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Tools For Building Machine Learning Models

These are the ten popular tools which can be useful for machine learning professionals or machine learning beginners.

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Tools For Building Machine Learning Models

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  1. Session 1

  2. Machine learning has emerged as a popular career option in recent years. The growing importance can be felt from the fact that the global machine learning market is expected to escalate from USD 1.41 Billion in 2017 to USD 8.81 Billion by 2022. It will be witnessing a Compound Annual Growth Rate (CAGR) of 44.1%. Technological advancement and data generation are the key drivers for the growth of machine learning. If you are planning to pursue the machine learning course, then you must also know about the different machine learning models and tools. In this blog, we are going to discuss machine learning models and tools.

  3. Top machine learning tools: 1. Microsoft Azure Machine Learning- The first tool that makes our list is Microsoft Azure Machine Learning. It is a cloud platform that allows the developer to build, develop and deploy the AI models. 2. IBM Watson- It is an IBM cloud service that uses data to put machine learning and deep learning models into production. This tool allows you to perform training and scoring. It is best suited for ML applications via API connections. 3. Google TensorFlow- Google uses this for research and production. It is an open-source library for dataflow programming. It is a machine learning framework. With the help of this tool, you have easy visualization of the neural network, thus making it a helpful tool for machine learning professionals.

  4. 4. Amazon Machine Learning- This machine learning tool by Amazon uses machine learning models and generates predictions. It includes tools like Amazon SageMaker, which makes it easy for data scientists and developers to use machine learning models. Besides, it also has an automatic data transformation tool and makes use of a machine learning tool simplified. 5. OpenNN- It is a software library for the neural network. This is written in C++. You can download the entire library from SourceForge or GitHub. 6. Scikit-Learn- The next useful tool for developers is the Scikit-Learn. It is an open-source platform. This tool helps in clustering, regression, classification, dimensionality, and pre-processing. This is built on NumPy, SciPy, and Matplotlib. You can also use this tool for testing.

  5. 7. Weka-If you are willing to use a user-friendly software, then you can consider Weka. This is an open-source software which is used in researching and teaching. You can simultaneously access other machine learning tools like R, Scikit-learn. 8. Pytorch- The nest tool that we have on our list is Pytorch. It is also an easy to use tool for deep learning. The reason it becomes an essential tool for machine learning and deep learning is that it is used for developing deep neural networks and tensor calculations. 9. RapidMiner- It works on cross-platforms and is a useful tool for the non-programmers. This tool can use its data and then test their models. This is easy to use the tool, and it becomes a boon for the non-programmers.

  6. 10. Google Cloud AutoML- The last tool that we are adding to our list is Google Cloud AutoML. It makes the Ai accessible to all. It provides pre-trained models which help the user to create different services, like speech recognition etc. These are the ten popular tools which can be useful for machine learning professionals or machine learning beginners. It is always good to have knowledge and expertise in the additional tools along with the conceptual knowledge about machine learning. It helps you in gaining insight and learning in-depth and thus helping you become an expert in machine learning. Choosing the right machine learning course also plays a key role. You should select the one that offers you both conceptual and practical knowledge. This will help you in becoming an expert in the domain of machine learning.

  7. Why is machine learning picking up pace? There are various reasons which make machine learning a popular career choice, its extensive usage, and applicability. The rising dependence on machine learning concepts and IoT are the key drivers. Also, you must know the following: • Around 75% of Netflix uses choose films recommended to them by ML algorithms • The ML market value is expected to rise to $20.83 in 2024. • The projected CAGR growth for ML is 42.8% • As per Tractica, the annual global AI software revenue will increase from $10.1 billion in 2018 to $126.0 billion by 2025.

  8. All this indicates that there is going to be a whopping increase in the market of ML and at the same time, there is going to be a higher demand for machine learning experts who can put their knowledge to use. Global Tech Council is providing certification courses in this field, allowing you to learn about Machine Learning, AI, Deep Learning and Data Science. All this is eventually going to help you become an expert in this field. To know more about the best online certificate program offered by Global Tech Council, connect with them today.

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