Nov 11Elastic Net Regression : The Best of L1 and L2 Norm PenaltiesElastic net represents a merging of the well-known regularized versions of linear regression, namely ridge and lasso. While ridge employs an L2 penalty and lasso utilizes an L1 penalty, elastic net incorporates both penalties. The advantage of elastic net is that you aren’t constrained to choosing between these two models…Machine Learning4 min readMachine Learning4 min read
Oct 12Choosing the Right Regression Approach: Parametric vs. Non-ParametricIntroduction: When it comes to regression analysis, choosing the right approach is crucial for accurate predictions and meaningful insights. Two common methods used are parametric, like linear regression, and non-parametric, like K-nearest neighbors (KNN) regression. …Machine Learning4 min readMachine Learning4 min read
Oct 9Tackling Overfitting with L1 and L2 Norm RegularisationIntroduction In the realm of Machine Learning, overfitting looms as a formidable adversary, posing a challenge to the creation of robust and accurate models. Overfitting occurs when a model becomes so engrossed in fitting the training data that it loses sight of its ability to generalize to new, unseen data. Essentially…Machine Learning3 min readMachine Learning3 min read
Oct 8The Art and Science of Feature Engineering in Data Science : Beginners GuideFeature engineering is a pivotal process in data science, involving the creation of new variables from raw data to enhance the performance and interpretability of models. Features are the characteristics of data that drive predictions, classifications, clustering, and more. …Machine Learning2 min readMachine Learning2 min read
Sep 19The Role of Dopamine and Prediction Errors: Insights from Neuroscience, for Reinforcement LearningIn the world of decision-making, an intriguing challenge confronts us — the quest to learn the most favorable choices through a process of trial and error, with the aim of maximizing rewards and avoiding penalties. This challenge, intriguingly, aligns closely with a fundamental concept in computer science: reinforcement learning. …Reinforcement Learning4 min readReinforcement Learning4 min read
Aug 3Journey Through the World of Machine Learning AlgorithmsA beginners guide to learning machine learning algorithms — A major chunk of Machine Learning is Algorithms. You might have mastered cleaning and tidying up the datasets and filtering out the features. What’s next ? Well, moving forward with the modelling, you will need to make a choice between different “possible” algorithms your dataset can be modelled on. …Machine Learning3 min readMachine Learning3 min read
Jun 13Training a Deep Convoluted Generative Adversarial Network — PyTorch ApproachLet’s walk-through the step-by-step approach to training Deep Convoluted Generative AI models to generate authentic and real-looking images from a custom dataset. Generative Adversarial Networks —an example The distinguishing factor of GANs is their ability to generate authentic, real-looking images, similar to the data distribution you might use. The concept…Generative Adversarial16 min readGenerative Adversarial16 min read
Apr 9Grocery Store Analogy to Better Understand Hyperparameters and ParametersEssential machine learning concept, simplified ! A machine learning model’s parameters and hyperparameters are essential components. However, given the complexity of machine learning, it can be difficult to comprehend them. Let’s use an accessible, everyday analogy that everyone can understand to debunk these ideas: navigating through a grocery store. Hyperparameters Consider…Machine Learning2 min readMachine Learning2 min read
Jan 21Migraine Classification : Using Synthetic Minority Oversampling Technique (SMOTE) Migraine ModelMigraine is not just a bad headache. It’s a disabling neurological disorder with different symptoms and different treatment approaches compared to other headache disorders. …Machine Learning4 min readMachine Learning4 min read
Jan 20Deep Learning to Predict Customer Delivery Times (CDT)- a Practical ApproachE-commerce has greatly impacted the way we shop for products and services. This can be attributed to various factors like — convenient home delivery of the order in a desired time window (e.g 10:00–10:30 AM), ability to browse and search through a ginormous catalog of products with a few clicks…Machine Learning9 min readMachine Learning9 min read
