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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 smtp, send-mails
smtplib - Simple Guide to Sending EMails using Python
Python

smtplib - Simple Guide to Sending EMails using Python

A detailed guide on how to use Python library "smtplib" to send emails (Gmail, Yahoo, etc) with simple examples. Tutorial covers various operations with mailbox servers like login / logout, verifying email ids, sending emails with CC / BCC, sending mails with attachments, etc. It uses SMTP protocol behind the scene to send emails.

Sunny Solanki  Sunny Solanki
Tags system-signals
signal - Simple Guide to Send, Receive and Handle System Signals in Python
Python

signal - Simple Guide to Send, Receive and Handle System Signals in Python

A comprehensive guide on how to use Python module "signal" to send, receive and handle system (Unix/Windows) signals to signal some event. The library lets us catch signal and run a handler (callback) based on event represented by signal. The signal can be sent to different threads and processes to inform them about event and execute handler by them accordingly.

Sunny Solanki  Sunny Solanki
Tags difflib, sequence-comparison
difflib - Simple Way to Find Out Differences Between Sequences / File Contents using Python
Python

difflib - Simple Way to Find Out Differences Between Sequences / File Contents using Python

A simple guide on how to use Python module “difflib” to compare sequences and find out differences between them. Tutorial explains whole API of a module to explain different ways of comparing sequences and format results in different ways. It can be very useful to compare file contents to see differences.

Sunny Solanki  Sunny Solanki
Tags time-series, trend, seasonality, pandas
How to Remove Trend & Seasonality from Time-Series Data in Python?
Data Science

How to Remove Trend & Seasonality from Time-Series Data in Python?

Tutorial provides a brief guide to detect stationarity (absence of trend and seasonality) in time series data. After checking for stationarity, the tutorial explains various ways to remove trends and seasonality from time series to make them stationary.

Sunny Solanki  Sunny Solanki
Tags configuration-files-parser
configparser - Simple Guide to Create and Parse Configuration Files in Python
Python

configparser - Simple Guide to Create and Parse Configuration Files in Python

A simple guide on how to use Python library "configparser" to create, read, parse, and modify config files (Generally ending with .conf, .ini, etc) in Python. Apart from basics, tutorial covers how to add/remove sections, create configuration from Python dictionary or strings and specify section details as formatted Python strings. All topics are covered with simple and easy-to-understand examples.

Sunny Solanki  Sunny Solanki
Tags events, scheduler, sched
sched - How to Schedule Events in Python?
Python

sched - How to Schedule Events in Python?

A simple guide on how to use Python module "sched" to schedule events. It let us run a function/callable in future. We can run a task at a specified time in future or after a specified time interval has passed. Apart from scheduling tasks, we have covered how to cancel tasks, assign priorities, create a scheduler with different time units, etc.

Sunny Solanki  Sunny Solanki
Tags metrics, visualizations
Scikit-Plot: Visualize ML Model Performance Evaluation Metrics
Machine Learning

Scikit-Plot: Visualize ML Model Performance Evaluation Metrics

Tutorial explains how to use Python library scikit-plot to create data visualizations of various ML metrics. It is designed on top of matplotlib and provides charts for most commonly used ML metrics like confusion matrix, ROC AUC curve, Precision-Recall Curve, Elbow Method, Silhouette Analysis, Feature Importance, PCA Projection, etc.

Sunny Solanki  Sunny Solanki
Tags xgboost
XGBoost - An In-Depth Guide [Python API]
Machine Learning

XGBoost - An In-Depth Guide [Python API]

An in-depth guide on how to use Python ML library XGBoost which provides an implementation of gradient boosting on decision trees algorithm. Tutorial covers majority of features of library with simple and easy-to-understand examples. Apart from training models & making predictions, topics like cross-validation, saving & loading models, early stopping training to prevent overfitting, creating custom loss function & evaluation metrics, etc are covered in detail.

Sunny Solanki  Sunny Solanki
Tags catboost
CatBoost - An In-Depth Guide [Python API]
Machine Learning

CatBoost - An In-Depth Guide [Python API]

An in-depth guide on how to use Python ML library catboost which provides an implementation of gradient boosting on decision trees algorithm. Tutorial covers majority of features of library with simple and easy-to-understand examples. Apart from training models & making predictions, topics like hyperparameters tuning, cross-validation, saving & loading models, plotting training loss/metric values, early stopping training to prevent overfitting, creating custom loss function & evaluation metrics, etc are covered in detail.

Sunny Solanki  Sunny Solanki
Tags lightgbm, boosting-decision-trees
LightGBM - An In-Depth Guide [Python API]
Machine Learning

LightGBM - An In-Depth Guide [Python API]

An in-depth guide on how to use Python ML library LightGBM which provides an implementation of gradient boosting on decision trees algorithm. Tutorial covers majority of features of library with simple and easy-to-understand examples. Apart from training models & making predictions, topics like cross-validation, saving & loading models, plotting features importances, early stopping training to prevent overfitting, creating custom loss function & evaluation metrics, etc are covered in detail.

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.