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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 haiku, glove-word-embeddings, text-classification
Haiku (JAX): Glove Embeddings for Text Classification Tasks
Artificial Intelligence

Haiku (JAX): Glove Embeddings for Text Classification Tasks

Tutorial guides on how to use GloVe word embeddings with Haiku (JAX) networks to solve text classification tasks. It explains various ways of handling embeddings to get better results.

Sunny Solanki  Sunny Solanki
Jul 17, 2022 ~10 mins
Tags object-detection, mxnet, pre-trained-models
MXNet (GluonCV): Object Detection using Pre-Trained Models
Artificial Intelligence

MXNet (GluonCV): Object Detection using Pre-Trained Models

The tutorial explains how to use pre-trained MXNet models for solving object detection tasks. MXNet has a helper library GluonCV which maintains pre-trained models for computer vision tasks. We have covered how to load a model and use it on random images downloaded from the internet to detect objects in them.

Sunny Solanki  Sunny Solanki
Jul 15, 2022 ~25 mins
Tags pytorch, object-detection, pre-trained-models
PyTorch: Object Detection using Pre-Trained Models / Networks
Artificial Intelligence

PyTorch: Object Detection using Pre-Trained Models / Networks

The tutorial guides on how to use pre-trained PyTorch models/networks for the object detection tasks. PyTorch provides pre-trained models through torchvision module. We have explained how you can load a model and run it on random images from the internet to detect objects in them.

Sunny Solanki  Sunny Solanki
Jul 13, 2022 ~25 mins
Tags haiku, jax, text-classification, word-embeddings
Haiku (JAX): Word Embeddings for Text Classification Tasks
Artificial Intelligence

Haiku (JAX): Word Embeddings for Text Classification Tasks

The tutorial guides to creating neural networks using Python library Haiku that uses word embeddings approach for solving text classification tasks. Haiku is a high-level deep learning library built on top of JAX.

Sunny Solanki  Sunny Solanki
Jul 11, 2022 ~35 mins
Tags mxnet, time-series, lstm, regression
MXNet: LSTM Networks for Time-Series Data (Regression Tasks)
Artificial Intelligence

MXNet: LSTM Networks for Time-Series Data (Regression Tasks)

The explains how to create RNNs (consisting of LSTM layers) to solve time series regression tasks using MXNet.The dataset used in the tutorial is a multivariate time-series dataset.

Sunny Solanki  Sunny Solanki
Jul 09, 2022 ~30 mins
Tags haiku, jax, text-classification
Haiku (JAX): Simple Guide to Text Classification
Artificial Intelligence

Haiku (JAX): Simple Guide to Text Classification

The tutorial covers how we can solve text classification tasks using Haiku neural networks. It uses a basic word frequency (bag of words) approach to encode text data before giving it to the neural network.

Sunny Solanki  Sunny Solanki
Jul 07, 2022 ~25 mins
Tags keras, lstm, time-series, regression
Keras: RNNs (LSTM) for Time Series Data (Regression Tasks)
Artificial Intelligence

Keras: RNNs (LSTM) for Time Series Data (Regression Tasks)

The tutorial explains how we can create RNNs consisting of LSTM layers for solving time-series regression tasks. The LSTM Networks (RNNs) are preferred for solving tasks involving time-series data as they can better capture order/sequence.

Sunny Solanki  Sunny Solanki
Jul 05, 2022 ~25 mins
Tags mxnet, text-generation, lstm, character-embeddings
Guide to Text Generation using MXNet RNNs (LSTM) (Character Embeddings)
Artificial Intelligence

Guide to Text Generation using MXNet RNNs (LSTM) (Character Embeddings)

The tutorial provides a guide on creating RNNs consisting of LSTM layers for solving text generation tasks. It uses a character-based approach to generate new text. The text data is encoded using the character embeddings approach.

Sunny Solanki  Sunny Solanki
Jul 03, 2022 ~30 mins
Tags mxnet, text-generation, character-based, lstm
MXNet: Text Generation using LSTM Networks (Character-based RNNs)
Artificial Intelligence

MXNet: Text Generation using LSTM Networks (Character-based RNNs)

The tutorial covers how we can create Recurrent Neural Networks (RNNs) consisting of LSTM Layers for text generation tasks. It uses a character-based approach (works on characters instead of words/n-grams) to generate new text. The text is encoded using the bag of words approach before giving it to LSTM layers for processing.

Sunny Solanki  Sunny Solanki
Jul 01, 2022 ~30 mins
Tags PyTorch, lstm, time-series, regression
PyTorch: LSTM Networks for Time-Series Data (Regression Tasks)
Artificial Intelligence

PyTorch: LSTM Networks for Time-Series Data (Regression Tasks)

The tutorial explains how to create Recurrent Neural Networks (RNNs) consisting of LSTM Layers to solve time-series regression tasks. LSTM networks are quite good at tasks involving time-series data.

Sunny Solanki  Sunny Solanki
Jun 09, 2022 ~30 mins

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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