Tutorials
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- PythonRequests in Python Tutorial – How to send HTTP requests in Python?Learn how to send HTTP requests in Python using the Requests library. Covers GET, POST, PUT, DELETE methods, headers, parameters, and response handling.
- PythonMatplotlib Pyplot – How to import matplotlib in Python and create different plotsA comprehensive guide to Matplotlib Pyplot, Python's core plotting interface. Learn to create line, scatter, bar, pie, histogram, and box plots with examples.
- PythonPython Boxplot – How to create and interpret boxplots (also find outliers and summarize distributions)Learn how to create and interpret boxplots in Python. Understand quartiles, detect outliers, and summarize distributions using Matplotlib and Seaborn.
- PythonBar Plot in Python – How to compare Groups visuallyLearn how to create bar plots in Python using Matplotlib and Seaborn. Covers simple, grouped, stacked, and horizontal bar charts with customization examples.
- Pythondata.table in R – The Complete Beginners Guidedata.table package is used for working with tabular data in R. It is super fast and has intuitive and terse syntax. This tutorial guide shows how to master data.table with practical examples and exercises.
- Statistics and MathKPSS Test for StationarityThe KPSS test checks whether a series is stationary around a deterministic trend. Like the ADF test it is widely used, but it differs in function and practical usage, so the two are not interchangeable. Learn the differences with simplified examples.
- Statistics and MathAugmented Dickey Fuller Test (ADF Test) – Must Read GuideAugmented Dickey Fuller test (ADF Test) is a common statistical test used to test whether a given Time series is stationary or not. It is one of the most commonly used statistical test when it comes to analyzing the stationary of a series
- Practice exercises101 R data.table ExercisesSharpen your R data.table skills with 101 practical exercises. Cover data manipulation, aggregation, joins, and reshaping with one of R's fastest packages.
- Statistics and MathWhat is P-Value? – Understanding the meaning, math and methodsThe p-value is a probability score used in statistical tests to establish the significance of an observed effect. Its meaning is often unclear even to experienced practitioners; this post explains the intuition behind it as clearly as possible.
- Practice exercises101 Python datatable Exercises (pydatatable)Python datatable is the newest package for data manipulation and analysis in Python. It carries the spirit of R's `data.table` with similar syntax. It is super fast, much faster than pandas and has the ability to work with out-of-memory data.
- Statistics and MathVector Autoregression (VAR) – Comprehensive Guide with Examples in PythonLearn Vector Autoregression (VAR) for multivariate time series forecasting. Covers the intuition, math, model training, diagnostics, and forecasting with Python statsmodels.
- Statistics and MathMahalanobis Distance – Understanding the math with examples (python)Mahalanobis distance is an effective multivariate distance metric that measures the distance between a point and a distribution. It is an extremely useful metric having, excellent applications in multivariate anomaly detection, classification on highly imbalanced datasets and one-class classification.
- Pythondatetime in Python – Simplified Guide with Clear Examplesdatetime is the standard module for working with dates in python. It provides 4 main objects for date and time operations: datetime, date, time and timedelta. In this post you will learn how to do all sorts of operations with these objects and solve date-time related practice problems (easy to hard) in Python.
- Machine LearningPrincipal Component Analysis – How PCA algorithms works, the concept, math and implementationUnderstand PCA, the math, concept, and Python implementation. Learn how Principal Component Analysis reduces dimensions while preserving maximum variance in your data.
- PythonPython Logging – Simplest Guide with Full Code and ExamplesThe logging module lets you track events while your code runs, so you can find what caused a crash from the logs. This post is a simple, clear guide to logging levels, handlers, formatters and tracebacks for small to large Python projects.
- PythonMatplotlib Histogram – How to Visualize Distributions in PythonMatplotlib histogram is used to visualize the frequency distribution of numeric array. In this article, we explore practical techniques like histogram facets, density plots, plotting multiple histograms in same plot.
- Statistics and MathTime Series Analysis in Python – A Comprehensive Guide with ExamplesTime series is a sequence of observations recorded at regular time intervals. This guide walks you through the process of analysing the characteristics of a given time series in python.
- PythonMatplotlib Tutorial – A Complete Guide to Python Plot with ExamplesThis tutorial explains matplotlib's way of making python plot, like scatterplots, bar charts and customize th components like figure, subplots, legend, title. Explained in simplified parts so you gain the knowledge and a clear understanding of how to add, modify and layout the various components in a plot.
- Generative AI and NLPTopic modeling visualization – How to present the results of LDA models?In this post, we follow a structured approach to build gensim's topic model and explore multiple strategies to visualize results using matplotlib plots.
- PythonTop 50 matplotlib Visualizations – The Master Plots (with full python code)A compilation of the Top 50 matplotlib plots most useful in data analysis and visualization. This list helps you to choose what visualization to show for what type of problem using python's matplotlib and seaborn library.
- PythonList Comprehensions in Python – My Simplified GuideList comprehensions is a pythonic way of expressing a 'For Loop' that appends to a list in a single line of code. It is an intuitive, easy-to-read and a very convenient way of creating lists.
- PythonPython @Property Explained – How to Use and When? (Full Examples)A python @property decorator lets a method to be accessed as an attribute instead of as a method with a '()'. Today, you will gain an understanding of why and in what situations you can use it and how to actually implement it.
- Machine LearningHow Naive Bayes Algorithm Works? (with example and full code)Understand how the Naive Bayes algorithm works with a step-by-step example. Covers Bayes Theorem, Laplace correction, Gaussian Naive Bayes, and full implementation code.
- PythonParallel Processing in Python – A Practical Guide with ExamplesParallel processing is when the task is executed simultaneously in multiple processors. In this tutorial, you'll understand the procedure to parallelize any typical logic using python's multiprocessing module.
- Generative AI and NLPCosine Similarity – Understanding the math and how it works (with python codes)Cosine similarity is a metric used to measure how similar the documents are irrespective of their size. It is the cosine of the angle between two vectors.
- Generative AI and NLPGensim Tutorial – A Complete Beginners GuideGensim is billed as a Natural Language Processing package that does 'Topic Modeling for Humans'. But it is practically much more than that. It is a leading and a state-of-the-art package for processing texts, working with word vector models (such as Word2Vec, FastText etc) and for building topic models.
- Generative AI and NLPLemmatization Approaches with Examples in PythonLemmatization is the process of converting a word to its base form. Python has nice implementations through the NLTK, TextBlob, Pattern, spaCy and Stanford CoreNLP packages. We will see how to optimally implement and compare the outputs from these packages.
- Machine LearningFeature Selection – Ten Effective Techniques with ExamplesDiscover 10 powerful feature selection techniques in R including Boruta, Lasso, stepwise selection, and variable importance to build better predictive models.
- Practice exercises101 Pandas Exercises for Data AnalysisMaster pandas with 101 hands-on exercises across 3 difficulty levels. Practice data manipulation, filtering, grouping, and more to sharpen your Python data analysis skills.
- Generative AI and NLPLDA in Python – How to grid search best topic models?Python's Scikit Learn provides a convenient interface for topic modeling using algorithms like Latent Dirichlet allocation(LDA), LSI and Non-Negative Matrix Factorization. In this tutorial, you will learn how to build the best possible LDA topic model and explore how to showcase the outputs as meaningful results.