CoderzColumn : Tutorials Home (Page: 5)

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 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
Tags candlestick, mplfinance, plotly, bokeh, bqplot, c…
Candlestick Chart in Python (mplfinance, plotly, bokeh, bqplot & cufflinks)
Data Science

Candlestick Chart in Python (mplfinance, plotly, bokeh, bqplot & cufflinks)

A simple guide to creating candlestick charts in Python using data visualization libraries mplfinance (matplotlib), Plotly, Bokeh, Bqplot, and Cufflinks. The tutorial covers a simple styling guide as well. Charts created using mplfinance are static whereas interactive for other libraries.

Sunny Solanki  Sunny Solanki
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
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
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
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
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
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
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
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
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.