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CoderzColumn Tutorials


Learning is a lifelong process. But you must know what, where, and how to learn? What skills to develop? What skills will help you boost your career? If not, you are at the right place! Our tutorial section at CoderzColumn is dedicated to providing you with all the practical lessons. It will give you the experience to learn Python for different purposes and code on your own. Our tutorials cover:

  • Python Programming - threading, multiprocessing, concurrent.futures, asyncio, queue, imaplib, smtplib, email, mimetypes, cprofile, profile, tracemalloc, logging, ipywidgets, beautifulsoup, filecmp, glob, shutil, tarfile, zipfile, argparse, datetime, traceback, abc, contextlib, warnings, dataclasses, re, difflib, textwrap, collections, heapq, bisect, weakref, configparser, signa, ipaddress, xarray, pandas, subprocess, sched, etc.
  • Artificial Intelligence - PyTorch, Keras, Tensorflow, JAX, MXNet, Torchvision, Torchtext, GluonCV, GluonNLP, etc
  • Machine Learning - Scikit-Learn, Statsmodels, XGBoost, CatBoost, LightGBM, optuna, scikit-optimize, hyperopt, bayes_opt, scikit-plot, lime, shap, eli5, etc.
  • Data Science - missingno, seaborn, pandas, sweetviz, numpy, networkx, xarray, awkward-array, etc.
  • Data Visualization - Matplotlib, Bokeh, Bqplot, Plotnine, Altair, Plotly, Cufflinks, Holoviews, dash, streamlit, panel, voila, bokeh, geopandas, geoviews, folium, ipyleaflet, geoplot, cartopy, etc.
  • Digital Marketing - SEO tactics, marketing strategies, Social Media marketing, and more.

For an in-depth understanding of the above concepts, check out the sections below.

Recent Tutorials


Tags Bokeh, widgets, Apps, GUI
Simple Guide to use Bokeh Widgets (Interactive GUI / Apps)
Data Science

Simple Guide to use Bokeh Widgets (Interactive GUI / Apps)

A simple guide on how to create interactive GUI / apps with widgets using Python Data viz library Bokeh. Tutorial explains how we can use widgets (dropdowns, radio buttons, checkboxes, date pickers, sliders, etc) available from bokeh with simple examples. Bokeh apps explained in tutorial use Python callbacks for updating charts.

Sunny Solanki  Sunny Solanki
Tags plotnine-charts, annotations
Add Annotations to Plotnine Charts
Data Science

Add Annotations to Plotnine Charts

A simple guide on how to add annotations to plotnine charts with simple examples. Plotnine is a Python data viz library that let us create static charts. Tutorial explains annotations like text labels, arrows, boxes, polygons, spans, slopes, etc.

Sunny Solanki  Sunny Solanki
Tags bayesian-optimization, hyperparameters-tuning
bayes_opt: Bayesian Optimization for Hyperparameters Tuning
Machine Learning

bayes_opt: Bayesian Optimization for Hyperparameters Tuning

A comprehensive guide on how to use Python library "bayes_opt (bayesian-optimization)" to perform hyperparameters tuning of ML models. Tutorial explains the usage of library by performing hyperparameters tuning of scikit-learn regression and classification models. Tutorial also covers other functionalities of library like changing parameter range during tuning process, manually looping for tuning, guided tuning, saving and resuming tuning process, etc.

Sunny Solanki  Sunny Solanki
Tags scikit-optimize, hyperparameters-optimization
Scikit-Optimize: Simple Guide to Hyperparameters Tuning / Optimization
Machine Learning

Scikit-Optimize: Simple Guide to Hyperparameters Tuning / Optimization

A complete guide on how to use Python library "scikit-optimize" to perform hyperparameters tuning of ML Models. Tutorial explains library usage by performing hyperparameters tuning of scikit-learn regression and classification models. Tutorial even covers plotting functionality provided by scikit-optimize to analyze hyperparameters tuning process.

Sunny Solanki  Sunny Solanki
Tags Pandas-Bokeh, interactive-charts
Pandas-Bokeh: Create Bokeh Charts from Pandas DataFrame with One Line of Code
Data Science

Pandas-Bokeh: Create Bokeh Charts from Pandas DataFrame with One Line of Code

A detailed guide to Python data visualization library Pandas_bokeh that let us create interactive bokeh charts from pandas dataframe with just one simple function call. Tutorial covers various charts (scatter, bar, step, line, pie, histogram, scatter maps, bubble maps, etc) available from library with simple examples.

Sunny Solanki  Sunny Solanki
Tags bokeh-charts, annotations
How to Add Annotations to Bokeh Charts?
Data Science

How to Add Annotations to Bokeh Charts?

A simple guide to add annotations to charts created using Python data viz library bokeh. Tutorial explains different annotations like arrows, labels, polygons, spans, ranges, bound, boxes, etc. We have also covered styling of these annotations with simple examples.

Sunny Solanki  Sunny Solanki
Tags GluonCV, Image-Segmentation, Pre-trained-Models
GluonCV: Image Segmentation using Pre-Trained MXNet Models
Artificial Intelligence

GluonCV: Image Segmentation using Pre-Trained MXNet Models

A brief guide on how to pre-trained MXNet models available from GluonCV library to perform image segmentation task. Tutorial loads various pre-trained MXNet models and makes predictions on images downloaded from internet to detect segments/objects present in them.

Sunny Solanki  Sunny Solanki
Tags tape-archives, tar-files
tarfile - Simple Guide to Work with Tape Archives in Python
Python

tarfile - Simple Guide to Work with Tape Archives in Python

A simple guide to use Python library tarfile to work with tape archives (tarfile). Tutorial explains how to read existing tar files, create tar files, add files to tar archives, extract contents of tar archives, compress contents using different algorithms, etc.

Sunny Solanki  Sunny Solanki
Tags bokeh, ipywidgets, widgets
How to Link Bokeh Charts with IPywidgets widgets to Dynamically Update Charts?
Data Science

How to Link Bokeh Charts with IPywidgets widgets to Dynamically Update Charts?

A simple guide to create interactive GUI using Python data viz library Bokeh and widgets library ipywidgets. Tutorial explains how we can link ipywidgets widgets (like dropdowns, checkboxes, sliders, etc) with Bokeh charts to dynamically update them with changes in widget states.

Sunny Solanki  Sunny Solanki
Tags Image-Segmentation, PyTorch, Pre-trained-models
PyTorch: Image Segmentation using Pre-Trained Models (torchvision)
Artificial Intelligence

PyTorch: Image Segmentation using Pre-Trained Models (torchvision)

A detailed guide on how to use pre-trained PyTorch models available from Torchvision module for image segmentation tasks. Tutorial explains how to use pre-trained models for instance segmentation as well as semantic segmentation.

Sunny Solanki  Sunny Solanki
Parallel Computing using Python

Parallel Computing using Python


Parallel Computing is a type of computation where tasks are assigned to individual processes for completion. These processes can be running on a single computer or cluster of computers. Parallel Computing makes multi-tasking super fast.

Python provides different libraries (joblib, dask, ipyparallel, etc) for performing parallel computing.

Concurrent Programming in Python

Concurrent Programming in Python


Concurrent computing is a type of computing where multiple tasks are executed concurrently. Concurrent programming is a type of programming where we divide a big task into small tasks and execute these tasks in parallel. These tasks can be executed in parallel using threads or processes.

Python provides various libraries (threading, multiprocessing, concurrent.futures, asyncio, etc) to create concurrent code.

Visualize Machine Learning Metrics

Visualize Machine Learning Metrics


Once our Machine Learning model is trained, we need some way to evaluate its performance. We need to know whether our model has generalized or not.

For this, various metrics (confusion matrix, ROC AUC curve, precision-recall curve, silhouette Analysis, elbow method, etc) are designed over time. These metrics help us understand the performance of our models trained on various tasks like classification, regression, clustering, etc.

Python has various libraries (scikit-learn, scikit-plot, yellowbrick, interpret-ml, interpret-text, etc) to calculate and visualize these metrics.

Interpret Predictions Of ML Models

Interpret Predictions Of ML Models


After training ML Model, we generally evaluate the performance of model by calculating and visualizing various ML Metrics (confusion matrix, ROC AUC curve, precision-recall curve, silhouette Analysis, elbow method, etc).

These metrics are normally a good starting point. But in many situations, they don’t give a 100% picture of model performance. E.g., A simple cat vs dog image classifier can be using background pixels to classify images instead of actual object (cat or dog) pixels.

In these situations, our ML metrics will give good results. But we should always be a little skeptical of model performance.

We can dive further deep and try to understand how our model is performing on an individual example by interpreting results. Various algorithms have been developed over time to interpret predictions of ML models and many Python libraries (lime, eli5, treeinterpreter, shap, etc) provide their implementation.

Python Data Visualization Libraries

Python Data Visualization Libraries


Data Visualization is a field of graphical representation of information / data. It is one of the most efficient ways of communicating information with users as humans are quite good at capturing patterns in data.

Python has a bunch of libraries that can help us create data visualizations. Some of these libraries (matplotlib, seaborn, plotnine, etc) generate static charts whereas others (bokeh, plotly, bqplot, altair, holoviews, cufflinks, hvplot, etc) generate interactive charts. Majority of basic visualizations like bar charts, line charts, scatter plots, histograms, box plots, pie charts, etc are supported by all libraries. Many libraries also support advanced visualization, widgets, and dashboards.

Advanced Data Visualizations using Python

Advanced Data Visualizations using Python


Basic Data Visualizations like bar charts, line charts, scatter plots, histograms, box plots, pie charts, etc are quite good at representing information and exploring relationships between data variables.

But sometimes these visualizations are not enough and we need to analyze data from different perspectives. For this purpose, many advanced visualizations are developed over time like Sankey diagrams, candlestick charts, network charts, chord diagrams, sunburst charts, radar charts, parallel coordinates charts, etc. Python has many data visualization libraries that let us create such advanced data visualizations.

Python Deep Learning Libraries

Python Deep Learning Libraries


Deep learning is a field in Machine Learning that uses deep neural networks to solve tasks. The neural networks with generally more than one hidden layer are referred to as deep neural networks.

Many real-world tasks like object detection, image classification, image segmentation, etc can not be solved with simple machine learning models (decision trees, random forest, logistic regression, etc). Research has shown that neural networks with many layers are quite good at solving these kinds of tasks involving unstructured data (Image, text, audio, video, etc). Deep neural networks nowadays can have different kinds of layers like convolution, recurrent, etc apart from dense layers.

Python has many famous deep learning libraries (PyTorch, Keras, JAX, Flax, MXNet, Tensorflow, Sonnet, Haiku, PyTorch Lightning, Scikeras, Skorch, etc) that let us create deep neural networks to solve complicated tasks.

Image Classification

Image Classification


Image classification is a sub-field under computer vision and image processing that identifies an object present in an image and assigns a label to an image based on it. Image classification generally works on an image with a single object present in it.

Over the years, many deep neural networks (VGG, ResNet, AlexNet, MobileNet, etc) were developed that solved image classification task with quite a high accuracy. Due to the high accuracy of these algorithms, many Python deep learning libraries started providing these neural networks. We can simply load these networks with weights and make predictions using them.

Python libraries PyTorch and MXNet have helper modules named 'torchvision' and 'gluoncv’ respectively that provide an implementation of image classification networks.