Tag Archives: machine learning
Different types of Time-series Forecasting Models
Forecasting is the process of predicting future events based on past and present data. Time-series forecasting is a type of forecasting that predicts future events based on time-stamped data points. Time-series forecasting models are an essential tool for any organization or individual who wants to make informed decisions based on future events or trends. From stock market predictions to weather forecasting, time-series models help us to understand and forecast changes over time. However, with so many different types of models available, it can be challenging to determine which one is best suited for a particular scenario. There are many different types of time-series forecasting models, each with its own strengths …
Transposed Convolution vs Convolution Layer: Examples
In the field of computer vision and deep learning, convolutional neural networks (CNNs) are widely used for image recognition tasks. A fundamental building block of CNNs is the convolutional layer, which extracts features from the input image by convolving it with a set of learnable filters. However, another type of layer called transposed convolution, also known as deconvolution, has gained popularity in recent years. In this blog post, we will compare and contrast these two types of layers, provide examples of their usage, and discuss their strengths and weaknesses. What are Convolutional Layer? What’s their purpose? A convolutional layer is a fundamental building block of a convolutional neural network (CNN). …
Support Vector Machine (SVM) Python Example
Support Vector Machines (SVMs) are a powerful and versatile machine learning algorithm that has gained widespread popularity among data scientists in recent years. SVMs are widely used for classification, regression, and outlier detection (one-class SVM), and have proven to be highly effective in solving complex problems in various fields, including computer vision (image classification, object detection, etc.), natural language processing (sentiment analysis, text classification, etc.), and bioinformatics (gene expression analysis, protein classification, disease diagnosis, etc.). In this post, you will learn about the concepts of Support Vector Machine (SVM) with the help of Python code example for building a machine learning classification model. We will work with Python Sklearn package for building the …
CNN Basic Architecture for Classification & Segmentation
As data scientists, we are constantly exploring new techniques and algorithms to improve the accuracy and efficiency of our models. When it comes to image-related problems, convolutional neural networks (CNNs) are an essential tool in our arsenal. CNNs have proven to be highly effective for tasks such as image classification and segmentation, and have even been used in cutting-edge applications such as self-driving cars and medical imaging. Convolutional neural networks (CNNs) are deep neural networks that have the capability to classify and segment images. CNNs can be trained using supervised or unsupervised machine learning methods, depending on what you want them to do. CNN architectures for classification and segmentation include …
Python – Replace Missing Values with Mean, Median & Mode
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 learning methods. Missing value imputation or replacing techniques help machine learning models learn from incomplete data. There are three main missing value imputation techniques – mean, median and mode. Mean is the average of all values in a set, median is the middle number in …
Feature Selection vs Feature Extraction: Machine Learning
Machine learning has become an increasingly important tool for businesses and researchers alike in recent years. From identifying patterns in data to making predictions about future outcomes, machine learning algorithms are now being used in a wide variety of fields. However, the success of these algorithms often depends on the quality of the features used to train them. This is where the concepts of feature selection and feature extraction come in. In this blog post, we’ll explore the difference between feature selection and feature extraction, two key techniques used in machine learning to optimize feature sets for better model performance. Both feature selection and feature extraction are used for dimensionality …
Keras: Multilayer Perceptron (MLP) Example
Artificial Neural Networks (ANN) have emerged as a powerful tool in machine learning, and Multilayer Perceptron (MLP) is a popular type of ANN that is widely used in various domains such as image recognition, natural language processing, and predictive analytics. Keras is a high-level API that makes it easy to build and train neural networks, including MLPs. In this blog, we will dive into the world of MLPs and explore how to build and train an MLP model using Keras. We will build a simple MLP model using Keras and train it on a dataset. We will explain different aspects of training MLP model using Keras. By the end of …
Neural Network & Multi-layer Perceptron Examples
Neural networks are an important part of machine learning, so it is essential to understand how they work. A neural network is a computer system that has been modeled based on a biological neural network comprising neurons connected with each other. It can be built to solve machine learning tasks, like classification and regression problems. The perceptron algorithm is a representation of how neural networks work. The artificial neurons were first proposed by Frank Rosenblatt in 1957 as models for the human brain’s perception mechanism. This post will explain the basics of neural networks with a perceptron example. You will understand how a neural network is built using perceptrons. This …
K-Fold Cross Validation – Python Example
In this post, you will learn about K-fold Cross-Validation concepts with 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 calculate k-fold cross-validation models. It is important to learn the concepts of cross-validation concepts in order to perform model tuning with the end goal to choose a model which has a high generalization performance. As a data scientist / machine learning Engineer, you must have a good …
Maximum Likelihood Estimation: Concepts, Examples
As data science continues to grow in importance and relevance, so too does the need for tools and techniques that can help extract insights from large, complex datasets. One such tool that is becoming increasingly popular among data scientists is Maximum Likelihood Estimation (MLE). This is becoming more so important to learn fundamentals of MLE concepts as it is at the core of generative modeling (generative AI). MLE is a statistical method used to estimate the parameters of a probability distribution, based on a set of observed data points. MLE is particularly important for data scientists because it underpins many of the probabilistic machine learning models that are used today. …
Generative vs Discriminative Models: Examples
The field of machine learning is rapidly evolving, and with it, the concepts and techniques that are used to develop models that can learn from data. Among these concepts, generative and discriminative models are two widely used approaches in the field. Generative models learn the joint probability distribution of the input features and output labels, whereas discriminative models learn the conditional probability distribution of the output labels given the input features. While both models have their strengths and weaknesses, understanding the differences between them is crucial to developing effective machine learning systems. Real-world problems such as speech recognition, natural language processing, and computer vision, require complex solutions that are able …
Accuracy, Precision, Recall & F1-Score – Python Examples
Classification models are used in classification problems to predict the target class of the data sample. The classification model 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. Performance measures in machine learning classification models are used to assess how well machine learning classification models perform in a given context. These 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. …
Neural Network Types & Real-life Examples
Neural networks are a powerful tool for data scientists, machine learning engineers, and statisticians. They have revolutionized the field of machine learning and have become an integral part of many real-world applications such as image and speech recognition, natural language processing, and autonomous vehicles. ChatGPT is a classic example how AI / neural network applications has taken world by storm. But what exactly are they and what are their different types? There are various types of neural networks, each with their own unique architecture and learning algorithm. Understanding the different types of neural networks and their real-life examples is crucial for anyone interested in machine learning and artificial intelligence. In …
Sequence to Sequence Models: Types, Examples
Sequence to sequence (Seq2Seq) modeling is a powerful machine learning technique that has revolutionized the way we do natural language processing (NLP). It allows us to process input sequences of varying lengths and produce output sequences of varying lengths, making it particularly useful for tasks such as language translation, speech recognition, and chatbot development. Sequence to sequence modeling also provides a great foundation for creating text summarizers, question answering systems, sentiment analysis systems, and more. With its wide range of applications, learning about sequence to sequence modeling concepts is essential for anyone who wants to work in the field of natural language processing. This blog post will discuss types of …
Machine Learning Bias Explained with Examples
In the artificial intelligence (AI) / machine learning (ML) powered world where predictive models have started getting used more often in decision-making areas, the primary concerns of policy makers, auditors and end users have been to make sure that these systems using the models are not making biased/unfair decisions based on model predictions (intentional or unintentional discrimination). Imagine industries such as banking, insurance, and employment where models are used as solutions to decision-making problems such as shortlisting candidates for interviews, approving loans/credits, deciding insurance premiums etc. How harmful it could be to the end users as these decisions may impact their livelihood based on biased predictions made by the model, thereby, …
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 …
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