We are living in an era where machine learning and artificial intelligence applications are popular even among the hardly literate people. Every company is going to be a tech company very soon. Artificial Intelligence is going to be the next digital frontier that finds profound implications across businesses and societies.

Earlier, a handful of computer systems were found in non-tech industries. Now is the era where every company is working on data, becoming tech companies. Artificial Intelligence and Machine Learning techniques are used in almost every industry, including Banking, Financial Services, Investments, pharmaceuticals and healthcare, manufacturing, logistics and transportation, media and entertainment, software products, information technology, and almost every sector.

Below mentioned are some quick and powerful stats about machine learning such that you can find taking a Machine learning Bootcamp one of the smartest moves to make a career in this domain.



Quick Stats:

● According to Fortune Business Insights, the AI market size worldwide is expected to reach USD 202.57 billion by the year 2026

● A report by Grandview Research states that the global AI market is expected to grow at an annual growth rate of around a whopping 40.2%

● Now comes a report by Forbes which states that around 58 million jobs will be created across the world in the field of Artificial Intelligence and Machine Learning

● Machine Learning Engineers are in demand across many big names, including Adobe, Amazon, IBM, Microsoft, Google, and more, according to Indeed.com

● The average annual salary of a Machine Learning Engineer ranges between USD 79k and USD 163k, according to Glassdoor

These powerful stats make it clear that Artificial Intelligence and Machine Learning are an ever-evolving and ever-growing domain and have a plethora of opportunities for professionals who are interested in this field.

Let us now discuss some of the popular Machine Learning frameworks that you should know in order to excel in this domain.

What is a Machine Learning Framework?
A Machine Learning Framework is a library, tool, or interface that enables a developer to develop AI and ML models easily without diving deep into the working of underlying algorithms. These frameworks are typically used in domains related to natural language processing, computer vision, and time-series predictions.

To infuse AI into applications, developers choose machine learning frameworks for cloud-based APIs as well as libraries and pre-trained models. A Machine Learning framework is basically used to train ML models.

Inference, the process of using trained models to perform classification and predictions for live data, is simplified by utilizing Machine Learning frameworks.

Let’s discuss some of the popular machine learning frameworks you should know.
Must-Know Machine Learning Frameworks

● Sci-kit Learn
This is an open-source scientific toolkit, one of the oldest machine learning frameworks available as a Python library. Sci-kit learn is ideal for beginners who wish to learn the basics of Machine Learning. It allows you to easily implement well-known algorithms, including logistic regression, linear regression, decision trees, random forest, and support vector machine.

Scikit-learn supports both supervised and unsupervised learning algorithms. It is usually used for training models based on structured data that are usually represented in the tabular form.

● TensorFlow
TensorFlow is one of the most popular machine learning frameworks that researchers and developers use worldwide. TensorFlow by Google is an open-source software library meant for numerical computations using data flow graphs. The best part of TensorFlow is that models trained by this library can be deployed on browsers, desktops, edge computing devices, and even microcontrollers. This support makes TensorFlow production-ready and unique.

With end-to-end capabilities, TensorFlow is a robust platform when you are required to build natural language processing, time-series, or computer vision models.

● Apache MXNet
It is popular among developers for its support for different coding languages, including Python, Java, C++, Matlab, Julia, JavaScript, R, Go, Scala, etc. For Python developers with wide-ranging requirements and different experience levels, MXNet provides a flexible and comprehensive API.

● Caffe
Another popular machine learning framework, Caffe, is made with speed, articulation, and measured quality. This framework is a BSD authorized C++ framework which has a Python interface. It is developed by BVLC (Berkley Vision and Learning Center) with network donors.

● Google Cloud ML Engine
This is specifically meant to help data scientists and developers develop and run top machine learning models in production. It offers prediction and training services that can be used individually or together. Businesses widely use it to solve issues such as clouds in satellite images, ensuring food safety, responding quickly to customer emails, and more.

● Spark Machine Learning Library
The main objective of this framework is to make hands-on machine learning easy and scalable. Spark comprises common utilities and learning algorithms, clustering, regression, dimensionality reduction, collaborative filtering, high-level pipeline APIs, and low-level optimization.

● Open Neural Network Exchange (ONNX)
This project was initiated by Facebook, AWS, and Microsoft in 2017 and was regarded as a graduate project in LFAI or Linux Foundation AI. ONNX allows you to introduce interoperability to trained models with different deep learning frameworks. ONNX is available as an open-source Python library that makes it easy to export models from one format to another.

● PyTorch
PyTorch is an open-source machine learning framework that is meant to be modular and flexible for research, including support and stability for production deployment. PyTorch is well-known for simplifying the process of implementing a neural network, making it an intuitive experience.

PyTorch is based on Torch and is used for natural language processing and computer vision applications.

Conclusion
You have now come across some of the popular Machine Learning frameworks that are must-know in order to build robust and versatile machine learning models. To get involved in a Machine Learning career, you can take an online training course.

Many online training resources, such as that conducted by Simplilearn, offer you to learn at your own pace and go hands-on with all the essential tools required to build ML models by making you work on real-life projects.

Enroll Now!!