The importance of Machine Learning in the digital era cannot be stressed enough. This new advancement in technology is changing the way businesses function and it is said to dominate in the future as well. Due to its increasing importance, every technology like converting image to video using chatgpt enthusiast is gung-ho on learning Machine Learning but mastering this technology requires the knowledge of Machine Learning tools that help developers to train the data the way they like and create awesome algorithms. Since, a myriad of Machine Learning tools are available in the industry today, choosing the right one becomes difficult. But, there’s nothing to worry about as in this blog, we will be presenting you with the most popular machine learning tools that will make life a lot simpler.
Machine Learning – What is it?
Machine Learning is that sub-field of data science and Artificial Intelligence that deals with the usage of data and algorithms to imitate human learning methods to create a more accurate system of learning. A few of the most popular use cases of Machine Learning are recommendation engines, fraud detection, and business process automation.
Machine Learning Tools – Why are they important?
As discussed above, Machine Learning uses algorithms, but how are these algorithms created? This is where Machine Learning tools come into the picture. Algorithms become more intelligent over time as more and more data are fed. They make the software more accurate in making predictions without human intervention. There are three types of machine learning tools depending on the degree of human intervention needed – supervised algorithms, unsupervised algorithms, and semi-supervised algorithms.
Top Things to look for while choosing a Machine Learning Tool
Here are the top things you should look for while choosing a machine-learning tool :
- Automated CapabilitiesData Modelling Capabilities
- Data processing
- Model Training Capabilities
- Model Deployment
- Data Visualization Features
In other words, it is important to determine your needs before choosing a machine learning tool.
7 Most Popular Machine Learning Tools
- ML Notify : One of the most popular machine learning tools is ML Notify which is an open-source tool that sends notifications to you via mobile, email, and web, after the completion of the training and that too with a single import line. It offers support for major Machine Learning frameworks such as LightGBM, Keras, and TensorFlow. What more? It is a safe tool to use as there is no leakage of sensitive data. All that is needed to get started with MLNotify is scanning the QR, copying the URL, or visiting https://mlnotify.aporia.com to enter the code.
- Koalas : The Koalas project was introduced to increase the productivity of data scientists especially while handling massive sets of data. All it does is integrate the panda’s data frame API on top of Apache Spark. The former refers to the de-facto standard (single-node) Python Data Frame implementation while the latter refers to the de-facto standard for data processing on a large scale. It allows you to have a single codebase that works well with pandas (tests, smaller datasets) as well as Spark (distributed datasets).
- Hermione : Data scientists are always on the look out for easier and faster ways to implement better-ordered scripts. Hermione is the newest and one of the most popular machine learning tools that allows data scientists to do just that. It is an open-source library that offers classes in activities that are used daily such as text vectoring and data view. Hermione requires you to follow a procedure and single-handedly bears the responsibility for taking care of the rest.
- Hydra : Creating complicated apps for research purposes has never been easier than using Hydra, which is another open-source Python framework and the next on our list of the most popular machine learning tools. Hydra was named so based on its ability to perform multiple similar jobs just like a Hydra which has many heads. It can create a hierarchical configuration from various sources. Using Hydra, the configuration can be overridden from the line of command. It also allows you to run your application either locally or remotely.
- Amazon Web Services : The AWS platform allows developers to utilize various Machine Learning tools and technologies. Amazon SageMaker particularly lets you construct, train, and deploy machine learning models with a lot of ease. It also allows you to create unique models that support other open-source Machine Learning platforms.
- IBM Watson : Another of the most popular machine learning tools is IBM’S Watson which offers various capabilities for research purposes. With Watson’s framework for setting up chatbots, you can train your chatbots at a greater speed and make them more efficient.
- Colab : If you deal with Python a lot, Collaboratory or Collab is the go-to machine learning tool for you. It allows you to write and run Python codes super-fast and that too in a separate browser. You are not bogged down with configuration requirements when you are using and can share results easily and quickly.
Machine Learning is not just a buzzword these days but a revolutionary technology that makes machines perform activities just like a human being but at a greater scale and a faster rate. Machines can even make the lives of humans easier, thanks to the advancements in the field of Machine Learning.
The above Machine Learning tools have made machine learning a fun concept while allowing data scientists to save valuable time while writing codes. Be it Hydra or Hermione, Koalas or even MLNotify, Machine Learning tools have made can improve the ability of machines to learn without human intervention.
These tools have the capability of eliminating several pressing problems and also opening new doors of opportunities. The demand for Machine Learning scientists and data scientists is increasing by the day and will create a pool of Machine Learning experts. Therefore, it is a chance for other aspiring machine learning scientists to be part of something massive and empowering. These machine learning tools thus, need to be learned in depth to unleash their true potential.