Introduction

Many real-world machine learning problems can be framed as graph problems. On online platforms, users often share assets (e.g. photos) and interact with each other (e.g. messages, bookings, reviews)…

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Python is the most widely used programming language today. While solving data science problems and challenges, Python continues to surprise users. Most data scientists are already using the possibilities of Python programming every day. Python is easy to learn and debug language that has been widely used, object-oriented, open-source and powerful. Python has been built with high-performance Python data science libraries that programmers use every day to solve problems.

Scrapy

Scrapy is fast, open-source web crawling frameworks (spider bots) written in Python for large scale web scraping. It is commonly used to efficiently extract the data from the web page, process them as needed and store them in preferred structure and format with the help of selectors based on XPath. For example, URLs or contact info. Scrapy is a great tool for scraping data used in Python machine learning models.

Developers use it for gathering data from APIs. This full-fledged framework follows the “Don’t Repeat Yourself” principle in the design of its interface. As a result, the tool inspires users to write universal code that can be reused for building and scaling large crawlers.

BeautifulSoup

Beautifulsoup is a python library most commonly known for web crawling and data scraping. Users can collect data that are available on some website without a proper CSV or API, and this library can help them scrape it and arrange it into the required format.

It is a parsing library in Python that enables web scraping from HTML and XML documents automatically detecting encodings and gracefully handles documents even with special characters. We can navigate a parsed document and find what we need which makes it quick and painless to extract the data from the web pages.

pandas

Pandas is a library created to help developers work with “labelled” and “relational” data intuitively. It’s based on two main data structures: “Series” (one-dimensional, like a list of items) and “Data Frames” (two-dimensional, like a table with multiple columns). Pandas allow converting data structures to DataFrame objects, handling missing data, and adding/deleting columns from DataFrame, imputing missing files, and plotting data with histogram or plot box. It’s a must-have for data wrangling, manipulation, and visualization.

Eloquent syntax and rich functionalities give you the freedom to deal with missing data providing high-level data structures and manipulation tools. It enables to create its own function and run it across a series of data with high-level abstraction.

matplotlib

Matplotlib is a plotting library for Python with around 26,000 comments on GitHub and a very vibrant community of about 700 contributors is extensively used for data visualization. This standard data science library helps to generate data visualizations such as two-dimensional diagrams and graphs (histograms, scatterplots, non-Cartesian coordinates graphs). It also provides an object-oriented API, which can be used to embed those plots into applications.

Because of this library Python can compete with scientific tools like MatLab or Mathematica. However, developers need to write more code than usual while using this library for generating advanced visualizations.

Mostly used for Correlation analysis of variables, Visualize the distribution of data to gain instant insights, outlier detection using a scatter plot etc.

plotly

Plotly provides high quality, publication-ready and interactive charts. Boxplot, heatmaps, bubble charts are a few examples of the types of available charts.

It is one of the finest data visualization tools available built on top of the visualization library D3.js, HTML, and CSS. It is created using Python and the Django framework.

The library works very well in interactive web applications. Its creators are busily expanding the library with new graphics and features for supporting multiple linked views, animation, and crosstalk integration.

Seaborn

Seaborn is based on Matplotlib and serves as a useful Python machine learning tool for visualizing statistical models — heatmaps and other types of visualizations that summarize data and depict the overall distributions. Many data scientists prefer seaborn over matplotlib due to its high-level interface for drawing attractive and informative statistical graphics.

Seaborn provides easy functions that help to focus on the plot and benefit from an extensive gallery of visualizations (including complex ones like time series, joint plots, and violin diagrams).

bokeh

Bokeh library is a great tool for creating interactive and scalable visualizations inside browsers using JavaScript widgets. It is fully independent of Matplotlib which focuses on interactivity and presents visualizations through modern browsers similarly to Data-Driven Documents (d3.js). It offers a set of graphs, interaction abilities (like linking plots or adding JavaScript widgets), and styling.

pydot

Pydot helps to generate oriented and non-oriented graphs serving as an interface to Graphviz. It is used to show the structure of graphs that comes in handy when you’re developing algorithms based on neural networks and decision trees.

NumPy

Numerical Python is the fundamental package for numerical, scientific computation in Python performing basic and advanced array operations. It is used heavily for the applications of Machine Learning and Deep Learning. It has around 18,000 comments on GitHub and an active community of 700 contributors. It helps to process arrays that store values of the same data type and makes performing math operations on arrays (and their vectorization) easier. The vectorization of mathematical operations on the NumPy array type increases performance and accelerates the execution time.

Numpy forms the base of other libraries, such as SciPy and scikit-learn, replacement of MATLAB when used with SciPy and matplotlib. It is a general-purpose array-processing package that provides high-performance multidimensional objects called arrays and tools for working with them. NumPy also addresses the slowness problem partly by providing these multidimensional arrays as well as providing functions and operators that operate efficiently on these arrays.

Numpy provides fast, precompiled functions for numerical routines as it follows array-oriented computing for better efficiency. It also supports an object-oriented approach for Compact and faster computations with vectorization.

SciPy

Scientific Python is extensively used for high-level computations such as scientific and technical computations because it extends NumPy and provides many user-friendly and efficient routines for scientific calculations. SciPy has around 19,000 comments on GitHub and an active community of about 600 contributors. It is used heavily in the fields of mathematics, science, and engineering. It is equivalent to using Matlab which is a paid tool.

This useful library includes modules for linear algebra, integration, optimization, and statistics. Its main functionality was built upon NumPy, so its arrays make use of this library. SciPy works great for all kinds of scientific programming projects (science, mathematics, and engineering). It offers efficient numerical routines such as numerical optimization, integration, and others in submodules.

It also includes high-level commands for data manipulation and visualization making optimization algorithms one of its applications. One of the major features is multidimensional image processing with the SciPy ndimage submodule. It includes built-in functions for solving differential equations and the Fourier transform.

TensorFlow

TensorFlow is an end-to-end machine learning library that includes tools, libraries, and resources for the research community to push the state of the art in deep learning and developers in the industry to build ML & DL powered applications. It’s the best tool for tasks like object identification, speech recognition, and many others. It helps in working with artificial neural networks that need to handle multiple data sets. The library includes various layer-helpers (tflearn, tf-slim, skflow), which make it even more functional. TensorFlow is constantly expanded with its new releases — including fixes in potential security vulnerabilities or improvements in the integration of TensorFlow and GPU.

TensorFlow provides better computational graph visualizations and reduces errors by 50 to 60 percent in neural machine learning. With seamless library management backed by Google, it also supports parallel computing to execute complex models. It is particularly useful for speech and image recognition, text-based applications, time-series analysis, video detection etc.

Keras

Keras provides a vast prelabeled dataset that can be used to directly import and load. It contains various implemented layers and parameters that can be used for the construction, configuration, training, and evaluation of neural networks. The deep learning models can be used directly to make predictions or extract its features without creating or training your own new model.

SciKit-Learn

Scikits is a group of packages in the SciPy Stack that were created for specific functionalities, for example, image processing. Scikit-learn uses the math operations of SciPy to expose a concise interface to the most common machine learning algorithms. Data scientists use it for handling standard machine learning and data mining tasks such as clustering, regression, model selection, dimensionality reduction, and classification. It provides numerous applications like clustering, classification, regression, model selection, dimensionality reduction etc.

PyTorch

PyTorch is a Python-based scientific computing package that uses the power of graphics processing units. PyTorch is based on Torch, which is an open-source deep learning library implemented in C, with a wrapper in Lua.

It is one of the most commonly preferred deep-learning research platforms built to provide maximum flexibility and speed. It has helped accelerate the research that goes into deep learning models by making them computationally faster and less expensive. It’s also used for other tasks for example, for creating dynamic computational graphs and calculating gradients automatically.

Some of the features such as robust Ecosystem, cloud support, distributed Training makes PyTorch famous for providing two of the most high-level features that are tensor computations with strong GPU acceleration support and building deep neural networks on a tape-based autograd system.

PyCaret

PyCaret is an open-source, machine learning library in Python that helps you from data preparation to model deployment. It helps you save tons of time by being a low-code library.

It is an easy to use machine learning library that will help you perform end-to-end machine learning experiments, whether that’s imputing missing values, encoding categorical data, feature engineering, hyperparameter tuning, or building ensemble models.

XGBoost

XGBoost library is used to implement machine learning algorithms under the Gradient Boosting framework. It is portable, flexible, and efficient. It offers parallel tree boosting that helps teams to resolve many data science problems. Another advantage is that developers can run the same code on major distributed environments such as Hadoop, SGE, and MPI.

This list is by no means complete! The Python ecosystem offers many other tools that can be helpful for data science work. Data scientists and software engineers involved in data science projects that use Python will use many of these tools, as they are essential for building high-performing ML models in Python. Python is a powerful yet simple language for all of your machine learning tasks.

Introduction

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