Machine Learning is one of the key skill that a data scientist must possess. In order to be well versed with machine learning, a data scientist must be able to express his/her statistical learning through various tools.
1. Scikit-learn
It is probably the most popular and easy to implement machine learning library. It is written in Python and provides a wide array of tools like classification, clustering, regression analysis, etc. Scikit-learn offers simple tools for data-mining and analysis of data. It is open-source and runs on top of Scipy, Numpy, and Matplotlib.
Scikit-learn was initially envisioned at the google summer of code in 2007 by the French Computer Scientist David Cournapeau. You can also use its advanced features like Ensemble Learning, Boosting, Dimensionality Reduction, and Parameter Tuning.
2. NLTK
It is an open-source machine learning library that is for the purpose of Natural Language Processing. NLTK stands for Natural Language Tool Kit. It provides various symbolic and statistical tools for NLP. NLTK provides a variety of operations like stemming, lemmatization, tokenization, punctuation, character count, word count etc.
Furthermore, NLTK provides an interface to over 50 corpora, that allows the users to access text corpus. Gutenberg corpus is the most popular one in NLTK. This corpus consists of over 25,000 free books that can be analyzed. The authors of NLTK have also written a book that provides an in-depth overview of the library.
3. PyTorch
Pytorch is an open deep-learning framework that was developed by AI. It offers two main important features like tensors and deep neural networks.
PyTorch is most famous for research and prototyping. It is being popularly used for high-end research purposes as well as building software pipelines. Uber’s probabilistic programming language software called “Pyro” uses the PyTorch framework. For users whose language of preference is Python will enjoy using PyTorch. It also provides dynamic graph building capabilities to its users. PyTorch also gives your code the ability of data parallelism.
4. Keras
Keras is a powerful API that is used for building powerful neural networks. It is capable of running on top of TensorFlow, CNTK or Theano. Using Keras, you can perform dynamic prototyping. It is also easy to learn that supports convolutional neural networks and recurrent neural networks.
Furthermore, Keras is capable of running on top of the GPU and CPU. Keras is easy to implement and provides a readable code for the users. With Keras, you can develop models, define layers and set up input-output functions. Keras uses TensorFlow in its backend. By backend, we mean that Keras performs tensor products, convolutions and other low-level computations using TensorFlow or Theano.
5. Apache Spark
Apache Spark is an open-source Big Data Platform. It provides data parallelism and extensive support for fault-tolerance. It is an improvement over the older big data platform like Hadoop because it provides real-time data streaming capability. Furthermore, Spark provides various data processing tools like Machine Learning.
Spark is a comprehensive Data Science tool because it not only provides you with the ability to apply machine learning algorithms to the data but also provides you with the ability to handle the colossal amount of Big Data. It is popular for its lightning fast-computational technology. Apache Spark the most in-demand skill in IT technology. So, we recommend you to enroll in one of our courses offered Big data Hadoop with Spark designed by Data Brio Academy to get a clear insight of Apache Spark.
6. SAS
It is a stable, trusted and an efficient statistical analysis tool offered by the SAS Institute. SAS stands for Statistical Analysis System. It provides a wide range of tools for advanced analytics, multivariate analysis, business intelligence as well as predictive analytics.
There are various components of SAS and the results can be published in the form of HTML, PDF, and Excel. SAS provides an extensive GUI to deploy machine learning algorithms and also accelerate the iterative process of machine learning.
7. Numpy
Numpy is the building block of the many machine learning libraries like TensorFlow, PyTorch, and Keras. In order to learn Machine Learning and implement your neural networks from scratch, you must possess the knowledge of Numpy. Numpy facilitates fast and efficient computation of large scale tensors and vectors.
While Python was originally not designed for numerical computing, its readability and ease of use made it an ideal choice for this field. However, being an interpreter based language, Python suffered from the problem of low-speed in its operations. Therefore, in order to mitigate this issue, Travis Oliphant introduced Numpy in 2006. Since then, it has been the backbone of many advanced machine learning libraries.
8. Mlr
Mlr is an R package that provides extensive support for a large number of classification and regression techniques. You can also perform survival analysis, clustering, cost-sensitive learning, etc. Furthermore, you can perform resampling with cross-validation and bootstrapping.
It can also be used for hyperparameter tuning and model optimization. Using mlr, you can perform quadratic discriminant analysis, logistic regression, decision trees, random forests and many more operations.
9. XGBoost
XGBoost is an R package that provides an efficient implementation of the gradient boosting algorithm. This package is most widely used by Kagglers who use XGBoost algorithm for increasing their accuracy.
10. Shogun
Shogun is a popular open-source machine learning library that is written in C++. Since it is written in C++, it offers rapid prototyping and allows you to pipeline your project in the real-world scenario. Furthermore, it provides support in R, Scala, Python, Ruby, and C#. Shogun facilitates a variety of operations in Machine Learning like classification, clustering, hidden-Markov models, linear discriminant analysis, etc.
Summary
So, these were some of the important tools that are used in Machine Learning. We went through tools and libraries of Python and R, as well as individual software suites like SAS and Shogun.
We hope that you learned about these Machine learning tools and have the required knowledge to initiate your journey into the world of Data Science and Machine Learning with Data Brio Academy.