7 Libraries That Will Make Data Management Easier For Your Next Machine Learning Project

The amount of data we are creating on a daily basis is growing at a rapid rate. This might seem like good news because we have more data that can be used to train machines, which will make them smarter but it also poses many data management challenges such as data bias, variance, storage, security and compliance. To overcome these challenges, you will first have to understand the machine learning pipeline structure.

The machine learning pipeline consists of four stages:

  • Pre-processing
  • Learning
  • Evaluation
  • Prediction

Most machine learning applications follow the same standard pipeline structure. Due to this, researchers were able to create frameworks to help app development services automate and streamline mundane tasks. That is why we have frameworks like Tensor and PyTorch which help you manage this pipeline in a more efficient manner through their modules.

These machine learning frameworks can also come in handy when you have to develop new machine learning models, training machines and performing predictive tasks but they still lag behind when it comes to performing highly specific tasks such as data preparation and visualization. To fill in these gaps, you need specific data libraries that can do a better job in handling those specialized tasks.

In this article, you will learn about seven data libraries that make data management a breeze for your machine learning and deep learning projects.

If you want to perform scientific computing and calculations in your machine learning projects, then there is no better library than SciPy. This open-source Python library provides data scientists access to scientific, mathematical and engineering functions. Visualizing data is a breeze thanks to easy-to-learn syntax. Additionally, it can also improve your system prototyping processes and data processing capabilities.

Libraries like SciPy extend Python capabilities as a general-purpose programming language and put it in direct competition against specialized software used for this purpose such as MATLAB and Scilab. All the SciPy functions are neatly organized and tucked away under domain-based sub-packages. What’s more, developers can easily be able to integrate already created C and Fortran code in the Python interpreter, which offers code usability to a certain degree. With libraries for almost every task available, SciPy makes life easier for machine learning developers.

  • Matplotlib

Whether you want to create highly immersive or interactive visualization or want static graphs or plots, Matplotlib have you covered. This data visualization library can also be used for creating high-quality visuals for both offline and online publications. Moreover, this python library can also come in handy when it comes to extracting useful insights from large datasets. By using application programming interfaces, developers can also integrate these graphs and plots into their mobile apps. You can also add images, colors, labels and even form fields.

  • Pandas

Pandas is a Python library that enables you to perform complex mathematical operations quickly while offering user flexibility with the data structure. What makes this library special is its intuitive data analysis capabilities and comprehensive feature set. It also gives users access to many data visualization methods, which makes extracting and presenting useful insights from data far easier. You can even perform many calculations on your data by using Pandas. It even allows you to handle missing data by filling and dropping it. You can even perform data insertion, deletion, or align data automatically or explicitly.

 

It offers support for two major types of data structures:

  • One dimensional Series
  • Two dimensional DataFrames
  • Seaborn

Despite being feature-rich, there are instances when machine learning developers have to use third-party packages. Based on matplotlib, Seaborn is a data visualization library that allows developers to extend the functionality while reducing the time required to generate visually appealing graphs highlighting useful insights extracted from large datasets.

Seaborn plotting functions work on a deep frame data structure that empowers developers to perform semantic mapping and statistical aggregation processes to create insightful data visualization. With dataset centric application programming interface, you can even explain what different elements of your plot and graph mean allowing businesses to make better sense out of data.

  • NumPy

NumPy is a popular python library that specializes in handling multi-dimensional arrays as well as performing complex mathematical operations. The biggest advantage of NumPy is that it can easily be wrapped around the corresponding libraries in the C language. This means that it offers the best of both worlds just like when you buy VPS server.

It does this by combining the ease of use of Python with the efficiency of the C language. Whether you want to perform fast matrix operations or just want to store data or create arrays with random numbers. Some other libraries on this list such as Pandas heavily rely on NumPy.

  • Scikit Learn

Initially developed at Google’s summer of code by David Cournapeau, Scikit Learn has already been used for the creation of many successful and popular machine learning models. Its intuitive predictive data analysis tool does a great job. Built upon other libraries such as NumPy, SciPy and more, it shares the same characteristics. If you are struggling to decide which machine learning algorithm or deep learning model you should be adopting, Scikit Learn can make your decision a lot easier by letting you compare different machine learning algorithms and models.

Scikit Learn allows you to perform six major functions:

  • Classification
  • Clustering
  • Regression
  • Model Selection
  • Dimensionality reduction
  • Pre-processing

 

  • Flask

If you are familiar with Flask, you might argue that Flask is not a library but is a micro web framework and you are right. The only difference is that it is not packaged with the components that are critical for other Digital marketing strategy framework. Thanks to third-party extensions, machine learning developers can embed those components into the Flask application. Due to this, Flask consumes fewer resources because it is lightweight and drastically reduces your development time. This makes it an ideal choice for those who want to feed their machine learning models but don’t want to get their hands dirty with web programming.

Which libraries do you use to manage data in your machine learning projects? Let us know in the comments section below.

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