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:
For an in-depth understanding of the above concepts, check out the sections below.
This article is your comprehensive guide to working with realtime streaming data using Bokeh. Learn how to visualize and analyze your streaming data in real-time with Bokeh's powerful features and tools. Whether you're a beginner or a seasoned data scientist, this article will provide you with the knowledge and skills you need to effectively work with streaming data using Bokeh.
Learn how to create a Simple Dashboard using Panel with Widgets in this comprehensive tutorial. With easy-to-follow steps, you'll be able to design and customize your dashboard to fit your specific needs. Increase your productivity and organization with this powerful data visualization tool.
Streamline your dashboard creation process with Streamlit, a Python library that allows you to build interactive dashboards with minimal coding. This article will show you how to create a dashboard with fewer than 50 lines of code, saving you time and effort in the development process. Learn how to use Streamlit's intuitive syntax and pre-built components to create a functional dashboard in no time.
Learn how to create visually appealing sales funnel charts using Matplotlib, a popular data visualization library in Python. This step-by-step guide will show you how to use Matplotlib to visualize your sales data and make data-driven decisions for your business.
Discover how to create population pyramid charts using Matplotlib, a powerful data visualization library in Python. This tutorial will guide you through the process of building a population pyramid chart, a commonly used visualization for analyzing demographic data. With customizable styles and features, you can create a professional-looking chart that effectively communicates population trends and distribution. Enhance your data visualization skills and create insightful population pyramid charts with this step-by-step guide using Matplotlib.
Learn how to create professional-looking timelines using Matplotlib, a popular data visualization library in Python. This tutorial will walk you through the step-by-step process of building a timeline chart with customizable styles and features, perfect for presenting historical events, project plans, or schedules. Improve your data visualization skills and impress your audience with this easy-to-follow guide on creating timelines with Matplotlib.
A simple guide on how to create a Venn diagram using Python data visualization library Matplotlib. We explain how to create a data science Venn diagram using Matplotlib.
A simple guide on how to watermark matplotlib charts. The tutorial explains how to add image and text watermarks to the matplotlib chart with simple examples.
Discover how to create Hexbin Charts using Matplotlib, a popular Python library for data visualization. This tutorial will guide you through the process of creating stunning visualizations that allow you to explore and analyze your data in a new and exciting way.
Learn how to create visually appealing Gauge Charts using the powerful Python data visualization library, Matplotlib. Follow this step-by-step tutorial and add an extra layer of insight to your data.
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 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.
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.
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.
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.
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.
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 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.