H2O’s driverless AI offers simple data visualization techniques for representing high-degree feature interactions and nonlinear model behavior.
It provides Machine Learning Interpretability (MLI) through visualizations that clarify modeling results and the effect of features in a model.
Go through the following link to read more about H2O’s Driverless AI perform MLI.
Machine Learning Interpretability Python Libraries for Audio Processing Audio processing or audio analysis refers to the extraction of information and meaning from audio signals for analysis or classification or any other task.
It’s becoming a popular function in deep learning so keep an ear out for that.
LibROSA LibROSA is a Python library for music and audio analysis.
It provides the building blocks necessary to create music information retrieval systems.
Click this link to check out the installation details.
Here’s an in-depth article on audio processing and how it works: Getting Started with Audio Data Analysis using Deep Learning (with case study) Madmom The name might sound funny, but Madmom is a pretty nifty audio data analysis Python library.
It is an audio signal processing library written in Python with a strong focus on music information retrieval (MIR) tasks.
You need the following prerequisites to install Madmom: NumPy SciPy Cython Mido And you need the below packages to test the installation: PyTest PyAudio PyFftw The code to install Madmom: pip install madmom We even have an article to learn how Madmom works for music information retrieval: Learn Audio Beat Tracking for Music Information Retrieval (with Python codes) pyAudioAnalysis pyAudioAnalysis is a Python library for audio feature extraction, classification, and segmentation.
It covers a wide range of audio analysis tasks, such as: Classify unknown sounds Detect audio events and exclude silence periods from long recordings Perform supervised and unsupervised segmentation Extract audio thumbnails and much more You can install it by using the following code: pip install pyAudioAnalysis Python Libraries for Image Processing You must learn how to work with image data if you’re looking for a role in the data science industry.
Image processing is becoming ubiquitous as organizations are able to collect more and more data (thanks largely to advancements in computational resources).
So make sure you’re comfortable with at least one of the below three Python libraries.
OpenCV-Python When it comes to image processing, OpenCV is the first name that comes to my mind.
OpenCV-Python is the Python API for image processing, combining the best qualities of the OpenCV C++ API and the Python language.
It is mainly designed to solve computer vision problems.
OpenCV-Python makes use of NumPy, which we’ve seen above.
All the OpenCV array structures are converted to and from NumPy arrays.
This also makes it easier to integrate with other libraries that use NumPy such as SciPy and Matplotlib.
Install OpenCV-Python in your system: pip3 install opencv-python Here are two popular tutorials on how to use OpenCV in Python: Building a Face Detection Model from Video using Deep Learning (Python Implementation) 16 OpenCV Functions to Start your Computer Vision journey (with Python code) Scikit-image Another python dependency for image processing is Scikit-image.
It is a collection of algorithms for performing multiple and diverse image processing tasks.
You can use it to perform image segmentation, geometric transformations, color space manipulation, analysis, filtering, morphology, feature detection, and much more.
We need to have the below packages before installing scikit-image: Python (>= 3.
5) NumPy (>= 1.
11.
0) SciPy (>= 0.
17.
0) joblib (>= 0.
11) And this is how you can install scikit-image on your machine: pip install -U scikit-learn Pillow Pillow is the newer version of PIL (Python Imaging Library).
It is forked from PIL and has been adopted as a replacement for the original PIL in some Linux distributions like Ubuntu.
Pillow offers several standard procedures for performing image manipulation: Per-pixel manipulations Masking and transparency handling Image filtering, such as blurring, contouring, smoothing, or edge finding Image enhancing, such as sharpening, adjusting brightness, contrast or color Adding text to images, and much more!.How to install Pillow?.It’s this simple: pip install Pillow Check out the following AI comic illustrating the use of Pillow in computer vision: The AI Comic: Z.
A.
I.
N – Issue #2: Facial Recognition using Computer Vision Python Libraries for Database Learning how to store, access and retrieve data from a database is a must-have skill for any data scientist.
You simply cannot escape from this aspect of the role.
Building models is great but how would you do that without first retrieving the data?.I’ve picked out two Python libraries related to SQL that you might find useful.
psycopg Psycopg is the most popular PostgreSQL (an advanced open source relational database ) adapter for the Python programming language.
At its core, Psycopg fully implements the Python DB API 2.
0 specifications.
The current psycopg2 implementation supports: Python version 2.
7 Python 3 versions from 3.
4 to 3.
7 PostgreSQL server versions from 7.
4 to 11 PostgreSQL client library version from 9.
1 Here’s how you can install psycopg2: pip install psycopg2 SQLAlchemy Ah, SQL.
The most popular database language.
SQLAlchemy, a Python SQL toolkit and Object Relational Mapper, gives application developers the full power and flexibility of SQL.
It is designed for efficient and high-performing database access.
SQLAlchemy considers the database to be a relational algebra engine, not just a collection of tables.
To install SQLAlchemy, you can use the following line of code: pip install SQLAlchemy Python Libraries for Deployment Do you know what model deployment is?.If not, you should learn this ASAP.
Deploying a model means putting your final model into the final application (or the production environment as it’s technically called).
Flask Flask is a web framework written in Python that is popularly used for deploying data science models.
Flask has two components: Werkzeug: It is a utility library for the Python programming language Jinja: It is a template engine for Python Check out the example below to print “Hello world”: View the code on Gist.
The below article is a good starting point to learn Flask: Tutorial to deploy Machine Learning models in Production as APIs (using Flask) End Notes In this article, we saw a huge bundle of python libraries that are commonly used while doing a data science project.
There are a LOT more libraries that are out there but these are the core ones every data scientist should know.
Any Python libraries I missed?.Or any library from our list which you particularly found useful?.Let me know in the comments section below!.You can also read this article on Analytics Vidhyas Android APP Share this:Click to share on LinkedIn (Opens in new window)Click to share on Facebook (Opens in new window)Click to share on Twitter (Opens in new window)Click to share on Pocket (Opens in new window)Click to share on Reddit (Opens in new window) Related Articles (adsbygoogle = window.
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