Published in Heartbeat·PinnedTechniques to Handle Imbalanced Data in Machine LearningDifferent ways to handle imbalanced data for a classification problem — In the machine learning process, while dealing with classification problems, you’ve probably faced situations where the number of observations for one of the target class labels is significantly lower than the observations of other target classes. …Heartbeat6 min readHeartbeat6 min read
Published in Heartbeat·PinnedHyperparameter Tuning in Machine Learning Using GridSearchCVFinding the optimal hyperparameters using grid search — Model parameters and model hyperparameters are two important things that we define while building a machine learning model. For better accuracy of the model, data scientists apply multiple relevant algorithms to their data and then choose the best model on the basis of the best performance metric created by the…Heartbeat5 min readHeartbeat5 min read
Published in Heartbeat·Nov 22Guide to Cross-Validation Techniques and Its Types in Machine LearningExplanation of cross-validation and its types in machine learning — Cross-validation is a resampling technique in machine learning which ensures the performance and efficiency of the machine learning model by evaluating it over a subset of the dataset. It validates the model performance by training it on a subset of the input data and then testing it on the subset…Crossvalidation6 min readCrossvalidation6 min read
Published in Heartbeat·Oct 21Guide to Activation Functions in Neural NetworksHow to choose the best activation function for your deep learning model — Activation functions play an essential part in artificial neural networks as they decide whether a neuron should be activated or not. Activating a neuron determines whether the information that the neuron is receiving is relevant and if it should be used or ignored. Activation functions are mathematical equations that help…Heartbeat6 min readHeartbeat6 min read
Published in Heartbeat·Oct 6Guide to Loss Functions for Machine Learning ModelsLoss functions to use when training machine learning models — In machine learning, a loss function is used to measure the loss, or cost, of a specific machine learning model. These loss functions calculate the amount of error in a specific machine learning model using some mathematical formula and measure the performance of that specific model. There are various loss…Heartbeat6 min readHeartbeat6 min read
Published in Level Up Coding·Sep 30Member-onlyProgramming Jokes That Will Make You LaughJokes that you can relate to your daily life at work — We all like jokes, especially when we can relate them to our daily life and work. In this article, I’ve compiled some programming jokes that you can relate to your daily work life. …Programming3 min readProgramming3 min read
Published in Heartbeat·Sep 28Guide to Gradient Descent in Machine LearningAn overview of Stochastic Gradient Descent, Batch Gradient Descent and Mini-Batch Gradient Descent — Gradient Descent is an iterative optimization algorithm in machine learning that aims to minimize the cost function by finding the global minimum. In this way, it helps machine learning models make more accurate predictions. This algorithm updates the parameters of the machine learning models after each iteration. It keeps updating…Gradient Descent5 min readGradient Descent5 min read
Published in Level Up Coding·Aug 31Member-only7 Bad Habits That Every Python Programmer Should AvoidThings that you should avoid while writing code in python — Python is a very simple and versatile programming language in terms of its syntax and applications. It is also very popular amongst beginners due to its simple syntax and it is very easy to learn. In this article, we will go through some common mistakes and bad habits that developers…Python6 min readPython6 min read
Aug 22Member-onlyGuide to Sampling Techniques in Data ScienceSampling techniques in data science that you should know — Sampling is the process of selecting a group of individuals from a target population. It allows us to get information about the total population, based on the statistics from a subset of that target(total) population. With this sampling process, we can study and analyze the behaviour of the target population…Data Science5 min readData Science5 min read
Published in Heartbeat·Jul 26Guide to Distance Metrics in Machine Learning AlgorithmsDistance Metrics used in machine learning algorithms — Distance metrics play a very important role in machine learning. A number of machine learning algorithms, both supervised and unsupervised, use distance metrics to calculate the similarity between two data points. These distance metrics calculate mathematics-driven distance between various data points available in the dataset. …Heartbeat6 min readHeartbeat6 min read
