Tag Archives: machine learning
NLP: Different Types of Language Models – Examples
Have you ever wondered how your smartphone seems to know exactly what you’re going to type next? Or how virtual assistants like Alexa and Siri understand and respond to your queries with such precision? The magic is NLP language models. In this blog, we will explore the diverse types of language models in NLP that have evolved over time, each with its unique capabilities and applications. From the simplicity of N-gram models, which predict text based on preceding words, to the sophisticated neural network-based models like RNNs, LSTMs, and the groundbreaking large language models using Transformers, we will learn about the intricacies of these models, examples of real-world applications and …
Bag of Words in NLP & Machine Learning: Examples
Last updated: 6th Jan, 2024 Most machine learning algorithms require numerical input for training the models. Bag of words (BoW) effectively converts text data into numerical feature vectors, making it compatible with a wide range of machine learning algorithms, from linear classifiers like logistic regression to complex ones like neural networks. In this post, you will learn about the concepts of bag-of-words model and how to train a text classification model using Python Sklearn. Some of the most common text classification problems includes sentiment analysis, spam filtering etc. In these problems, one can apply bag-of-words technique to train machine learning models for text classification. It will be good to understand the …
Cohen Kappa Score Explained: Formula, Example
Last updated: 5th Jan, 2024 Cohen’s Kappa Score is a statistic used to measure the performance of machine learning classification models. In this blog post, we will discuss what Cohen’s Kappa Score is and Python code example representing how to calculate Kappa score using Python. We will also provide a code example so that you can see how it works! What is Cohen’s Kappa Score or Coefficient? Cohen’s Kappa Score, also known as the Kappa Coefficient, is a statistical measure of inter-rater agreement for categorical data. Cohen’s Kappa Coefficient is named after statistician Jacob Cohen, who developed the metric in 1960. It is generally used in situations where there …
Validation Techniques for Machine Learning Models: Examples
Last updated: 4th Jan, 2024 In the realm of machine learning, the emphasis increasingly shifts towards solving real-world problems with high-quality models. Creating high performant models does not not just depend on raw computational power or theoretical knowledge, but crucially on the ability to systematically conduct and learn from a myriad of different models by trying with hypothesis and related experiments including different algorithms, datasets / features, hyperparameters, etc. This is where the importance of a robust validation strategy and related techniques becomes paramount. Validation techniques, in essence, are the methodologies employed to accurately assess a model’s errors and to gauge how its performance fluctuates with different experiments. The primary …
K-Fold Cross Validation in Machine Learning – Python Example
Last updated: 3rd Jan, 2024 In this post, you will learn about K-fold Cross-Validation concepts used while training machine learning models with the help of Python code examples. K-fold cross-validation is a data splitting technique that can be implemented with k > 1 folds. K-Fold Cross Validation is also known as k-cross, k-fold cross-validation, k-fold CV, and k-folds. The k-fold cross-validation technique can be implemented easily using Python with scikit learn (Sklearn) package which provides an easy way to implement training of k-fold cross-validation models. It is important to learn the concepts of k-fold cross-validation concepts in order to perform model tuning with the end goal to choose a model which has …
Machine Learning Definition, Examples, Method, Types
Last updated: 3rd Jan, 2024 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? Definition 1: Simply speaking, machine learning can be defined as an approach to model our beliefs about real-world events. For example, let’s say a person came to a doctor with a …
Machine Learning Models Solution Design: Examples
This blog is crafted for data scientists, machine learning (ML) and software engineers, business analysts / product managers, and anyone involved in the ML project lifecycle, aiming to create a reliable solution design and development strategy / plan for successful AI / machine learning project implementation and value realization. The blog revolves around a series of critical solution design questions, meticulously curated to guide teams from the initial conception of a project to its final deployment and beyond. By addressing each of these solution design questions, teams can ensure that they are not only building a model that is technically proficient but also one that aligns seamlessly with business objectives, …
Micro-average, Macro-average, Weighting: Precision, Recall, F1-Score
Last updated: 30th Dec, 2023 In this post, you will learn about how to use micro-averaging and macro-averaging methods for evaluating scoring metrics (precision, recall, f1-score) for multi-class classification machine learning problem. You will also learn about weighting method used as one of the other averaging choices of metrics such as precision, recall and f1-score for multi-class classification problem. The concepts will be explained with Python code examples. What & Why of Micro, Macro-averaging and Weighting metrics? Micro and macro-averaging methods are used in the evaluation of classification models, to compute performance metrics like precision, recall, and F1-score. These methods are especially relevant in scenarios involving multi-class or multi-label classification. In case of multi-class classification, …
ROC Curve & AUC Explained with Python Examples
Last updated: 29th Dec, 2023 Confusion among data scientists regarding ROC Curve and AUC often stems from misunderstanding their relationship. The ROC Curve visualizes true positive vs false positive rates at various thresholds, while AUC quantifies the overall ability of a model to discriminate between classes, with higher values indicating better performance. In this post, you will learn about ROC Curve and AUC concepts along with related concepts such as True positive and false positive rate with the help of Python examples. It is very important to learn ROC, AUC and related concepts as it helps in selecting the most appropriate machine learning classification models based on the model performance. What is …
Accuracy, Precision, Recall & F1-Score – Python Examples
Last updated: 29th Dec, 2023 Classification models are used in classification problems to predict the target class of the data sample. The classification machine learning models predicts the probability that each instance belongs to one class or another. It is important to evaluate the performance of the classifications model in order to reliably use these models in production for solving real-world problems. The performance metrics include accuracy, precision, recall, and F1-score. Because it helps us understand the strengths and limitations of these models when making predictions in new situations, model performance is essential for machine learning. The most common question asked is what is accuracy, precision, recall and f1 score? In …
Mean Squared Error or R-Squared – Which one to use?
Last updated: 29th Dec, 2023 As you embark on your journey to understand and evaluate the performance of regression models, it’s crucial to know when to use each of these metrics and what they reveal about your model’s accuracy. In this post, you will learn about the concepts of the mean-squared error (MSE) and R-squared (R2), the difference between them, and which one to use when evaluating the linear regression models. Note that MSE is very closely related to root mean squared error (RMSE) which is also discussed in this blog. You also learn Python examples to understand the concepts in a better manner. For learning the differences between other …
Data Science Competitions on Different Online Platforms
Data science / Machine Learning is an ever-evolving field, and competitions provide a great way for beginners / practitioners to hone their skills, solve real-world problems, enhance their resumes / CVs and even earn rewards. Here’s a roundup of some notable machine learning / data science / AI competition platforms, each offering unique opportunities. Each of these data science competition platforms offers unique opportunities and challenges, making them ideal for both beginners and expert data scientists at various stages of their careers to learn, compete, and contribute to a wide array of problems.
Large Language Models (LLMs) & Semantic Search: Examples
Have you ever marveled at how typing a few words into a search engine yields exactly the information you’re looking for from the vast expanse of the web? This is largely thanks to the advancements in semantic search, bolstered by technologies like Large Language Models (LLMs). Semantic search, which focuses on understanding the intent and contextual meaning behind queries, benefits from LLMs to provide more accurate and relevant results. However, it’s important to note that traditional search engines also rely on a sophisticated mix of algorithms, indexing, and ranking systems. LLMs complement these systems by enhancing their ability to interpret complex queries, making your search experience more intuitive and effective. …
Introducing Our New Data Science & AI Trends Page
We are thrilled to announce the launch of our dedicated Data Science and AI Trends page at VitalFlux.com! This new resource is designed to be a one-stop hub for data scientists, AI enthusiasts, and anyone passionate about staying at the forefront of technological innovation. What You’ll Find Our Data Science & AI Trends page is more than just a collection of articles; it’s a dynamic resource that aggregates the most insightful and current information from various high-impact sources. Here’s a sneak peek at what you can expect: Web Pages Stay informed with our selection of web pages from leading research institutions, tech news outlets, and individual thought leaders in the …
Python – Replace Missing Values with Mean, Median & Mode
Last updated: 18th Dec, 2023 Have you found yourself asking question such as how to deal with missing values in data analysis stage? When working with Python, have you been troubled with question such as how to replace missing values in Pandas data frame? Well, missing values are common in dealing with real-world problems when the data is aggregated over long time stretches from disparate sources, and reliable machine learning modeling demands for careful handling of missing data. One strategy is imputing the missing values, and a wide variety of algorithms exist spanning simple interpolation (mean, median, mode), matrix factorization methods like SVD, statistical models like Kalman filters, and deep …
Tool – Machine Learning Algorithm Cheat Sheet
Here is a comprehensive and user-friendly tool designed to bridge the gap between complex machine learning concepts and practical understanding. Whether you’re a student, educator, data scientist, or just a curious learner, this tool is your go-to resource for quick insights into some of the most popular and widely used machine learning algorithms. From Linear Regression to more advanced techniques like XGBoost and Principal Component Analysis, the plugin offers a succinct summary of each algorithm, including its definition, typical use cases, and applicable Python and R libraries. Select a Machine Learning Algorithm Select a machine learning algorithm from the drop-down to view and learn the details. Select a Feature Scaling …
I found it very helpful. However the differences are not too understandable for me