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NumPy, pandas, and Matplotlib
- Libraries like NumPy, pandas, and Matplotlib are foundational for AI tasks such as data manipulation, analysis, and visualization.
medium.com/ai-guys/python-essentials-libraries-and-tools-for-ai-programming-385eaccf890c
Jan 16, 2024 · Now that you know why Python is one of the top programming languages, here are the 10 best python libraries for machine learning and AI: 1. NumPy. NumPy is widely regarded as the best Python library for machine learning and AI. It is an open-source numerical library that can be used to perform various mathematical operations on different matrices.
Apr 5, 2024 · Let’s now explore famous Python libraries extensively used in AI and ML across multiple fields like Machine Learning, Deep Learning, Artificial Intelligence, Data Processing, Computer Vision, Natural Language Processing, Data Visualization, Web Development, and Web Scraping.
- Introduction
- Staple Python Libraries For Data Science
- Machine Learning Python Libraries
- Automated Machine Learning (AutoML) Python Libraries
- Deep Learning Python Libraries
- Python Libraries For Natural Language Processing
- Choosing The Best Python Library
Python is one of the most popular programming languages used across various tech disciplines, especially in data science and machine learning. Python offers an easy-to-code, object-oriented, high-level language with a broad collection of libraries for a multitude of use cases. It has over 137,000 libraries. One of the reasons Python is so valuable ...
1. NumPy
NumPy, is one of the most broadly-used open-source Python libraries and is mainly used for scientific computation. Its built-in mathematical functions enable lightning-speed computation and can support multidimensional data and large matrices. It is also used in linear algebra. NumPy Array is often used preferentially over lists as it uses less memory and is more convenient and efficient. According to NumPy’s website, it is an open-source project aiming to enable numerical computing with Pyth...
2. Pandas
Pandas is an open-source library commonly used in data science. It is primarily used for data analysis, data manipulation, and data cleaning. Pandas allow for simple data modeling and data analysis operations without needing to write a lot of code. As stated on their website, pandas is a fast, powerful, flexible, and easy-to-use open-source data analysis and manipulation tool. Some key features of this library include: 1. DataFrames, which allow for quick, efficient data manipulation and incl...
3. Matplotlib
Matplotlib is an extensive library for creating fixed, interactive, and animated Python visualizations. A large number of third-party packages extend and build on Matplotlib’s functionality, including several higher-level plotting interfaces (Seaborn, HoloViews, ggplot, etc.) Matplotlib is designed to be as functional as MATLAB, with the additional benefit of being able to use Python. It also has the advantage of being free and open source. It allows the user to visualize data using a variety...
7. LightGBM
LightGBM is an immensely popular open-source gradient boosting library that employs tree-based algorithms. It offers the following advantages: 1. Faster training speed and higher efficiency 2. Lower memory usage 3. Better accuracy 4. Support of parallel, distributed, and GPU learning 5. Capable of handling large-scale data It can be used for both supervised classification and regression tasks. You can check out the official documentation or their GitHubto learn more about this amazing framewo...
8. XGBoost
XGBoost is another widely-used distributed gradient boosting library created to be portable, flexible, and efficient. It enables the implementation of machine learning algorithms within the gradient boosting framework. XGBoost offers (GBDT) gradient boosted decision trees, a parallel tree boosting that offers solutions to many data science problems quickly and accurately. The same code runs on major distributed environments (Hadoop, SGE, MPI) and can solve innumerable problems. XGBoost has ga...
9. CatBoost
Catboost is a fast, scalable, high-performance gradient boosting on decision trees library used for ranking, classification, regression, and other machine learning tasks for Python, R, Java, and C++. It supports computation on CPU and GPU. As the successor of the MatrixNet algorithm, it is widely used for ranking tasks, forecasting, and making recommendations. Thanks to its universal character, it can be applied across a wide range of areas and to a variety of problems. The advantages of CatB...
13. PyCaret
This hugely popular, open-source machine learning library automates machine learning workflows in Python using very little code. It is an end-to-end tool for model management and machine learning that can dramatically accelerate the experiment cycle. Compared to other open-source machine learning libraries, PyCaret offers a low-code solution that can replace hundreds of lines of code with a mere few. This makes experiments exponentially fast and efficient. PyCaret is currently available for u...
14. H2O
H2O is a machine learning and predictive analytics platform that enables the construction of machine learning models on big data. It also provides easy productionalization of those models in an enterprise environment. H2O’s core code is written in Java. The algorithms use the Java Fork/Join framework for multi-threading and are implemented on top of H2O’s distributed Map/Reduce framework. H2O is licensed under the Apache License, Version 2.0, and is available for Python, R, and Java languages...
15. TPOT
TPOT is an Automated Machine Learning (AutoML) library. It was built as an add-on to scikit-learn and uses Genetic Programming (GP) to determine the best model pipeline for a given dataset. Using a special version of genetic programming, TPOT can automatically design and optimize data transformations and machine learning models, ensuring maximal classification accuracy for a given supervised learningdata set. TPOT is one of the oldest AutoML libraries in Python. It can be used both for classi...
18. TensorFlow
TensorFlow is a popular open-source library for high-performance numerical computation developed by the Google Brain team at Google, and a mainstay in the field of deep learning research. As stated on the official website, TensorFlow is an end-to-end open-source platform for machine learning. It offers an extensive, versatile assortment of tools, libraries, and community resources for machine learning researchers and developers. Some of the features of TensorFlow that made it a popular and wi...
19. PyTorch
PyTorchis a machine learning framework that dramatically speeds up the journey from research prototyping to production deployment. It is an optimized tensor library for deep learning using GPUs and CPUs, and is considered to be an alternative to TensorFlow. Over time, PyTorch’s popularity has grown to overtake TensorFlow on Google trends. It was developed and is maintained by Facebook and is currently available for use under BSD. According to the official website, key features of PyTorch are:...
20. FastAI
FastAI is a deep learning library offering users high-level components that can generate state-of-the-art results effortlessly. It also includes low-level components that can be interchanged to develop new approaches. It aims to do both of these things without substantially compromising its ease of use, flexibility, or performance. Features: 1. A type dispatch system for Python along with a semantic type hierarchy for tensors. 2. A GPU-optimized computer vision library that is fully extendabl...
23. NLTK
NLTK is one of the main platforms for constructing Python programs to parse human language data. It provides easy-to-use interfaces to more than 50 corpora and lexical resources like WordNet, as well as a suite of text processing libraries; it also offers wrappers for industrial-strength NLP libraries. NLTK has been called “a wonderful tool for teaching, and working in, computational linguistics using Python”. The library is open-source and available for use under the Apache License 2.0. To l...
24. spaCy
spaCy is an industrial-strength, open-source natural language processing library in Python. spaCy excels at large-scale information extraction tasks. It is written from the ground up in carefully memory-managed Cython. spaCy is the ideal library to use if your application needs to process massive web dumps. Features: 1. spaCy supports CPU and GPU processing. 2. Offers support for 66+ languages 3. Has 73 trained pipelines for 22 languages 4. Multi-task learning with pre-trained transformers li...
25. Gensim
Gensim is a Python library for topic modeling, document indexing, and similarity retrieval with large corpora. Its principle usership is in the information retrieval and natural language processing communities. Features: 1. All algorithms are memory independent so Gensim can process input larger than RAM. 2. Intuitive interfaces 3. It facilitates the implementation of commonly used algorithms, including Latent Dirichlet Allocation, Random Projections, online Latent Semantic Analysis, and word...
Selecting the right Python library for your data science, machine learning, or natural language processing tasks is a crucial decision that can significantly impact the success of your projects. With a vast array of libraries available, it's essential to consider various factors to make an informed choice. Here are key considerations to guide you:
- 1 min
- Python Libraries for Data Processing and Modeling.
- 1. Pandas. Pandas is one of the best libraries for Python, which is a free software library for data analysis and data handling. It was created as a community library project and was initially released around 2008.
- 2. NumPy. NumPy is a free Python software library for numerical computing on data that can be in the form of large arrays and multi-dimensional matrices.
- 3. SciPy. SciPy is a free software library for scientific computing and technical computing of data. It was created as a community library project and was initially released around 2001.
Learn about the 4 most essential Python libraries for AI. Plus: How to choose between TensorFlow and PyTorch.
6 days ago · 1. Numpy. NumPy is a very popular python library for large multi-dimensional array and matrix processing, with the help of a large collection of high-level mathematical functions. It is very useful for fundamental scientific computations in Machine Learning.
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Mar 7, 2022 · The Best Python Libraries for Machine Learning. When it comes to machine learning and deep learning projects written in Python, there are thousands of libraries to pick and choose from. However, they’re not all on the same level of code quality, diversity, or size.
