Machine learning tools are algorithmic applications of artificial intelligence that enrich the systems with the ability to learn. Machine learning tools allow the software to become more accurate in calculating outcomes without being explicitly programmed. It reduces human efforts and is believed to provide appropriate results so far. Machine learning has become the center of attraction in the past few years due to its

In other words, an algorithm is used to gain data from the world, and that data is fed back into the model so that it improves with the passage of time. It’s called machine learning because the model “learns” as it is provided with more and more data on a regular basis. They can be used to detect fraud, filter spam, build news feeds, security threats, and much more.

The software industry is flooded with different machine learning tools. It becomes very difficult to pick up the best machine learning tool from the rest. But don’t worry, as we have come up with a list of the most popular machine learning tools. We have created the top tools list containing the top 18 Machine learning tools for you!

1. Scikit-learn

scikit-learn is a machine learning tool that acts as a library for the Python programming language. It helps in selecting efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction.

Key Features:

• Scikit-learn comes with inbuilt datasets like the iris dataset, house prices dataset, diabetes dataset, and much more. These datasets are easy to understand, one can directly implement ML models, and good for beginners.

• It integrates with many other Python libraries, such as matplotlib and Plotly for plotting, NumPy for array vectorization, pandas data frames, scipy, and many more.

• A Decision Tree is a powerful tool that can be used for both classification and regression problems. It uses a tree-like model to make decisions and predict the output.

Cost:

You can request a quote on their website.

2. Weka

weka is a combination of machine learning algorithms for data mining tasks. It is written in Java and was developed at the University of Waikato, New Zealand. In weka data sources, classifiers, etc. are beans and can be connected graphically.

Key Features:

• Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization, and an environment for comparing learning algorithms.

• The data can be imported from a file in various formats like ARFF, CSV, C4.5, binary. Data can also be read from a URL or from an SQL database.

• Pre-processing tools in WEKA are called “filters” and there are filters available for Discretization, normalization, resampling, attribute selection, transforming, and combining attributes.

Cost:

You can request a quote on their website.

3. Rapidminer

Rapidminer provides an open and extensible data science platform. A graphical design environment makes it simple and fast to design better models while Visual representation with Annotations facilitates collaboration among all stakeholders.

Key Features:

• It can easily work with multiple data sources: Excel, Access, Oracle, IBM DB2, Microsoft SQL, Sybase, Ingres, MySQL, Postgres, SPSS, dBase, Text files, and much more.

• It includes multiple data management methods such as data loading, data transformation, data modeling, and data visualization methods.

• Seamlessly access and use of algorithms from H2O, Weka, and other third-party libraries

Cost:

You can request a quote on their website.

4. Tensorflow

Tensorflow is an open-source library that helps to develop and train ML models. It helps the developers to create large-scale neural networks with many layers and is mainly used for Classification, Perception, Prediction, Understanding, Discovering, and Creation.

Key Features:

• You can easily run TensorFlow applications on various platforms such as Android, Cloud, IOS. It has its own designed hardware to train the neural models known as Cloud TPUs.

• It provides computational graphing methods that support an easy to execute paradigm and works with multi-dimensional arrays with the help of a data structure tensor.

• It provides room for the prediction of stocks, products, etc with the help of training using the same models and different data sets.

• TensorFlow provides a defined level of abstraction by reducing the code length and cutting the development time.

Cost:

You can request a cost on their website.

5. Shogun

Shogun is an open-source machine learning library written in C++. It comes up with algorithms and data structures for machine learning problems. Also offers a full implementation of Hidden Markov Models.

Key Features:

• Shogun is an eCommerce management software that helps businesses create and operate frontend and backend web pages across various eCommerce platforms.

• It provides a visual builder tool, which allows employees to create and edit page templates with custom images, text, colors, and themes.

• It offers a codeless page creation process that gives an amazing user experience for your customers and helps you to move faster when trying out new elements and saves you money on developer costs.

Cost:

You can request a quote on their website.

6. Apache Mahout

Apache Mahout produces free implementations of distributed or scalable machine learning algorithms focused primarily on linear algebra. It also provides Java/Scala libraries for common maths operations and primitive Java collections.

Key Features:

• Mahout offers the coder a ready-to-use framework for doing data mining tasks and lets applications analyze large sets of data effectively.

• Supports Distributed Naive Bayes and comes with distributed fitness function capabilities along with matrix and vector libraries.

Cost:

You can request a quote on their website.

7. Keras.io

Keras.io provides a Python interface for artificial neural networks and acts as an interface for the TensorFlow library. Keras contains numerous implementations of commonly used neural network building blocks such as layers, objectives, activation functions, and optimizers.

Key Features:

• It allows the use of distributed training of deep-learning models on clusters of Graphics processing units and tensor processing units.

• It is designed to enable fast experimentation with deep neural networks and focuses on being user-friendly, modular, and extensible.

Cost:

You can request a quote on their website.

8. PyTorch

PyTorch is a machine learning tool that is based on the Torch library. It is primarily developed by Facebook's AI Research lab and can be used for various applications such as natural language processing and computer vision.

Key Features:

• PyTorch provides two high-level functions such as Tensor computing (like NumPy) with strong acceleration via graphics processing units and (GPU)Deep neural networks built on a type-based automatic differentiation system.

• PyTorch uses a method called automatic differentiation which helps in building neural networks to save time on one epoch.

Cost:

You can request a quote on their website.

9. KNIME

KNIME is an open source analytics, reporting and integration platform and integrates various components for machine learning and data mining through its modular data pipelining "Building Blocks of Analytics" feature.

Key Features:

• It allows users to easily build data analysis workflows using pre-built nodes and components from the KNIME Hub.

• KNIME analytic workflows are run through the interactive user interface, enabling the data analysis process to be easily integrated into local job management and executed on a periodic basis.

Cost:

You can request a quote on their website.

10. Accord.net

Accord.net is a framework which comes up with a set of libraries that are available in source code as well as via executable installers and NuGet packages. It is a complete framework for building production-grade computer vision, computer audition and statistics applications.

Key Features:

• The framework comprises multiple libraries encompassing a wide range of scientific computing applications.

• It also offers a large number of probability distributions, hypothesis tests, kernel functions and support for most popular performance measurements techniques.

Cost:

You can request a quote on their website.

11. Google Cloud ML engine

Google Cloud ML engine provides a series of modular cloud services including computing, data storage, data analytics and machine learning. In 2008, Google announced App Engine, a platform for developing and hosting web applications in Google-managed data centers.

Key Features:

• One can use Google Cloud ML Engine just to train a complex model by leveraging the GPU and TPU infrastructure.

• It automates all resource provisioning and monitoring for running the jobs and also manages the lifecycle of deployed models and their versions.

• It can also perform hyperparameter tuning that influences the accuracy of predictions.

Cost:

You can request a quote on their website.

12. Google Colab

Google Colab allows anybody to write and execute arbitrary python code through the browser, and is especially well suited to machine learning, data analysis and education.

Key Features:

• Colab is a hosted Jupyter notebook service that requires no setup to use, while providing free access to computing resources including GPUs.

• Google Colab provides even more pre-installed machine learning libraries such as Keras, TensorFlow, and PyTorch.

• Just like collaborating on a Google Docs document, you can co-code with multiple developers using a Google Colab notebook.

Cost:

You can request a quote on their website

13. Jupyter Notebook

Jupyter notebook is a web-based interactive development environment for notebooks, code, and data. It helps in developing open-source software, open standards, and services for interactive computing across dozens of programming languages.

Key Features:

• It allows combining code, comments, media, and visualizations in documents and has quickly become one of the most popular online computational notebooks, used by top companies such as Google, Microsoft, and NASA.

• JupyterLab, which is a web-based IDE for Jupyter Notebooks, allows you to set up your user interface to support various workflows in machine learning, data science and scientific computation. You can run terminals, text editors, and code consoles in your web browser.

• It is used for data analysis , data cleaning, data transformation, machine learning and statistical modeling.

Cost:

You can request a quote on their website

14. Amazon Machine learning

Amazon Machine Learning enables developers to create, train, and deploy machine-learning (ML) models in the cloud. This machine learning tool provides a number of built-in ML algorithms that developers can train on their own data.

Key Features:

• It can be used to train and deploy machine learning models to analyze and approve automotive classified ad listings.

• Helps in developing practical on-site solutions that could address the over- and under-utilization of cars in real-time using an optimization engine built in Amazon SageMaker.

• It gives developers of all levels without any extensive machine learning background the right tools to create data driven models.

• It allows you to create a data source object from data residing in Amazon Redshift, which is the Data Warehouse Platform as a service.

Cost:

You can request a quote on their website.

15. IBM watson

IBM watson consists of a workspace that includes multiple collaboration and open-source tools for use in data science. It helps to create a project with a group of collaborators, all having access to various analytics models and using various languages.

Key Features:

• It provides access to data sets that are available through Watson Data Platform, on-premises or on the cloud

• It has resources such as articles on the latest developments from the data science world and public data sets and is available in on-premises, cloud, and desktop forms.

• It brings together staple open source tools including RStudio, Spark and Python, along with additional tools such as a managed Spark service and data shaping facilities, in a secure and governed environment.

Cost:

You can request a quote on their website.

16. Numpy

Numpy is an important library on which almost every data science or machine learning Python package such as SciPy, Mat−plotlib , Scikit-learn, depends on to a reasonable extent. It is useful for performing mathematical and logical operations on Arrays.

Key Features:

• Its functions are very useful for performing any mathematical or scientific calculation.

• It is an open-source Python library used to perform various mathematical and scientific tasks and also contains multi-dimensional arrays and matrices, along with many high-level mathematical functions that operate on these arrays and matrices.

Cost:

You can request a quote on their website.

17. Spyder

Spyder is a free and open source scientific environment written in Python, for Python. It is a combination of the advanced editing, analysis, debugging, and profiling functionality of a comprehensive development tool with the data exploration and interactive execution of a scientific package.

Key Features:

• It is an acronym for Scientific Python development environment used in machine learning projects developed in Python language.

• It is an acronym for Scientific Python development environment used in machine learning projects developed in Python language.

• It has some essential features like advanced editing, interactive testing, and debugging.

Cost:

You can request a quote on their website.

18. Mallet

Mallet is a Java based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.

Key Features:

• It has sophisticated tools for document classification: efficient routines for converting text to “features”, a wide variety of algorithms, and code for evaluating classifier performance using several commonly used metrics.

• It includes tools for sequence tagging for applications such as named-entity extraction from text.

• The MALLET topic modeling toolkit contains efficient, sampling-based implementations of Latent Dirichlet Allocation, Pachinko Allocation, and Hierarchical LDA.

Cost:

You can request a quote on their website.

Things to Consider Before Selecting Machine Learning Tools

When you have to choose between various machine learning tools , try to keep the following things in mind -

1. What are your requirements?

As a mindful buyer, always keep in mind your requirements. Before making any purchase, you should make a list of the things you want from the tools. Sometimes, due to lack of knowledge, one may end up buying unnecessary and useless tools. That’s why you should be clear about your expectations from the tool.

2. Have you done enough research?

Well, a good homework before any purchase is not a bad option. Before buying any tools, make sure that you take reviews from different platforms and people as well. Everything that glitters is not gold. It becomes very important to take reviews before making any purchase to save yourself from further regrets.

3. Is the tool good for future purposes as well?

Walking ahead of everyone is not a bad choice. A tool that promises updated service even further, should be the first choice of the buyer. In the changing times, when the technology is changing at a rapid speed, a tool that offers updated service with every change should be considered first.

Conclusion

Picking up the best machine learning tool among the rest is quite a hard task. We have put together mostly all the most demanded machine learning tools of today’s time. All the above mentioned machine learning tools are unique in their own way and provide the best service. We have highlighted all the main features of each tool. We hope you read all the descriptions carefully and choose the best for yourself.

FAQs

How machine learning tools help us in daily life?

Well, machine learning is easy to understand with the help of an example. When you watch something on Amazon prime video, you start getting recommendations of different contents on the basis of your last watched content. Mostly the recommended content is similar to your previous watched data. Machine learning tries to put forward that content which may attract you. It helps the user in picking up the best deal among the rest. Machine Learning is used to select the data which matches your choice. It helps to get access to new content without putting much effort.

Are human efforts required for effective machine learning ?

No, machine learning doesn’t require human efforts. For example, for an e-commerce website, machine learning helps to recommend products to the customers on the basis of browsing history. It can easily understand the browsing activities and purchase histories which helps machine learning to suggest the right products, deals to the users. Another example is an antivirus software which easily learns the art of detecting threats without requiring any human efforts. It is clear now that machine learning has reduced human efforts to a large extent.

What is the future of machine learning?

With the advancement of technology, the usage of machine learning is increasing to a great extent. The global machine learning market is predicted to grow from $8.43 billion to $117.19 billion in the near future. Quantum machine learning can also improve the chances of getting more accurate results and sound results. Quantum machine learning has the ability to boost the performance of machine learning in the near future.

Automated machine learning which is the process of automating the process of applying machine learning algorithms to complete real-life tasks is also becoming a trend and enjoys good attention from varieties of business around the globe. Keeping everything in mind, machine learning does have a bright future in the coming years.

Is machine learning used for operating cars?

The algorithms of machine learning help in making the self driven car a reality. The algorithms of machine learning are helping humans to bring self -driving cars into reality. The algorithms of machine learning collect data from the cameras, microphones, sensors and make appropriate decisions. No wonder, machine learning is the future of advanced transportation. Tesla is the perfect example of a self driven car whose performance depends on its machine learning algorithms. Inventors are working towards it to bring a drastic change in the transportation system.

Is machine learning linked with artificial intelligence?

Artificial intelligence and machine learning, parts of computer science, are correlated with each other. Artificial intelligence uses algorithms that can work with their own intelligence. While Machine learning is about extracting more knowledge from the data provided.

Machine learning is a part of AI which enables a machine to learn from past data without any further programming. The goal of Machine learning is to give the users more favorable results while Artificial intelligence aims at developing more advanced and user-friendly computer systems.