With the rise of smart product markets, the machine learning industry is on the rise. Machine learning is also now one of the most effective cost-cutting tools in almost all industries. ML libraries are available in many programming languages, but python, being the most user-friendly and easy-to-manage language and having a large developer community, is best suited for machine learning purposes, and that is why many ML libraries are written in Python. In addition, python works seamlessly with C and C ++, so libraries already written in C / C ++ can easily be extended to Python. In this guide, we will discuss the most useful and best machine learning libraries in the #Python programming language.
TensorFlow
TensorFlow is a library developed by the Google Brain team with the main goal of deep learning and neural networks. It makes it easy to distribute work across multiple CPU cores or GPU cores and can even distribute work across multiple GPUs. TensorFlow uses tensors for this purpose. Tensors can be defined as a container that can store N-dimensional data along with its linear operations. Although it is production ready and supports reinforcement learning along with neural networks, it is not commercially supported, which means that any error or defect can only be fixed with community help.
Numpy
Based on the older Numeric library, Numpy is used to process multivariate data and complex mathematical functions. Numpy is a fast computational library that can handle problems and functions ranging from basic algebra to even Fourier transforms, random modeling, and shape manipulation. This library is written in C, which gives it an advantage over the standard built-in Python sequencer. Numpy arrays are better than the pandas series in terms of indexing, and Numpy works better when the number of entries is less than 50 KB. NumPy arrays are loaded into a single CPU, which can cause slower processing compared to newer alternatives such as Tensorflow, Dask or JAX, but nevertheless, learning Numpy is very easy and it is one of the most popular libraries to enter the Machine Learning World.
Natural language toolkit (NLTK)
NLTK is a widely used library for text classification and natural language processing. It performs word construction, lemmatization, tokenization and keyword searching in documents. The library can additionally be used for sentiment analysis, understanding movie reviews, food reviews, text classifier, checking and censoring vulgarized words from comments, text mining, and many other human language operations. Its broader uses include artificial intelligence-based chatbots, which require text processing to train their identification models, as well as creating sentences important to machine-human interaction in the near future.
Pandas
The library is written in the Python.web platform and is used to process numerical data and time series. It uses frames and data series to define three-dimensional and two-dimensional data, respectively. It also provides big-data indexing options for quickly searching through large data sets. It is well known for its ability to reshape data, rotate along a user-defined axis, handle missing data, merge and combine data sets, and filter data options. Pandas is very useful and very fast with large data sets. Its speed exceeds that of Numpy when the number of records exceeds 50k. It is the best library for data cleaning because it provides interactivity like Excel and speed like Numpy. It is also one of the few ML libraries that can handle DateTime without the help of any external libraries, and with minimal Python code scanning . As we all know, the most important part of data analysis and machine learning is data cleaning, processing and analysis, in which Pandas helps very effectively.
Scikit-Learn
Scikit-learn mainly focuses on various data modeling concepts such as regression, classification, clustering, model selection, etc. D. The library is written on top of Numpy, Scipy and matplotlib. It is a commercially available open source library that is also very easy to understand. It integrates easily with other machine learning libraries such as Numpy and Pandas for analysis, and Plotly for graphing data in graphical format for visualization purposes. This library helps both with and without a teacher.
Keras
Keras provides a Python interface to the Tensorflow library, specifically focused on AI neural networks. Earlier versions also included many other server-side parts, such as Theano, the Microsoft cognitive platform, and PlaidMl. Keras contains standard blocks of commonly used neural networks as well as tools to make image and text processing faster and smoother. In addition to the standard neural network blocks, it also provides recurrent neural networks.
PyTorch
Pytorch is an ML library developed by Facebook, based on the Torch Library (an open-source ML library written in the Lua programming language). The project is written in Python Web Development , C ++ and CUDA. In addition to Python, PyTorch has extensions to both C and C++ languages. It is a competitor to Tensorflow because both of these libraries use tensors, but it is easier to learn and integrates better with Python. Although it supports NLP, the focus of the library is on developing and teaching deep learning models only.
MlPack
MlPack is basically a C ++-based ML library that has bindings to other Python languages, including R, Julia, and GO programming. It is designed to support almost all well-known machine learning algorithms and models, such as GMM, K-means, least-corner regression, linear regression, etc. and is an easy-to-use library so that even a programmer who is not familiar with programming can understand and use it without any problems. It comes under a BSD license, making it available as both open source and proprietary software as needed.
OpenCV
OpenCV is an open-source platform for computer vision and image processing. This library contains over 2,500 algorithms dedicated to computer vision and machine learning. It can track human movements, detect moving objects, extract 3D models, merge images to create high-resolution images, and explore AR capabilities. It is used in various surveillance monitoring activities by many governments, especially in China and Israel. In addition, major camera manufacturers around the world are using OpenCv to make their technology convenient and user-friendly.
Matplotlib
Matplotlib is a library used in Python to graphically represent data before moving it to data processing and training for machine learning purposes. It uses Python GUI tools to create graphs and plots using object-oriented APIs. Matplotlib also provides a MATLAB-like interface so that the user can perform tasks similar to MATLAB. This library is free and open source and has many extension interfaces that extend the matplotlib API to various other libraries.