Explore best practices and enhance your prompts for better results — Prompt engineering has quickly become a part of our lives, just like a breeze, all thanks to ChatGPT and other large language models. Although it’s not a completely new field, it’s currently experiencing great popularity. …

A deep dive into Batch, Stochastic, and Mini-Batch Gradient Descent algorithms using Python — Gradient descent is a popular optimization algorithm that is used in machine learning and deep learning models such as linear regression, logistic regression, and neural networks. It uses first-order derivatives iteratively to minimize the cost function by updating model coefficients (for regression) and weights (for neural networks).

Create insights from frequent patterns using market basket analysis with Python — Association rule mining is a rule-based machine learning technique used to find frequent patterns in a data set. Frequent patterns may include frequent itemsets that are usually bought together or subsequences that are bought in sequence. For example, cookies and coffee can be frequent itemset for a cafe, and a…

A tutorial on topic modeling using Latent Dirichlet Allocation (LDA) and visualization with pyLDAvis — Topic modeling is a popular technique in Natural Language Processing (NLP) and text mining to extract topics of a given text. Utilizing topic modeling we can scan large volumes of unstructured text to detect keywords, topics, and themes. Topic modeling is an unsupervised machine learning technique and does not need…

A tutorial on how to detect outliers using standard deviation, interquartile range, isolation forest, DBSCAN, and local outlier factor — Outliers are data points that are far away from the majority of the observations in the dataset. Outliers can appear for many reasons such as natural deviations in population behavior, fraudulent activities, and human or system errors. …

A tutorial on mean, mode, time series, KNN, and MICE imputation — Missing data occurs when there is no data stored for a variable of interest in a dataset. Depending on its volume, missing data can harm the findings of any data analysis or the robustness of machine learning models. While dealing with missing data using Python, dropna() function from Pandas comes…

And a deep dive into how to combine them — Hyperparameter tuning also known as hyperparameter optimization is an important step in any machine learning model training that directly affects model performance. This article covers two very popular hyperparameter tuning techniques: grid search and random search and shows how to combine these two algorithms with coarse-to-fine tuning. …

Improve your model with voting, bagging, boosting and stacking — Ensemble learning is a technique used in machine learning to combine multiple models into a group model, in other words into an ensemble model. The ensemble model aims to perform better than each model alone or if not, to perform at least as well as the best individual model in…

What companies can learn from employee turnover data — Survival analysis is a popular statistical method to investigate the expected duration of time until an event of interest occurs. We can recall it from medicine as patients' survival time analysis, from engineering as reliability analysis or time-to-failure analysis, and from economics as duration analysis. Besides these disciplines, survival analysis…