- TensorFlow: An open-source machine learning framework developed by Google that supports both deep learning and traditional machine learning algorithms.
- PyTorch: An open-source deep learning framework known for its dynamic computational graphs and intuitive API.
- scikit-learn: A widely-used Python library that provides a comprehensive set of tools for machine learning, including classification, regression, clustering, and dimensionality reduction algorithms.
- Keras: A high-level deep learning library that runs on top of TensorFlow and allows for easy prototyping and experimentation.
- H2O.ai: An open-source platform that provides scalable machine learning and predictive analytics capabilities.
- Caffe: A deep learning framework popular for its efficiency in training convolutional neural networks (CNNs).
- Theano: A Python library that allows for efficient mathematical computations and supports deep learning algorithms.
- KNIME: An open-source platform for data analytics and machine learning that provides a wide range of tools for building ML workflows.
- BigML: A cloud-based machine learning platform that offers automated feature engineering and model selection.
- Amazon SageMaker: A fully managed service that provides a complete set of tools for building, training, and deploying machine learning models on Amazon Web Services.
- IBM Watson Studio: An integrated environment for data science and machine learning that supports various tools and frameworks.
- Google Cloud AutoML: A cloud-based platform that offers automated machine learning services, making it easier to build ML models without extensive coding.
- Orange3: An open-source data visualization and analysis tool that provides a visual programming interface for machine learning tasks.
- Weka: An open-source software suite that provides various machine learning algorithms and tools for data preprocessing and visualization.
- Azure Databricks: A cloud-based data analytics platform that combines Apache Spark with Microsoft Azure services for scalable machine learning.
- Google Cloud ML Engine: A managed service on Google Cloud Platform for training and deploying machine learning models at scale.
1. TensorFlow
TensorFlow is another framework of Python. It finds its usage in deep learning and having a knowledge of its libraries such as Keras, helps a machine learning engineer to move ahead confidently in their career.
2. Pytorch
Pytorch is a Deep Learning framework. It makes very strong use of GPU. This makes it very fast and flexible to use. Pytorch is 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.
3. Scikit-Learn
Scikit-Learn is an open-source package in ML. It also provides a unified platform for users. This platform helps in regression, classification, and clustering. Dimensionality reduction and preprocessing are also done using Scikit-learn. It is built on top of three main libraries, NumPy, SciPy, and MatplotLib. This Machine Learning tool helps in training and testing your models as well.
4. Keras
Keras is a great ML tool if you are a beginner. It is an advanced neural network API. Keras runs on top of Theano, TensorFlow, CNTK. It can create both CNN and RNN or their combination. The library is very much user-friendly and easy to use. Its design is an API especially for humans rather than machines. Keras is one of the widely used Machine Learning tools for beginners. It is also one of the best Machine Learning tools out there.
5. H2O.ai
H2O.ai is a user-friendly, accessible AI platform that was named a Visionary by Gartner in the 2020 Magic Quadrant for Data Science and Machine Learning Platforms. Fraud prevention, anomaly detection, and price optimization are some items they offer. H2O Sparkling Water integrates with Spark for users who want to make a query using Spark SQL, feed the results into H2O to build a model and make predictions, and then use the results again in Spark.
6. Caffe
Caffe is a Deep Learning library which is very much used in the industry today. It provides good expression, speed, and modularity. Caffe was created in Berkeley university. C++ is the base language for Caffe. But, the interface provided is written in Python. It is usually used in image classification and segmentation. Caffe supports GPU and CPU. Facebook has created caffe2 which also includes recurrent neural network features.
7. Theano
Theano is like TensorFlow. It helps in math-based calculations like vector and matrix evaluations. Numpy is the main base of Theano. Theano works on both CPU and GPU. Tensorflow runs on C++ and Python. But Theano is completely Python-based. It is fast and easy to execute on the system.
8. KNIME
KNIME is an open-source machine learning tool for data analytics, business intelligence, and text mining. It can be used in finance, pharmaceuticals, and CRM. It is one of the most easy to learn and install machine learning tools. The best part about KNIME is that it can integrate codes of programming languages like Python, Java, R, JavaScript, C++, etc. If you are a beginner in the field of AI and machine learning, you should definitely try your hands on this tool as its platform has been built for powerful analytics on a Graphical User Interface workflow. This implies that even if you do not have knowledge of coding, you’ll be able to derive insights using KNIME. Taking up KNIME Courses will help you learn the concept comprehensively.
9. BigML
With its goal to make machine learning easy, simple, and beautiful for all users, BigML is one of the most comprehensive machine learning tools. It offers a managed platform to create and share your datasets and models. It is a highly scalable, cloud based, easy to integrate and use tool. BigML is loaded with a wide range of machine learning features such as regression, classification, cluster analysis, topic modeling, anomaly detection, etc.
10. Amazon SageMaker
Launched in 2018, the Amazon SageMaker Ground Truth was initially built to allow users to identify raw data, add informative labels, and produce labeled synthetic data to create training datasets for machine learning models. It also offers two versions: Amazon SageMaker Ground Truth Plus and Amazon SageMaker Ground Truth.
Data labeling tools: Amazon SageMaker Ground Truth helps users build accurate training datasets for machine learning and AI models in a timely manner.
Project management and services: As a user, here you can not only improve the quality of your training datasets but also set up labeling workflows, apply ML-powered automation, choose your own data labeling workforce, and increase the visibility of data labeling operations.
11. IBM Watson Studio
IBM Watson Studio helps users to build, run and manage their machine learning models. It brings all the open-source tools like RStudio, Spark and Python together in an integrated environment. It also provides additional tools such as Spark service and data shaping facilities. In this way IBM Watson Studio provides you with all the tools that you need to solve business problems. It offers a drag and drop data prep facility, along with blending and modeling, text analytics for unstructured data and a well-documented, easy to use API.
12. Google Cloud AutoML
The basic concept of cloud autoML is to make AI accessible for everyone. It’s used for businesses as well. Cloud AutoML provides pre-trained models for creating various services. These services include 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.
13. Orange3
Orange3 is a completely free and open-source machine-learning toolkit for Python. It’s used for everything from data mining and visualization to preprocessing and modeling. It has an easy-to-use canvas interface making it a favorite of those newer to machine learning. However, it also has advanced features seasoned developers would need.
14. Weka
Weka is an open-source machine learning tool that helps in data classification, preparation, regression, clustering, visualization, and data mining. Written in Java, it supports platforms like Linux, Mac OS, Windows. It comprises a collection of algorithms for data analysis, predictive modeling, and data visualization.Thanks to its easy to understand algorithms, it is widely used for teaching and research purposes and also for industrial applications.
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