Artificial Intelligence is an astonishing technology if you use it correctly. How fascinating it would be to build a machine that behaves like a human being to a great extent. Mastering machine learning tools will let you play with the data, train your models, discover new methods, and create your algorithms.
Artificial Intelligence comes with an extensive collection of AI tools, platforms, and software. Moreover, AI technology is evolving continuously. Out of a pile of AIlearning tools, you need to choose any of them to gain expertise. This article has a list of top 10AI tools that are widely used by experts.
Scikit-learn was initially developed by David Cournapeau in 2007 as a Google Summer of Code project. Later MatthieuBrucher joined the project and began to use it as apart of his thesis work. In 2010 INRIA got involved and therefore first public release (v0.1 beta) was published in late January 2010. Scikit-Learn is an open-source package in AI. It also provides a unified platform for users. This platform helps in regression, classification, and clustering. Dimensionality reduction and pre-processing are also done using Scikit-learn. It is built on top of three main libraries. The NumPy, SciPy, and MatplotLib. This AI tool helps in training and testing your models as well.
KNIME is an open-source AI tool and it is GUI based. In 2006 the first version of KNIME was introduced and several pharmaceutical companies start using KNIME and many life science software vendors began integrating their tools into KNIME. It doesn’t require prior coding knowledge. You can still perform operations using the facilities provided by KNIME. KNIME is usually used for data-related operations.
These include data mining, manipulation, etc. KNIME processes data by creating various workflows and executing them. It has repositories that consist of many nodes. These nodes are then dragged into the KNIME portal. Then a workflow of nodes is created and executed.
TensorFlow is an open-source software library for machine learning developed by the Google Brain Team for various sorts of perceptual and language understanding tasks, and to conduct sophisticated research on machine learning and deep neural networks. It is Google Brain’s second-generation machine learning system and can run on multiple CPUs and GPUs. TensorFlow is deployed in various products of Google like speech recognition, Gmail, Google Photos, and even Search.
TensorFlow performs numerical computations using data flow graphs. These elaborate the mathematical computations with a directed graph of nodes and edges. Nodes implement mathematical operations and also can represent endpoints to feed in data, obtrude results, or read/write persistent variables. Edges describe the input/output relationships between nodes. Data edges carry dynamically-sized multi-dimensional data arrays or tensors.
4. Waikato Environment for Knowledge Analysis (Weka)
Waikato Environment for Knowledge Analysis (Weka), initially developed at the University Of Waikato, New Zealand. It’s free software licensed under the GNU General Public License, and is companion software to the book “Data Mining: Practical Machine Learning Tools and Techniques”. Weka feed tools for data pre-processing, implementation of several Machine Learning algorithms, and visualization tools also so that you can develop machine learning techniques and apply them to real-world data mining problems
PyTorch is open-source machine learning, library based on the Torch library, used for applications like computer vision and natural language processing, primarily developed by Facebook’s AI Research lab (FAIR).Its Deep Learning framework. It makes very strong use of GPU. This makes it very fast and flexible to use. Its useful because it is used in very important aspects of ML. Like, tensor calculations and building deep neural networks. Pytorch is completely Python-based and is a great substitute for NumPy. It has a great future as it is still a young player in the industry.
RapidMiner is a data science software platform developed by the RapidMiner Company that provides an integrated environment for data preparation, machine learning, text mining, deep learning, and predictive analytics. It is for business, commercial applications, and also for research, education, training, rapid prototyping, and application development and supports all steps of the machine learning process including data preparation, results in visualization, model validation, and optimization.
It is an amazing interface and is quite helpful for non-programmers. This tool works on cross-platform operating systems. It is usually used in corporates and industries for quick testing of data and models. The rapid miner interface provides a user-friendly platform. In this, you can put your data and test your model. You only have to drag and drop the item in the interface. That’s the reason many non-programmers use it.
Google Cloud AutoML
In April 2008, Google announced App Engine. It is a platform for developing and hosting web applications in Google-managed data centers, from the company it was the first cloud computing service. The service became available to the public in November 2011. Since the announcement of the App Engine, Google also added multiple cloud services to the platform. Google Cloud Platform provides infrastructure as a service, platform as a service, and also serverless computing environments.
The basic concept of cloud autoML is to make AI accessible to everyone. It’s used for businesses as well. Cloud AutoML provides pre-trained models for creating various services. These services cover everything from speech, text recognition, etc. Google Cloud AutoML at the moment is starting to become popular among companies. It is very difficult to spread AI in every field. This is because every sector doesn’t have skilled people in AI/ML. So, Google has created the Cloud AutoML platform which provides pre-trained models. This is a great step by Google. The reason being, it helps users from all backgrounds to create and test their data
Azure Machine Learning Studio
Azure was announced in October 2008, started with the codename “Project Red Dog”, and released on February 1, 2010, as “Windows Azure” before being renamed “Microsoft Azure” on March 25, 2014. It provides a drag and drops option to the users. This is a very useful and easy method to form connections of datasets and modules. Azure also has the aim of providing AI facilities to all people. It works both on CPU and GPU. This Machine Learning tool is not as popular because of Google. But it is still a useful tool.
Accord.net is a computational framework of ML. It usually consists of audio and image packages. These packages help in training models and to create applications. These applications include computer vision, audition, etc. Since it is .Net, the base of the library is c# language. It helps in classification, regression, etc. Accord has special libraries for audio and images. These are very helpful for testing and manipulating audio files etc.
Colab or Colab notebook is an environment that is now provided by Google. This environment is based on Jupyter Notebook. It is one of the most efficient platforms for ML in the market. The only thing is everything in Colab will be cloud-based. You can work with many tools like TensorFlow, Pytorch, and Keras on the Colab. Colab can improve your Python skills. We can also use a free GPU provided by Colab for extra processing. Google Drive is a storage method here.
So, these were some of the most popular and widely used machine learning tools. All these show how advanced machine learning is. All these tools use different programming languages and run on them. For example, some of them run on Python, some on C++, and some on Java.