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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

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

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

Aug 27, 2022 ~25 mins

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

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

Aug 27, 2022 ~30 mins

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

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

Aug 26, 2022 ~30 mins

sched - How to Schedule Events in Python?

sched - How to Schedule Events in Python?

Aug 25, 2022 ~15 mins

Scikit-Plot: Visualize ML Model Performance Evaluation Metrics

Scikit-Plot: Visualize ML Model Performance Evaluation Metrics

Aug 24, 2022 ~30 mins

XGBoost - An In-Depth Guide [Python API]

XGBoost - An In-Depth Guide [Python API]

Aug 21, 2022 ~60 mins

CatBoost - An In-Depth Guide [Python API]

CatBoost - An In-Depth Guide [Python API]

Aug 20, 2022 ~60 mins

LightGBM - An In-Depth Guide [Python API]

LightGBM - An In-Depth Guide [Python API]

Aug 19, 2022 ~30 mins

Scikit-Learn - Incremental Learning for Large Datasets

Scikit-Learn - Incremental Learning for Large Datasets

Aug 18, 2022 ~30 mins

joblib - Parallel Processing in Python

joblib - Parallel Processing in Python

Aug 17, 2022 ~30 mins

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Machine Learning (38)
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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.

joblib

ipyparallel

dask.distributed

dask.distributed

dask.delayed

dask.bag

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.

threading

multiprocessing

multiprocessing

concurrent.futures

concurrent.futures

asyncio

asyncio - Synchronization Primitives

asyncio - Synchronization Primitives

queue

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.

Scikit-Learn: ML Model Evaluation Metrics

Scikit-Learn: ML Model Evaluation Metrics

scikit-plot

yellowbrick

interpret-ml

interpret-text

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.

LIME

Shap Values

eli5

treeinterpreter

treeinterpreter

dice-ml

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.

matplotlib (Static Charts)

matplotlib (Static Charts)

bokeh (Interactive Charts)

bokeh (Interactive Charts)

bqplot (Interactive Charts)

bqplot (Interactive Charts)

plotnine (Static Charts)

plotnine (Static Charts)

altair (Interactive Charts)

altair (Interactive Charts)

seaborn (Static Charts)

seaborn (Static Charts)

holoviews (Static & Interactive Charts)

holoviews (Static & Interactive Charts)

cufflinks (Interactive Charts)

cufflinks (Interactive Charts)

hvplot (Interactive Charts)

hvplot (Interactive Charts)

Pandas-Bokeh (Interactive Charts)

Pandas-Bokeh (Interactive Charts)

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.

Candlestick Charts

Candlestick Charts

Sankey Diagrams

Sankey Diagrams

Sunburst Charts

Sunburst Charts

Chord Diagrams

Network Charts

Parallel Coordinates Charts

Parallel Coordinates Charts

Radar Charts

Treemap Charts

Connection Maps

Connection Maps

Choropleth Maps

Choropleth Maps

Scatter & Bubble Maps

Scatter & Bubble Maps

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.

PyTorch

Keras

JAX

Flax

MXNet

PyTorch Lightning

PyTorch Lightning

Haiku

Sonnet

Scikeras

Skorch

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.

PyTorch (Torchvision): Image Classification using Pre-trained Networks

PyTorch (Torchvision): Image Classification using Pre-trained Networks

MXNet (GluonCV): Image Classification using Pre-trained Networks

MXNet (GluonCV): Image Classification using Pre-trained Networks

Image Classification using PyTorch CNNs

Image Classification using PyTorch CNNs

Image Classification using Flax (JAX) CNNs

Image Classification using Flax (JAX) CNNs

Image Classification using MXNet CNNs

Image Classification using MXNet CNNs

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