Data science and machine learning are the most needed technologies in this era, prompting everyone to learn different libraries and software packages to realize them. This blog post will focus on Python libraries for data science and machine learning. These are the libraries that let you master the two most hyped skills on the market.

The following is a list of topics that will be covered in this blog:

Introduction to Data Science and Machine Learning Why should Python be used for data science and machine learning? Python Library for Data Science and Machine Learning Python Library for Statistics Python Library for Visualization Python Library for Machine Learning Python Library for Deep Learning Python Library for Natural Language Processing Introduction to Data Science and Machine Learning

When I started to study data science and machine learning, this problem always bothered me the most. What caused the heated discussion around these two topics?

Hum has a lot to do with the amount of data we generate. Data is the fuel needed to promote the ML model, and since we are in the era of big data, it is clear why data science is regarded as the most promising job role in this era!

I would say that data science and machine learning are skills, not just technology. They are the skills needed to gain useful insights from data and solve problems by building predictive models.

Formally speaking, they are defined like this.

Data science is a process of extracting useful information from data to solve practical problems.

Machine learning is a process that allows machines to learn how to solve problems by providing a large amount of data.

These two fields are highly interrelated.

Machine learning is a part of data science, which uses ML algorithm and other statistical techniques to understand how data affects and develops business.

Why use Python?

Python ranks first among the most popular programming languages in machine learning and data science. Let's find out why.

Easy to learn: Python uses a very simple syntax, which can be used to achieve simple calculations, such as adding two strings in a complex process, such as building a complex ML model. Less code: Realizing data science and machine learning involves countless algorithms. Thanks to Python's support for predefined packages, we don't have to write algorithms. In order to make things simple, Python provides a method of "checking while coding", thus reducing the burden of testing code. Pre-built library:? Python has more than 100 pre-built libraries for implementing various ML and deep learning algorithms. Therefore, whenever you want to run an algorithm on a data set, all you need to do is install and load the necessary packages with one command. Examples of pre-built libraries include NumPy, Keras, Tensorflow, Pytorch, etc. Platform independence: Python can run on a variety of platforms, including Windows, macOS, Linux, Unix and so on. When transferring code from one platform to another, you can use a software package such as PyInstaller, which will solve all the dependency problems. Lots of community support: Besides a lot of supporters, Python has many communities, groups and forums where programmers can post their mistakes and help each other. Python library

One of the most important reasons why Python is popular in AI and ML fields is that Python provides thousands of built-in libraries with built-in functions and methods, which can easily analyze, process, process and model data. . In the next section, we will discuss libraries used for the following tasks:

Visual data modeling of statistical analysis data and NLP statistical analysis of machine learning deep learning

Statistics is one of the most basic foundations of data science and machine learning. All ML and DL algorithms and techniques are based on the basic principles and concepts of statistics.

Python comes with a large number of libraries that are only used for statistical analysis. In this blog, we will focus on the top statistical software packages, which provide built-in functions to perform the most complex statistical calculations.

The following is a list of top Python libraries for statistical analysis:

statistical model

NumPy or digital Python is one of the most commonly used Python libraries. The main function of the library is to support multidimensional arrays of mathematical and logical operations. NumPy provides functions that can be used to index, classify, shape and transmit images and sound waves. They are multidimensional real number arrays.

The following is a list of functions of NumPy:

It is necessary for machine learning algorithms (such as linear regression, logical regression, naive Bayes, etc.). ) to perform simple to complex mathematical and scientific calculations, strong support for multi-dimensional array objects, and set up Fourier transform and data processing routines to handle array elements to perform linear algebraic calculations. SciPy

SciPy library based on NumPy is a set of sub-packages, which can help solve the most basic problems related to statistical analysis. SciPy library is used to process array elements defined by NumPy library, so it is usually used to calculate mathematical equations that NumPy cannot complete.

The following is a list of SciPy functions:

It is used with NumPy array to provide a platform and many mathematical methods, such as numerical integration and optimization. It has a set of subpackets, which can be used for vector quantization, Fourier transform, integration, interpolation and so on. Provides a complete stack of linear algebraic functions, which can be used for more advanced calculations, such as clustering using k-means algorithm. Provide support for signal processing, data structure and numerical algorithm, and create sparse matrix. panda

Pandas is another important statistical database, which is mainly used in statistics, finance, economy, data analysis and other fields. The library relies on NumPy arrays to process Pandas data objects. NumPy, Pandas and SciPy are all very dependent on each other in scientific calculation and data processing.

I am often asked to choose the best panda NumPy and SciPy, but I prefer to use them because they are very dependent on each other. Pandas is one of the best libraries for processing large amounts of data, and NumPy has excellent support for multidimensional arrays. Scipy, on the other hand, provides a set of subpackages that perform most statistical analysis tasks.

The following is a list of panda functions:

Create fast and efficient DataFrame objects using predefined and custom indexes. It can be used to process large data sets and execute subsets, data slices, indexes and so on. Provides built-in functions for creating Excel charts and performing complex data analysis tasks, such as descriptive statistical analysis, data sorting, transformation, operation, visualization, etc. Provide a statistical model for processing time series data.

StatsModels Python software package is built on NumPy and SciPy, which is the best choice for creating statistical models, data processing and model evaluation. In addition to using NumPy array and scientific model in SciPy library, Pandas is also integrated for effective data processing. The library is famous for statistical calculation, statistical testing and data exploration.

The following is a list of functions of StatsModels:

The best library to perform statistical tests and hypothesis tests that cannot be found in NumPy and SciPy libraries. Provide the realization of R formula to realize better statistical analysis. It belongs to R language commonly used by statisticians. Because it widely supports statistical calculation, it is usually used to realize generalized linear model (GLM) and ordinary least square linear regression (OLM) models. Statistical tests, including hypothesis testing (zero theory), are all done with StatsModels library. Therefore, they are the most commonly used and effective Python libraries for statistical analysis. Now we enter the data visualization part of data science and machine learning.

Data visualization

Caption 1000 words. We have all heard of art quotations, but so have data science and machine learning.

Data visualization is to effectively express key insights from data through graphic representation. Including graphics, charts, mind maps, heat maps, histograms, density maps and so on. Study the correlation between various data variables.

In this blog, we will focus on the best Python data visualization software packages, which provide built-in functions to study the dependencies between various data functions.

The following is a list of top Python libraries for data visualization:

matplotlibmatplotlibplotybokhmatplotlib

Matplotlib is the most basic data visualization software package in Python. It supports various graphs, such as histogram, bar graph, power spectrum, error graph, etc. It is a two-dimensional graphics library, which can generate clear graphics and is very important for exploratory data analysis (EDA).

This is a list of functions of Matplotlib:

Matplotlib makes it extremely easy to draw graphics by providing functions such as selecting appropriate line styles, font styles and formatting axes. The graphs you create can help you clearly understand trends and patterns and relate them. They are usually tools for reasoning quantitative information. It contains the Pyplot module, which provides an interface very similar to the MATLAB user interface. This is one of the best functions of the Matplotlib software package. Provide object-oriented API module, and use GUI tools (such as Tkinter, wxPython, Qt, etc.) to integrate graphics into applications. ).Matplotlib

Matplotlib library forms the basis of Seaborn library. Compared with Matplotlib, Seaborn can be used to create more attractive and descriptive statistical charts. In addition to extensive support for data visualization, Seaborn also provides a built-in data set API for studying the relationship between multiple variables.

The following is a list of Seaborn's functions:

Provides options for analyzing and visualizing univariate and bivariate data points and comparing data with other subsets of data. Support automatic statistical estimation and graphical representation of linear regression models of various target variables. By providing the function of performing advanced abstraction, a complex visualization for constructing multi-graph grids is constructed. It has many built-in themes, which can be used for styling and creating matPlotylib plot.

Ploty is one of the most famous graphical Python libraries. It provides interactive graphics to understand the correlation between target variables and predicted variables. It can be used to analyze and visualize statistical, financial, commercial and scientific data to generate clear charts, sub-charts, heat maps, 3D charts and so on.

This is a list of features that make Ploty one of the best visualization libraries:

It has more than 30 chart types, including 3D charts, scientific and statistical charts, SVG maps and so on, to achieve clear visualization. With Ploty's Python API, you can create public * * */private dashboards composed of charts, graphs, texts and Web images. Visualizations created with Ploty are serialized in JSON format, so you can easily access them on different platforms, such as R, MATLAB and Julia. It comes with a built-in API called Plotly Grid, which allows you to import data directly into Ploty environment. Scattered view

Bokeh is one of the most interactive libraries in Python, which can be used to build descriptive graphical representations of Web browsers. It can conveniently handle huge data sets and establish general diagrams, which is helpful to carry out extensive EDA. Bokeh provides the most well-defined functions to build interactive drawings, dashboards and data applications.

This is a list of functions of Shotshot:

Use simple commands to help you quickly create complex statistical charts, and support output in HTML, notebook and server formats. It also supports multiple language bindings, including R, Python, lua, Julia and so on. Flask and django also integrate Bokeh, so you can also show visualization effects on these applications. It provides support for converting into visual files written by other libraries (such as matplotlib, seaborn, ggplot, etc.). Therefore, these are the most useful Python libraries for data visualization. Now, let's discuss the top Python library used to realize the whole machine learning process.

machine learning

Creating a machine learning model that can accurately predict results or solve specific problems is the most important part of any data science project.

Realize ML, DL, etc. It involves writing thousands of lines of code, which may become more troublesome when you want to create a model to solve complex problems through neural networks. Fortunately, we don't need to write any algorithms, because Python comes with several software packages, which are only used to realize the techniques and algorithms of machine learning.

In this blog, we will focus on top-level ML software packages, which provide built-in functions to implement all ML algorithms.

The following is a list of top Python libraries for machine learning:

sci kit-learnxgboosteli 5 sci kit-learn

Scikit-learn is one of the most useful Python libraries and the best library for data modeling and model evaluation. It has countless functions, and its sole purpose is to create models. It contains all supervised and unsupervised machine learning algorithms, and also has well-defined functions for integrated learning and promoting machine learning.

The following is a list of functions of Scikit learning:

Provide a set of standard data sets to help you start machine learning. For example, the famous Iris data set and Boston house price data set are part of Scikit-learn library. Built-in methods for performing supervised and unsupervised machine learning. This includes problem solving, clustering, classification, regression and anomaly detection. Through the built-in feature extraction and feature selection functions, it can help identify important attributes in data. It provides a method of cross-validation to evaluate the model performance, and also provides a parameter adjustment function to optimize the model performance. XGBoost

XGBoost stands for "extreme gradient enhancement" and is one of the best Python software packages to enhance machine learning. Libraries such as LightGBM and CatBoost are also equipped with well-defined functions and methods. The main purpose of establishing this library is to realize gradient lifting and improve the performance and accuracy of machine learning model.

Here are some of its main functions:

Originally written in C ++, this library is considered as one of the fastest and most effective libraries to improve the performance of machine learning model. The core XGBoost algorithm can be parallelized and can effectively use the functions of multi-core computers. This also makes the library powerful enough to handle a large number of data sets and work across data sets networks. Provides internal parameters for performing cross-validation, parameter adjustment, regularization and handling missing values, and also provides an API compatible with scikit-learn. This library is often used in top data science and machine learning competitions because it has been proved to be superior to other algorithms. ElI5

ELI5 is another Python library that focuses on improving the performance of machine learning models. The library is relatively new, and is usually used with XGBoost, LightGBM, CatBoost, etc. Improve the accuracy of machine learning model.

Here are some of its main functions:

Provide integration with Scikit-learn software package to express functional importance and explain the prediction of decision tree and tree-based integration. It analyzes and explains the predictions made by XGBClassifier, XGBRegressor, LGBMClassifier, LGBMRegressor, catboostClassifier, CatBoostRegressor and CatBoost. It supports the implementation of various algorithms to check the black box model, including the text interpreter module, which allows you to interpret the predictions made by the text classifier. It is helpful to analyze the weight and prediction of scikit learning general linear model (GLM), including linear regression and classifier. Deep learning

The biggest progress in machine learning and artificial intelligence is through deep learning. With the introduction of deep learning, it is now possible to build complex models and deal with huge data sets. Fortunately, Python provides the best deep learning software package to help build effective neural networks.

In this blog, we will focus on providing top-level deep learning software packages to realize the built-in functions of complex neural networks.

The following is a list of top Python libraries for deep learning:

TensorFlowPytorchKerasTensorFlow

TensorFlow is one of the best Python libraries for deep learning, and it is an open source library for data stream programming across various tasks. It is a symbolic mathematics library, which is used to build powerful and accurate neural networks. It provides an intuitive multi-platform programming interface and can be highly extended in a wide range of fields.

The following are some key functions of TensorFlow:

It allows you to build and train multiple neural networks to help adapt to large projects and data sets. In addition to supporting neural networks, it also provides functions and methods to perform statistical analysis. For example, it has built-in functions (such as Bernoulli, Chi2, Uniform, Gamma, etc.) to create probability models and Bayesian networks. ). The library provides layered components, which can operate the weights and deviations hierarchically, and can also improve the performance of the model by implementing regularization techniques (such as batch standardization and packet loss). ). It comes with a visualization program called TensorBoard, which can create interactive graphics and visual graphics to understand the dependency of data functions. An open source machine learning framework

Pytorch is an open source scientific computing software package based on Python, which is used to realize deep learning technology and neural network on large data sets. Facebook actively uses this library to develop neural networks to help complete various tasks, such as face recognition and automatic labeling.

The following are some of the main functions of Pytorch:

Provide an easy-to-use API for integration with other data science and machine learning frameworks. Like NumPy, Pytorch provides a multidimensional array called Tensors. Unlike NumPy, it can even be used on GPU. It can not only be used for modeling large-scale neural networks, but also provides an interface with more than 200 mathematical operations for statistical analysis. Create a dynamic calculation diagram, and establish a dynamic diagram at each code execution point. These charts are helpful for time series analysis and real-time sales forecast. Klaas

Keras is considered as one of the best deep learning libraries in Python. It provides comprehensive support for the construction, analysis, evaluation and improvement of neural networks. Keras is built based on Theano and TensorFlow Python libraries, which provides additional functions for building complex large-scale deep learning models.

Here are some key features of Keras:

Provide support for the construction of all kinds of neural networks, that is, complete connection, convolution, collection, circulation, embedding and so on. For large data sets and problems, these models can be further combined to create a complete neural network, which has built-in functions to perform neural network calculation, such as definition layer, target, activation function, optimizer and a large number of tools, making it easier to process image and text data. It has some preprocessing data sets and training models, including MNIST, VGG, Inception, SqueezeNet, ResNet and so on. It is easy to expand and supports adding new modules including functions and methods. natural language processing

Have you ever thought about how Google correctly predicts what you are searching for? The technology behind Alexa, Siri and other chat bots is natural language processing. NLP has played a great role in the system design based on artificial intelligence, which helps to describe the interaction between human language and computer.

In this blog, we will focus on top-level natural language processing packages, which provide built-in functions to implement advanced systems based on artificial intelligence.

This is a list of top Python libraries for natural language processing:

NLTKspaCyGensimNLTK (Natural Language Toolkit)

NLTK is considered as the best Python software package to analyze human language and behavior. NLTK library is the first choice for most data scientists. It provides an easy-to-use interface, including more than 50 kinds of corpus and vocabulary resources, which is helpful to describe the interaction between people and build AI-based systems (such as recommendation engines).

The following are some key functions of the NLTK library:

Provide a set of data and text processing methods for text analysis, classification, tagging, stemming, tagging, parsing and semantic reasoning. Wrappers containing industrial NLP libraries are used to build complex systems to help classify texts and find behavioral trends and patterns of human language. It has a comprehensive guide to describe the implementation of computational linguistics and a complete API document guide, which can help all novices to start using NLP. It has a huge community of users and professionals, providing comprehensive tutorials and quick guides to learn how to use Python for computational linguistics. liberal

SpaCy is a free open source Python library for implementing advanced natural language processing (NLP) technology. When you deal with a large number of texts, it is very important to understand the morphological meaning of texts and how to classify them to understand human language. These tasks can be easily accomplished through space.

The following are some key functions of the space library:

In addition to language computing, spaCy also provides a separate module to build, train and test statistical models, so as to better help you understand the meaning of words. Built-in language notes can help you analyze the grammatical structure of sentences. This not only helps to understand the test, but also helps to find the relationship between different words in the sentence. It can be used to apply tokenization to complex nested tags containing abbreviations and multiple punctuation marks. Besides being very powerful and fast, spaCy also provides support for more than 5 1 languages. Gensim

Gensim is another open source Python package. Its modeling aims to extract semantic topics from large-scale documents and texts, so as to process, analyze and predict human behavior through statistical models and language calculation. No matter whether the data is original data or unstructured data, it has the ability to handle massive data.

The following are some of the main functions of Genism:

It can be used to build a model to effectively classify documents by understanding the statistical semantics of each word. Have Word2Vec, FastText, latent semantic analysis and other text processing algorithms. These algorithms study statistical patterns in documents to filter out unnecessary words and build models with only important functions. Provides I/O wrappers and readers that can import and support various data formats. It has a simple and intuitive interface that beginners can use easily. The API learning curve is also very low, which explains why many developers like this library.