develop predictive models for different types of data. It has become the backbone of many intelligent applications and evaluating machine learning model performance at a regular intervals is key to success of such applications. A machine learning model’s performance depends on several factors including the type of algorithm used, how well it was trained and more. In this blog post, we will discuss essential techniques for evaluating machine-learning model performance in order to provide you with some best practices when working with machine-learning models. The following are different techniques that can be used for evaluating machine learning …
Data Preprocessing Steps in Machine Learning
Data preprocessing is an essential step in any machine learning project. By cleaning and preparing your data, you can ensure that your machine learning model is as accurate as possible. In this blog post, we’ll cover some of the important and most common data preprocessing steps that every data scientist should know. Replace/remove missing data Before building a machine learning model, it is important to preprocess the data and remove or replace any missing values. Missing data can cause problems with the model, such as biased results or inaccurate predictions. There are a few different ways to handle missing data, but the best approach depends on the situation. In some …
