There is no doubt that machine learning in general, is one of the most trending topics in the tech world in the past few years. And why not? It's easy to see why with all of the really interesting use-cases they solve, like voice recognition, image recognition, and many more amazing things .
It is a well-known fact that Python is one of the best languages you can use to learn (and implement) machine learning techniques for a few reasons.
Following are a few important reasons as to why Python is popular:
- It has a huge collection of libraries.
- It is a beginner’s level programming language because of its simplicity and easiness.
- Portability is another reason for the huge popularity of Python.
- Python’s programming syntax is simple to learn and is of high level when we compare it to C, Java, and C++
For more details on python web frameworks, Check Best Python Web Frameworks To Learn In 2021.
What is machine learning?
Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a part of artificial intelligence.
It is the most algorithm-intense field in computer science. Gone are those days when people had to code all algorithms for machine learning. Thanks to Python and its libraries, modules, and frameworks, it is now more effective and easier.
It is the most algorithm-intense field in computer science. Gone are those days when people had to code all algorithms for machine learning. Thanks to Python and its libraries, modules, and frameworks, it is now more effective and easier.
Some of the best Python Libraries For Machine Learning in 2021
Well, Python has grown to become the most preferred language for machine learning algorithm implementations. Let’s have a look at the main Python libraries used for machine learning.
ML/DL involves a lot of mathematical calculations and operations, especially Matrix. To make it easy and less time consuming, python libraries for machine learning are used.
As per the report, NumPy, Pandas, and Matplotlib are the most preferred python libraries for machine learning.
Credit: Jetbrains
Now, let's check some of the best python libraries for machine learning in 2021.
Credit: Pinterest
1. Numpy
NumPy or Numerical Python is linear algebra developed in Python. A large number of developers and experts prefer it to the other Python libraries for machine learning. Because, almost all Python machine-learning packages like scipy, Mat-plotlib, Scikit-learn, etc heavily depends on this library to a reasonable extent.
Furthermore, it comes with functions for dealing with complex mathematical operations like Fourier transformation, linear algebra, random number, and features that work with matrices and n-arrays in Python. Also, this python package performs scientific computations. Thus, it is widely used in handling sound waves, images, and other binary functions.
Features:
- High-performance N-dimensional array object
- Interactive
- Multidimensional container for generic data
- Intuitive
Additional Info:
- Git Stars: 16k
- Forks: 5.2k
2. Tensorflow
TensorFlow is an end-to-end python machine learning library for performing high-end numerical computations. It is one of the most recommended Python Libraries for machine learning. It can handle deep neural networks for NLP (Natural Language Processing), recurrent neural networks, image recognition, word embedding, handwritten digit classification, and PDE (Partial Differential Equation). TensorFlow Python assures excellent architecture support to allow easy computation deployments across a wide range of platforms, including desktops, servers, and mobile devices.
One of the major benefits of TensorFlow is Abstraction for machine learning and AI projects. This feature allows the developers to focus on the comprehensive logic of the app instead of dealing with the mundane details of implementing algorithms. Besides, with this library, python developers can now effortlessly leverage AI and ML to create unique responsive applications, which respond to user inputs like facial or voice expression.
Features:
- It is optimized for speed, it makes use of techniques like XLA for quick linear algebra operations.
- Responsive Construct
- Flexible
- Easily Trainable
- Parallel Neural Network Training
- Large Community
- Open Source
Additional Info:
- Git Stars: 153k
- Forks: 83.8k
3. Pytorch
Have you ever thought about why PyTorch has become one of the popular Python libraries for machine learning in such a short time?
PyTorch is a production-ready Python machine-learning library with excellent examples, applications, and use cases supported by a strong community. This library absorbs strong GPU acceleration and enables you to apply it from applications like NLP. As it supports GPU and CPU computations, it provides you with performance optimization and scalable distributed training in research as well as production.
Deep neural networks and Tensor computation with GPU acceleration are the two high-end features of the PyTorch. It includes a machine learning compiler called Glow that boosts the performance of deep learning frameworks.
Features:
- Hybrid Front-End
- Distributed Training
- Python First
- Libraries And Tools
Additional Info:
- Git Stars: 45.6k
- Forks: 12.1 k
4. Pandas
In machine learning projects, a substantial amount of time is spent on preparing the data as well as analyzing basic trends & patterns. This is where the Python Pandas attracts machine learning experts’ attention. Python Pandas is an open-source library that comes with a wide range of tools for data manipulation & analysis. With help of this library, you can read data from a broad range of sources like SQL databases, CSV, JSON files, and Excel.
It allows you to manage complex data operations with just one or two commands. Besides, Python Pandas comes with several inbuilt methods for combining data, and grouping & filtering time-series functionality. Overall, Pandas is not just limited to handle data-related tasks; it also serves as the best starting point to create more focused and powerful data tools.
Features:
- Fast and efficient DataFrame object with the default and customized indexing.
- Tools for loading data into in-memory data objects from different file formats.
- Data alignment and integrated handling of missing data.
- Reshaping and pivoting of data sets.
Additional Info:
- Git Stars: 28.1k
- Forks: 11.7k
5. Metplotlib
As the name suggests, Matplotlib gives a MATLAB-like interface and provides the users with an excellent user experience. It uses standard GUI toolkits like Qt, wxPython, GTK+, or Tkinter to provide programmers with an object-oriented API to embed graphs and plots into their applications.
Matplotlib helps in producing high-quality 2D plotting graphs and figures in multiple formats. The types of graphs and plots include error charts, plots, histograms, scatter plots, bar charts with minimal lines of code.
Features:
- Faster Text Rendering
- Qualitative colormap
- Improved Image support
- Improved offset text choice
Additional Info:
- Git Stars: 13k
- Forks: 5.6k
6. Scipy
SciPy is one of the popular Python libraries for machine learning. It has been a favorite library among Machine Learning enthusiasts as it contains different modules for integration, optimization, linear algebra, and statistics. There is a difference between the SciPy library and the SciPy stack. The SciPy is one of the core packages that make up the SciPy stack. Besides, SciPy is also very useful for image manipulation.
Features:
Additional Info:
- Git Stars: 7.8k
- Forks: 3.5k
7. Keras
Keras is one of the coolest Python libraries for machine learning. If you are a beginner in Machine Learning then I suggest you use Keras. Besides, It provides an easier way to express Neural networks. Furthermore, It also provides some of the utilities for compiling models, processing datasets, evaluating results, visualization of graphs, and many more.
Keras internally uses either Tensorflow or Theano as a backend. Some other popular neural network frameworks like CNTK can also be used. Keras is slow when compared to other libraries because it constructs a computational graph using the backend infrastructure and then uses it to perform operations. In addition, Keras provides many preprocessed datasets and pre-trained models like Inception, SqueezeNet, Mnist, VGG, ResNet, etc.
Features:
- It runs smoothly on both CPU and GPU.
- Prelabeled Datasets
- Multiple methods for Data Preprocessing
- Model Evaluation
- Modularity
Additional Info:
- Git Stars: 50.5k
- Forks: 18.7k
8. Theano
At the core, Theano is a well-known scientific computing library that enables you to define, optimize as well as evaluate mathematical expressions, which deals with multidimensional arrays. The fundamental of several ML and AI applications is the repetitive computation of a tricky mathematical expression.
Theano allows you to make data-intensive calculations faster than when executing on your CPU alone. In addition, it is well optimized for GPUs, which offers effective symbolic differentiation and includes extensive code-testing capabilities.
When it comes to top performance, Theano is a great Python machine learning library as it includes the ability to deal with computations in large neural networks. It aims to boost the development time and execution time of ML apps, particularly in deep learning algorithms. Only one drawback of Theano in front of TensorFlow is that its syntax is quite hard for beginners.
Features:
- Tight integration with NumPy
- Transparent use of a GPU
- Efficient symbolic differentiation
- Speed and stability optimizations
- Dynamic C code generation
- Extensive unit-testing and self-verification
Additional Info:
- Git Stars: 9.3k
- Forks: 2.5k
Conclusion
Well, the aim of this article was to provide you the best Python Libraries For Machine Learning in 2021.
The python ecosystem is huge, both in terms of contribution and usage. We discussed Python libraries for machine learning used in all major areas of ML, right from the data manipulation stage to deep learning, natural language processing, and even visualization.
Also, python has a diverse set of libraries available which not only enhance its capabilities but also showcase the breadth and depth of tasks one can perform. Here we have mentioned just a few of them.
There are many more amazing libraries being used and worked upon. If you know any, do share in the comments below.