{"id":56831,"date":"2019-05-25T14:12:44","date_gmt":"2019-05-25T08:42:44","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=56831"},"modified":"2025-07-27T12:37:32","modified_gmt":"2025-07-27T07:07:32","slug":"machine-learning-tools","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/machine-learning-tools\/","title":{"rendered":"Upskill with Top 10 Machine Learning Tools and get Hired"},"content":{"rendered":"

Ever wondered how Gmail detects spam mail and categorizes in spam category? Have you ever tagged your friends in an image on Facebook?<\/p>\n

Facebook suggests you the name of your friends whom you want to tag. How it is possible for Facebook to recognize the faces and give suggestions? The answer is Machine Learning<\/strong>.<\/p>\n

Not only Facebook and Google, but every big and small firms are using Machine Learning and its tools.<\/p>\n

So, it becomes necessary for you, to upgrade yourself with the latest cutting-edge technologies like ML<\/strong>, AI<\/strong>, Data Science<\/strong>, and Big Data<\/strong> and to get hired<\/strong> by a renowned company<\/strong>.<\/p>\n

In Machine learning, what you need to learn is ML tools that are used by companies<\/strong> to improve their performance<\/strong>.<\/p>\n

Today, we will learn about the top 10 Machine Learning Tools that must know for cracking your next job interview. These tools help you to learn languages like Python<\/strong> and R<\/strong>. So, without wasting more time, let’s quickly jump into the pool of ML Tools.<\/p>\n

Machine Learning has impacted various industries ranging from healthcare to the finance industry through automation of tasks and forecasting. Current and potential uses of ML tools do not stop at analytical \u2013 they include such fancy features as speech recognition, predictive maintenance, and functions that tailer marketing offers to a specific client. In addition to increasing one\u2019s technical proficiency, understanding these tools equips one to deal with such issues effectively.<\/p>\n

\"Top<\/a><\/p>\n

Most Popular Machine Learning Tools<\/h3>\n

Machine Learning is one of the key skill that a data scientist must possess.<\/p>\n

In order to be well versed with machine learning, a data scientist must be able to express his\/her statistical learning through various tools.<\/p>\n

1. Scikit-learn<\/h4>\n

Scikit-learn may be a free software machine learning library for the Python programing language. It features various classification, regression<\/strong> and clustering algorithms<\/strong> including support vector machines.<\/p>\n

Scikit-learn may be a free machine learning library for Python. It is probably the most popular<\/strong> and easy<\/strong> to implement machine learning library.<\/p>\n

Scikit-learn offers simple tools for data-mining and analysis of data. It is open-source and runs on top of Scipy<\/strong>, numpy<\/strong> and matplotlib.<\/strong><\/p>\n

Scikit-learn was initially envisioned at the Google summer of code in 2007<\/strong> by the French Computer Scientist David Cournapeau. You can also use its advanced features like Ensemble Learning<\/strong>, Boosting,<\/strong> Dimensionality Reduction<\/strong> and Parameter Tuning<\/strong>.<\/p>\n

2. NLTK<\/h4>\n

It is an open-source machine learning library that is for the purpose of Natural Language Processing.<\/p>\n

NLTK stands for Natural Language Tool Kit<\/strong>. It provides various symbolic<\/strong> and statistical<\/strong> tools<\/strong> for NLP<\/strong>.<\/p>\n

NLTK provides a variety of operations like stemming<\/strong>, lemmatization, tokenization, punctuation, character count, word count<\/strong> etc. Furthermore, NLTK provides an interface to over 50 corpora, that allows the users to access text corpus<\/strong>.<\/p>\n

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.<\/p>\n

3. PyTorch<\/h4>\n

PyTorch is a Python machine learning package supported Torch, which is an open-source machine learning package supported the programing language Lua. It is an open deep-learning framework that was developed by Facebook AI. It offers two main important features like tensors and deep neural networks.<\/p>\n

PyTorch is most famous for research<\/strong> and prototyping<\/strong>. It is being popularly used for high-end research purposes as well as building software pipelines.<\/p>\n

Uber\u2019s probabilistic programming language software called \u201cPyro\u201d<\/strong> 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.<\/p>\n

PyTorch also gives your code the ability of data parallelism.<\/p>\n

4. Keras<\/h4>\n

Keras may be a minimalist Python library for deep learning. It is a powerful API that is used for building powerful neural networks. It is capable of running on top of TensorFlow, CNTK<\/strong> or Theano.<\/strong><\/p>\n

While deep neural networks are all the craze , the complexity<\/strong> of the main frameworks<\/strong> has been a barrier<\/strong> to their use for developers new machine learning.<\/p>\n

Keras is one among the leading high-level neural<\/strong> networks APIs<\/strong>.<\/p>\n

Using Keras, you can perform dynamic prototyping<\/strong>. It is also easy to learn that supports convolutional<\/strong> neural networks<\/strong> and recurrent neural networks<\/strong>. Furthermore, Keras is capable of running on top of the GPU<\/strong> and CPU<\/strong>.<\/p>\n

Keras is easy to implement and provides readable code<\/strong> for the users. With Keras, you can develop models, define layers and set up input-output functions. Keras uses TensorFlow in its backend<\/strong>.<\/p>\n

By backend, we mean that Keras performs tensor products, convolutions and other low-level computations using TensorFlow or Theano.<\/p>\n

5. Apache Spark<\/h4>\n

Apache Spark is an open-source Big Data Platform<\/strong>. It provides data parallelism<\/strong> and extensive support<\/strong> for fault tolerance. It is an improvement over the older big data platform like Hadoop because it provides real-time data<\/strong> streaming capability.<\/p>\n

Furthermore, Spark provides various data processing tools<\/strong> like Machine Learning.<\/p>\n

Spark is a comprehensive Data Science tool because it not only provides you with the ability to apply machine learning<\/strong> algorithms to the data but also provides you with the ability to handle the colossal amount<\/strong> of Big Data<\/strong>. It is popular for its lightning-fast-computational<\/strong> technology.<\/p>\n

Apache Spark is the most in-demand<\/strong> skill<\/strong> in IT technology. So, I recommend you to explore the complete Spark tutorial series designed by DataFlair to get a clear insight<\/strong> of Apache Spark.<\/p>\n

6. SAS<\/h4>\n

It is a stable<\/strong>, trusted<\/strong> and an efficient<\/strong> statistical analysis tool offered by the SAS Institute.<\/p>\n

SAS stands for Statistical Analysis System<\/strong>. It provides a wide range of tools for advanced analytics<\/strong>, multivariate analysis<\/strong>, business intelligence<\/strong> as well as predictive analytics<\/strong>.<\/p>\n

There are various components of SAS and the results can be published in the form of HTML<\/strong>, PDF<\/strong> and Excel<\/strong>. SAS provides an extensive GUI<\/strong> to deploy machine learning algorithms and also accelerate<\/strong> the iterative process<\/strong> of machine learning.<\/p>\n

7. Numpy<\/h4>\n

Numpy is the building block of the many machine learning libraries like TensorFlow<\/strong>, PyTorch<\/strong> and Keras<\/strong>.<\/p>\n

In order to learn Machine Learning and implement your neural networks<\/strong> from scratch, you must possess the knowledge of Numpy. Numpy facilitates fast<\/strong> and efficient<\/strong> computation<\/strong> of large scale tensors and vectors.<\/p>\n

While Python was originally not designed for numerical computing<\/strong>, its readability<\/strong> and ease of use<\/strong> made it an ideal choice for this field.<\/p>\n

However, being an interpreter based language, Python suffered from the problem of low-speed<\/strong> in its operations<\/strong>. Therefore, in order to mitigate<\/strong> this issue, Travis Oliphant introduced Numpy<\/strong> in 2006.<\/p>\n

Since then, it has been the backbone<\/strong> of many advanced machine learning libraries<\/strong>.<\/p>\n

8. Mlr<\/h4>\n

mlr is an R package that provides extensive support for a large number<\/strong> of classification<\/strong> and regression<\/strong> techniques. You can also perform survival analysis<\/strong>, clustering<\/strong>, cost-sensitive learning<\/strong> etc.<\/p>\n

Furthermore, you can perform resampling with cross-validation<\/strong> and bootstrapping<\/strong>. It can also be used for hyperparameter<\/strong> tuning<\/strong> and model optimization<\/strong>.<\/p>\n

Using mlr, you can perform quadratic discriminant analysis<\/strong>, logistic regression<\/strong>, decision trees<\/strong>, random forests<\/strong> and many more operations.<\/p>\n

9. XGBoost<\/h4>\n

XGBoost is an R package that provides an efficient implementation<\/strong> of the gradient boosting algorithm<\/strong>. This package is most widely used by Kagglers<\/strong> who use XGBoost<\/strong> algorithm for increasing <\/strong>their accuracy<\/strong>.<\/p>\n

10. Shogun<\/h4>\n

Shogun is a popular open-source<\/strong> machine learning library that is written in C++<\/strong>. Since it is written in C++, it offers rapid prototyping<\/strong> and allows you to pipeline<\/strong> your project in the real-world scenario<\/strong>. Furthermore, it provides support in R<\/strong>, Scala<\/strong>, Python<\/strong>, Ruby<\/strong> and C#<\/strong>.<\/p>\n

Shogun facilitates a variety of operations in Machine Learning like classification<\/strong>, clustering<\/strong>, hidden-markov models<\/strong>, linear discriminant<\/strong> analysis<\/strong> etc.<\/p>\n

Summary<\/h3>\n

Machine Learning tools make model building easy and fast. Python is the most used language, supported by tools like scikit-learn, TensorFlow, and PyTorch. These tools have pre-built functions to load data, train models, and test results. You don\u2019t need to write everything from scratch.<\/p>\n

Other tools like Jupyter Notebook let you write and test code in parts. Pandas helps with data cleaning and organizing. If you don\u2019t like coding, tools like Weka, Orange, or KNIME offer point-and-click ways to build models. You just drag data and models and see the results.<\/p>\n

I hope that you understood these Machine learning tools and have the required knowledge<\/strong> to initiate your journey into the world of Data Science and Machine Learning with DataFlair.<\/p>\n","protected":false},"excerpt":{"rendered":"

Ever wondered how Gmail detects spam mail and categorizes in spam category? Have you ever tagged your friends in an image on Facebook? Facebook suggests you the name of your friends whom you want...<\/p>\n","protected":false},"author":5,"featured_media":56854,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[36],"tags":[19873,19875,19874],"class_list":["post-56831","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-machine-learning-tools","tag-machine-learning-tools-for-beginners","tag-top-ml-tools"],"yoast_head":"\nUpskill with Top 10 Machine Learning Tools and get Hired - DataFlair<\/title>\n<meta name=\"description\" content=\"Top machine learning tools that are helping big brands to improve their performance. 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