CoderzColumn : Tutorials Home (Page: 8)

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 cufflinks, plotly, pandas
cufflinks [Python] - How to create plotly charts from pandas dataframe with one line of code?
Data Science

cufflinks [Python] - How to create plotly charts from pandas dataframe with one line of code?

A detailed guide on how to use Python library "cufflinks" to create interactive data visualizations/charts. Cufflinks is built on top of Plotly and let us create charts by calling 'iplot()' method on Pandas dataframe. The 'iplot()' method tries to mimic 'plot()' API (matplotlib) of pandas dataframe to generate charts but uses Plotly.

Sunny Solanki  Sunny Solanki
Aug 05, 2022 ~30 mins
Tags profiling, memory_profiler
memory_profiler: How to Profile Memory Usage in Python?
Python

memory_profiler: How to Profile Memory Usage in Python?

A detailed guide on how to use Python library "memory_profiler" to profile memory usage by Python code/script/program and processes. Tutorial covers various ways of profiling with "memory_profiler" like "@profile decorator", "mprof shell command", "memory_usage() function", etc. It even covers how to use "memory_profiler" in Jupyter notebook using "%mprun" and "%memit" magic commands.

Sunny Solanki  Sunny Solanki
Aug 05, 2022 ~20 mins
Tags sankey-diagram, holoviews, plotly
How to Create Sankey Diagrams (Alluvial) in Python (holoviews & plotly)?
Data Science

How to Create Sankey Diagrams (Alluvial) in Python (holoviews & plotly)?

A detailed guide to creating Sankey Diagram (Alluvial Diagram) using Python data visualization libraries Plotly and Holoviews (Bokeh & Matplotlib). The charts are interactive and visualized in Jupyter Notebooks.

Sunny Solanki  Sunny Solanki
Aug 01, 2022 ~30 mins
Tags missing-data, visualization
missingno - Visualize Missing Values (NaNs / Null Values) Distribution in Datasets [Python]
Data Science

missingno - Visualize Missing Values (NaNs / Null Values) Distribution in Datasets [Python]

Tutorial explains how to use Python module "missingno" to analyze the distribution of missing data (NaNs/NULLs/None Values) in our datasets. It let us create various charts to visualize the spread of missing data from various angles which can help us make better decisions.

Sunny Solanki  Sunny Solanki
Jul 29, 2022 ~20 mins
Tags python, multiprocessing, threadpool, processpool
concurrent.futures: High-Level Multithreading and Multiprocessing API in Python
Python

concurrent.futures: High-Level Multithreading and Multiprocessing API in Python

A comprehensive guide on how to use Python module "concurrent.futures" for multitasking (Multithreading & Multiprocessing). The "concurrent.futures" module provides a very high-level API that let us create a pool of workers (threads/processes) to which we can submit tasks for completion. It'll take care of handling resources and we don't need to do much low-level coding that we have to do if we use "threading" or "multiprocessing" modules.

Sunny Solanki  Sunny Solanki
Jul 29, 2022 ~25 mins
Tags ipywidgets, notebook
ipywidgets - An In-depth Guide to Interactive Widgets in Jupyter Notebook [Python]
Python

ipywidgets - An In-depth Guide to Interactive Widgets in Jupyter Notebook [Python]

A detailed guide on how to use Python library ipywidgets that let us create widgets (dropdown, radio buttons, checkboxes, buttons, etc) in Jupyter notebooks. These widgets let us add interactivity to our notebooks. It can be linked to various data viz libraries to make charts interactive like dashboards and web apps.

Sunny Solanki  Sunny Solanki
Jul 29, 2022 ~45 mins
Tags gluoncv, image-classification, pre-trained-networ…
MXNet (GluonCV): Image Classification using Pre-Trained Models
Artificial Intelligence

MXNet (GluonCV): Image Classification using Pre-Trained Models

A brief guide on how to use pre-trained MXNet networks (GluonCV) for image classification tasks. GluonCV is an image processing toolkit of MXNet and provides many famous pre-trained networks like ResNet, VGG, InceptionNet, MobileNet, DenseNet, AlexNet, etc.

Sunny Solanki  Sunny Solanki
Jul 28, 2022 ~30 mins
Tags keras, neural-networks, simple-guide
Keras: Guide to Create Simple Neural Networks in Python
Artificial Intelligence

Keras: Guide to Create Simple Neural Networks in Python

A simple guide on how to use Python library keras to create neural networks. Tutorial is a good starting point for someone who is new to keras. It provides a guide on creating simple neural networks trained on toy datasets that solve classification and regression tasks.

Sunny Solanki  Sunny Solanki
Jul 25, 2022 ~25 mins
Tags shap, interpret-ml-models
SHAP Values - Interpret Machine Learning Model Predictions using Game Theoretic Approach [Python]
Machine Learning

SHAP Values - Interpret Machine Learning Model Predictions using Game Theoretic Approach [Python]

A detailed guide to use Python library SHAP to generate Shapley values (shap values) that can be used to interpret/explain predictions made by our ML models. Tutorial creates various charts using shap values interpreting predictions made by classification and regression models trained on structured data.

Sunny Solanki  Sunny Solanki
Jul 23, 2022 ~60 mins
Tags pytorch, image-classification, pre-trained-models
PyTorch: Image Classification using Pre-Trained Models
Artificial Intelligence

PyTorch: Image Classification using Pre-Trained Models

A simple guide on how to use pre-trained image classification models available from "torchvision" library of PyTorch. Torchvision is a computer vision toolkit of PyTorch which provides many pre-trained image classification networks like ResNet, VGG, AlexNet, MobileNet, InceptionNet, LeNet, etc.

Sunny Solanki  Sunny Solanki
Jul 21, 2022 ~25 mins

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Most Popular Tutorials

1. Candlestick Chart in Python (mplfinance, plotly, bokeh, bqplot & cufflinks)
2. ipywidgets - An In-depth Guide to Interactive Widgets in Jupyter Notebook
3. Simple Guide to Style Display of Pandas DataFrames
4. How to Use LIME to Understand sklearn Models Predictions [Python]?
5. imaplib - Simple Guide to Manage Mailboxes using Python

Tutorial Categories

Artificial Intelligence (83)
Data Science (84)
Digital Marketing (8)
Machine Learning (38)
Python (130)

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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

joblib
ipyparallel

ipyparallel
dask.distributed

dask.distributed
dask.delayed

dask.delayed
dask.bag

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

threading
multiprocessing

multiprocessing
concurrent.futures

concurrent.futures
asyncio

asyncio
asyncio - Synchronization Primitives

asyncio - Synchronization Primitives
queue

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

scikit-plot
yellowbrick

yellowbrick
interpret-ml

interpret-ml
interpret-text

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

LIME
Shap Values

Shap Values
eli5

eli5
treeinterpreter

treeinterpreter
dice-ml

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

Chord Diagrams
Network Charts

Network Charts
Parallel Coordinates Charts

Parallel Coordinates Charts
Radar Charts

Radar Charts
Treemap 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

PyTorch
Keras

Keras
JAX

JAX
Flax

Flax
MXNet

MXNet
PyTorch Lightning

PyTorch Lightning
Haiku

Haiku
Sonnet

Sonnet
Scikeras

Scikeras
Skorch

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