Category Archives: Data Science
Self-Supervised Learning: Concepts, Examples
Self-supervised learning is a hot topic in the world of data science and machine learning. It is an approach to training machine learning models using unlabeled data, which has recently gained significant traction due to its effectiveness in various applications. Self-supervised learning differs from supervised learning, where models are trained using labeled data, and unsupervised learning, where models are trained using unlabeled data without any pre-defined objectives. Instead, self-supervised learning defines pretext tasks as training models to extract useful features from the data that can be later fine-tuned for specific downstream tasks. The potential of self-supervised learning has already been demonstrated in many real-world applications, such as image classification, natural …
Gaussian Mixture Models: What are they & when to use?
In machine learning and data analysis, it is often necessary to identify patterns and clusters within large sets of data. However, traditional clustering algorithms such as k-means clustering have limitations when it comes to identifying clusters with different shapes and sizes. This is where Gaussian mixture models (GMMs) come in. But what exactly are GMMs and when should you use them? Gaussian mixture models (GMMs) are a type of machine learning algorithm. They are used to classify data into different categories based on the probability distribution. Gaussian mixture models can be used in many different areas, including finance, marketing and so much more! In this blog, an introduction to gaussian …
Seaborn: Multiple Line Plots with Markers, Legend
Do you want to learn how to create visually stunning and informative line plots that will captivate your audience by providing most apt information? Do you have the requirement of creating multiple line plots in the same figure representing sales of different products across different months in a year? Are you looking for a takeaway Python code with Seaborn library for creating line plots? If yes, you are in the right place. In this blog post, we’ll explore how to create multiple line plots with Seaborn, a powerful data visualization library built on top of Matplotlib. I will also show how to add markers to the line plots to make …
ChatGPT for Data Science Projects – Examples
Data science is all about turning raw data into actionable insights and outcomes that drive value for your organization. But as any data science professional knows, coming up with new, innovative ideas for your projects is only half the battle. The real challenge is finding a way to turn those ideas into results that can be used to drive business success by doing proper data analysis and building machine learning models using most appropriate algorithms. Unfortunately, many data science professionals struggle with this second step, which can lead to frustration, wasted time and resources, and missed opportunities. That’s where ChatGPT comes in. As a language model trained by OpenAI, ChatGPT …
Hypothesis Testing in Business: Examples
Are you a product manager or data scientist looking for ways to identify and use most appropriate hypothesis testing for understanding business problems and creating solutions for data-driven decision making? Hypothesis testing is a powerful statistical technique that can help you understand problems during exploratory data analysis (EDA) and identify most appropriate hypotheses / analytical solution. In this blog, we will discuss hypothesis testing with examples from business. We’ll also give you tips on how to use it effectively in your own problem-solving journey. With this knowledge, you’ll be able to confidently create hypotheses, run experiments, and analyze the results to derive meaningful conclusions. So let’s get started! Before going …
Sklearn Algorithms Cheat Sheet with Examples
The Sklearn library, short for Scikit-learn, is one of the most popular and widely-used libraries for machine learning in Python. It offers a comprehensive set of tools for data analysis, preprocessing, model selection, and evaluation. As a beginner data scientist, it can be overwhelming to navigate the various algorithms and functions within Sklearn. This is where the Sklearn Algorithms Cheat Sheet comes in handy. This cheat sheet provides a quick reference guide for beginners to easily understand and select the appropriate algorithm for their specific task. In this cheat sheet, I have compiled a list of common supervised and unsupervised learning algorithms, along with their Sklearn classes and example use …
Supervised & Unsupervised Learning Difference
Supervised and unsupervised learning are two different common types of machine learning tasks that are used to solve many different types of business problems. Supervised learning uses training data with labels to create supervised models, which can be used to predict outcomes for future datasets. Unsupervised learning is a type of machine learning task where the training data is not labeled or categorized in any way. For beginner data scientists, it is very important to get a good understanding of the difference between supervised and unsupervised learning. In this post, we will discuss how supervised and unsupervised algorithms work and what is difference between them. You may want to check …
Sklearn Neural Network Example – MLPRegressor
Are you interested in using neural networks to solve complex regression problems, but not sure where to start? Sklearn’s MLPRegressor can help you get started with building neural network models for regression tasks. While the packages from Keras, Tensorflow or PyTorch are powerful and widely used in deep learning, Sklearn’s MLPRegressor is still an excellent choice for building neural network models for regression tasks when you are starting on. Recall that Python Sklearn library is one of the most popular machine learning libraries, and it provides a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. In this blog post, we will be focusing on training a …
Neural Networks Interview Questions – Quiz #45
Are you preparing for a job interview in the field of deep learning or neural networks? If so, you’re likely aware of how complex and technical these topics can be. In order to help you prepare, we’ve put together a list of common neural network interview questions and answers in form of multiple-choice quiz. The quiz in this blog post covers basic concepts related to neural network layers, perceptron, multilayer perceptron, activation functions, feedforward networks, backpropagation, and more. We’ve included 15 multiple-choice questions, as well as 5 additional questions specifically focused on the backpropagation algorithm. I will be posting many more quizzes on the neural networks in time to come, …
Google’s Free Machine Learning Courses: Learn from the Best
Machine learning has become a fundamental part of almost every industry today. With the increasing demand for data scientists and machine learning engineers, it has become imperative for professionals to keep themselves updated with the latest tools and techniques. Fortunately, Google offers a range of free machine learning courses that cater to professionals of all expertise levels. In this blog, we will explore the top Google machine learning courses that will help learners enhance their skills and stay ahead of the game. List of Free Machine Learning Courses by Google The following is a list of free machine learning courses from Google which you can take online. These courses can …
KMeans Silhouette Score Python Example
If you’re building machine learning models for solving different prediction problems, you’ve probably heard of clustering. Clustering is a popular unsupervised learning technique used to group data points with similar features into distinct clusters. One of the most widely used clustering algorithms is KMeans, which is popular due to its simplicity and efficiency. However, one major challenge in clustering is determining the optimal number of clusters that should be used to group the data points. This is where the Silhouette Score comes into play, as it helps us measure the quality of clustering and determine the optimal number of clusters. Silhouette score helps us get further clarity for the following …
Why & When to use Eigenvalues & Eigenvectors?
Eigenvalues and eigenvectors are important concepts in linear algebra that have numerous applications in data science. They provide a way to analyze the structure of linear transformations and matrices, and are used extensively in many areas of machine learning, including feature extraction, dimensionality reduction, and clustering. In simple terms, eigenvalues and eigenvectors are the building blocks of linear transformations. Eigenvalues represent the scaling factor by which a vector is transformed when a linear transformation is applied, while eigenvectors represent the directions in which the transformation occurs. In this post, you will learn about why and when you need to use Eigenvalues and Eigenvectors? As a data scientist/machine learning Engineer, one must …
Z-score or Z-statistics: Concepts, Formula & Examples
Z-score, also known as the standard score or Z-statistics, is a powerful statistical concept that plays a vital role in the world of data science. It provides a standardized method for comparing data points from different distributions, allowing data scientists to better understand and interpret the relative positioning of individual data points within a dataset. Z-scores represent a statistical technique of measuring the deviation of data from the mean. It is also used with Z-test which is a hypothesis testing statistical technique (one sample Z-test or two samples Z-test). As a data scientist, it is of utmost importance to be well-versed with the z-score formula and its various applications. Having …
Descriptive Statistics – Key Concepts & Examples
Descriptive statistics is a branch of statistics that deals with the analysis of data. It is concerned with summarizing and describing the characteristics of a dataset. It is one of the most fundamental tool for data scientists to understand the data as they get started working on the dataset. In this blog post, I will cover the key concepts of descriptive statistics, including measures of central tendency, measures of spread and statistical moments. What’s Descriptive Statistics & Why do we need it? Descriptive statistics is used to summarize and describe the characteristics of a dataset in terms of understanding its mean & related measures, spread or dispersion of the data …
Backpropagation Algorithm in Neural Network: Examples
Artificial Neural Networks (ANN) are a powerful machine learning / deep learning technique inspired by the workings of the human brain. Neural networks comprise multiple interconnected nodes or neurons that process and transmit information. They are widely used in various fields such as finance, healthcare, and image processing. One of the most critical components of an ANN is the backpropagation algorithm. Backpropagation algorithm is a supervised learning technique used to adjust the weights of a Neural Network to minimize the difference between the predicted output and the actual output. In this post, you will learn about the concepts of backpropagation algorithm used in training neural network models, along with Python …
SVM RBF Kernel Parameters: Python Examples
Support vector machines (SVM) are a popular and powerful machine learning technique for classification and regression tasks. SVM models are based on the concept of finding the optimal hyperplane that separates the data into different classes. One of the key features of SVMs is the ability to use different kernel functions to model non-linear relationships between the input variables and the output variable. One such kernel is the radial basis function (RBF) kernel, which is a popular choice for SVMs due to its flexibility and ability to capture complex relationships between the input and output variables. The RBF kernel has two important parameters: gamma and C (also called regularization parameter). …
I found it very helpful. However the differences are not too understandable for me