Computer science is a vast area. Choosing the best certification courses for computer science engineers can be a little confusing. Every year there are new digital technologies entering the computer science field. With increased competition, students wish to be upgraded instantly with certification courses available online and offline.
Among numerous technologies, machine learning is gaining more visibility. Machine learning automates data analysis, allowing computers to perform tasks without explicit programming. TechRepublic reports that ML is the most in-demand AI skill.
Machine learning is categorized into supervised, unsupervised, and reinforcement ML. Students can take up machine learning certification to become expert engineers in machine learning.
With the market expanding rapidly, there are various machine learning tools available in the market. Choosing the right tool can automate the various processes to make it more efficient. In this article, we have listed a few tools that can help you automate your work processes.
Tools for machine learning
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TensorFlow
TensorFlow is a tool that helps in training and constructing machine learning and deep learning models. Google Brain Team developed this tool and the machine learning experts use it for developing various ML applications. The tool offers a powerful library, tools, and resources for numerical computation, useful for large-scale machine learning and deep learning projects. ML developers can construct ML applications efficiently. A high-level Keras API helps beginners to easily start with TensorFlow.
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PyTorch
This is an open-source machine learning framework developed by FAIR(Facebook’s AI Research lab). It is used for developing applications including computer vision and natural language processing. This tool has Python and C++ interfaces that are quite interactive. PyTorch is helpful in developing various deep learning software such as PyTorch Lightning, Hugging Face’s Transformers, Tesla autopilot, etc. It helps developers to create neural networks using the Autograde Module.
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Amazon Machine Learning (AML)
This is a cloud-based and robust machine learning software application used for developing models and making predictions. It also helps in integrating data from multiple sources, including Redshift, Amazon S3, or RDS. It helps users identify the patterns, build mathematical models, and make predictions. Additionally, this tool also supports multi-class classification, binary classification, and regression.
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Apache Mahout
This tool is an open-source project of Apache Software Foundation comprising matrix and vector libraries and used for building ML applications based on Linear Algebra. Apache Mahout is a distributed linear algebra framework and mathematically expressive Scala DSL that helps developers to implement their own algorithms. This tool is an efficient framework for executing scalable algorithms. Apache Mahout supports multiple distributed backends and runs on top of Apache Hadoop using the MapReduce paradigm.
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Google ML Kit for Mobile
This tool is optimized for mobile app developers. The ML kit is packed under the expertise of machine learning and technology to develop and create more robust, optimized, and personalized apps. It is helpful in face detection, text recognition, landmark detection, image labeling, and barcode scanning applications. It also helps while working offline.
MACHINE LEARNING FAQ
What is the difference between AI and machine learning?
Machine learning is based on the notion that machines ought to be capable of learning and adapting to changes in their environment; AI refers to the idea that machines can perform the tasks “smartly.” Artificial Intelligence applies machine learning and deep learning methods to tackle real-world problems.
Is Python alone enough for machine learning?
Python is sufficient to be a programming language in order to enter the field of ML. But, you’ll have to master other skills like ML algorithms, databases management languages, math, and statistics to be a fully-fledged computer-learning engineer.
Is coding involved in learning machine learning?
Suppose you’re planning to move into machine learning and artificial intelligence. In that case, a little coding is required.
In the field of ML, 3 programming languages pop up frequently: C++, Java, and Python However, it’s possible to be more specific.
Do Engineers use Maching Learning?
To be a machine-learning engineer, one must possess these qualifications and certifications: Knowledge of data science. Programming languages and coding such as Python, Java, C++, C, R, and JavaScript. Experience with dealing with ML frameworks.
The programming language used for ML?
Python is the leader. It is used by about 57 percent of data scientists and computer scientists, and about 33% of them recommend the library to develop. This is not surprising considering the changes in the machine learning Python frameworks in the last two years, including the launch of TensorFlow and various libraries.
Do I need to learn HTML prior to learning Python?
Python has become the third most used development technology, behind HTML/CSS as well as JavaScript. It is recommended to know HTML prior to learning Python If you want to develop applications for the web since it is the foundational building block of websites.
Is HTML difficult to master?
Because the basic concepts are easily learned, HTML is relatively simple to master. You could master the fundamentals in less than an hour! Once you’ve learned how tags work, you can begin designing your HTML pages!
Check:
Building an Android Mobile Game with Python | Is it a Great Idea?
Final words on Machine Learning Tools You Should Know
There are various ML tools available in the market. Choosing the right one depends on the user and requirements for the project. Most of the ML tools are free while some require the upgraded version to work effectively.
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