Machine learning (ML) emerged as one of the most exciting technologies in recent years. The applications of ML are impacting almost all the industries. And it is expected to continue this for a foreseeable future.
It would not be overstating to say that Python as a programming language became the most preferred for data processing, data visualization and ML. One of the main reasons is that Python has a huge set of libraries, modules and frameworks available for ML.
In general, ML projects are a bit different from conventional software projects. It depends on the technology stack, the skill-set, in depth-research and understanding. For building ML models, we need to choose a programming language which is flexible, easy in readability, easy to connect with databases, easy to document, and include a good number of libraries and frameworks.
Python meets almost all those criteria, and no wonder it is the most popular language among data engineers and data scientists.
Here are some of the top Python’s ML libraries to use.
Scikit-learn
Scikit-learn is one of the oldest ML python libraries, introduced in 2007. It provides a large number of ML models. It still continues to be very popular. The key benefits are that it is easy to understand, and has a fast learning curve. The packages are well designed and have greater usability. It is built on well known Python packages such as NumPy, SciPy, and Matplotlib.
Some of the key standing points are:
- Simple and efficient tools for predictive data analysis
- Accessible to everybody, and reusable in various contexts
- Built on NumPy, SciPy, and matplotlib
- Open source, commercially usable – BSD license
Although Scikit-learn provides a large number of ML models, data processing and ML model evaluations, it has a limited support for neural network based machine learning.
Keras
Keras is one of the leading deep learning libraries. It was introduced in 2015. Keras provides API functionality with capability of running on the top of TensorFlow, CNTK, or Theano. It’s very common that many kick-start with Scikit-learn and eventually graduate to Keras. Keras is great for fast experimentations and iterations for building deep learning models.
Some of the key standing points are:
- User friendliness and relatively easy and fast to do prototyping
- Both convolutional networks and recurrent networks are supported
- Can run seamlessly on GPU and CPU
- Batch normalization
- Easy to extend
- Large number of example available
Keras continues to be popular and still expected to be one of the top ML libraries to use.
Pytorch
First introduced in October 2016. Pytorch is primarily developed by Facebook AI Research lab (FAIR). However It is an open source library. Pytorch is the next level to Keras as it’s mainly production focused. A large number of software projects built on the top of PyTouch, including Uber’s Pyro HuggingFace’s Transformation and Catalyst. It provides high-level features such as Tensor computing, like NumPy, with acceleration using GPU, and deep neural networks built on a tape-based autodiff system.
Some of the key standing points are:
- Higher developer productivity
- Easy debugging
- Data parallelism
- Tensor computing with the ability for accelerated processing via GPU
- Dynamic computational graph support
Pytorch continues to keep gaining popularity, especially when considering production environments. Also Facebook baking is a big factor and we can pexpect it will stay one of the top ML libraries to use in 2020.
TensorFlow
TensorFlow is backed by the Google Brain Team. TensorFlow was first publicly released in November 2019. It has gained tremendous popularity in a very short time. Thanks to Google for giving tremendous support. TensorFlow is an open source library. It is used for numerical computation with symbolic math, and heavily used for large scale machine learning.
When talking about programming languages, TensorFlow is a mix package. It is based on Python, C++ and CUDA. So calling it purley a python library will be a bit misleading. TensorFlow uses Python as a front-end (wrapper). The execution of the application is run on high performance C++ and CUDA.
TensorFlow supports production prediction at scale, with the same models used for training. One of the great benefits of using TensorFlow is that it provides abstraction, which means developers can focus on the overall logic of the applications, instead of the algorithms.
Some of the key standing points are:
- It’s open source and free
- Natural Language Processing
- Image, Text, and Speech recognition
- Can be used as backend for other libraries such keras
- Good custom hardware support such as TPUs
- Continually adding new features
There is no doubt that TensorFlow will be one of the top used ML libraries in 2020.
Other notable ones
- Numpy
- Pandas
- NLTK
- Spark MLlib
- MXNet
There are many ML libraries that rely on NumPy, such as matplotlib, SciPy, Scikit-learn. NumPy provides various functions to deal with complex mathematical operations like linear algebra, random number generators, matrices, and n-array.
For data preparation, and analysis for basic trends and patterns Pandas is very famous. Pandas couples with other ML libraries very well. Pandas is an open-source, and offers several tools for data analysis and manipulation.
NLTK is a python library focused on natural language processing. It is oftenly one of the top choices when working with human data. It is also used for tokenization and classification of texts, recognition on voice and handwriting.
Spark MLlib is another ML library focused on ease in scalability of computation. It’s relatively simple to use and easy to setup. Several ML algorithms are supported, such as Regression, Clustering, dimensional reduction and classification.
MXNet is highly scalable and it supports quick model training. Besides Python, MXNet also supports several other languages including C++, Go, Scala and Julia. MXNet is famous for its portability and scalability. Several big names in tech such as Microsoft, Intel and Amazon AWS support MXNet.