Businesses worldwide are adopting Machine Learning (ML) technology as it gives insights into market trends and consumer behaviour, enabling companies to achieve business goals. Did you know the global market for ML is projected to reach an annual growth rate (CAGR) of 34.8% from 2023 to 2030? You can build a robust career in this arena by pursuing higher education. So, let’s understand Machine Learning better to plan your academic journey.

Key Highlights:

• What is Machine Learning
• What are the advantages of Machine Learning
• Interesting examples of ML
• What is AI and Machine Learning connection
• What is Deep Learning
• What are the advantages of deep learning
• What is Supervised Learning
• What is Unsupervised Learning
• Popular universities for Machine Learning
• The eligibility criteria for ML courses

What is Machine Learning
Machine learning is a subfield of Computer Science that enables the system to learn and function based on past data. In simple words, ML technology uses data and algorithms to imitate how humans learn from experience, becoming more accurate in predicting outcomes. The objective is to gain deep insights into the learning process of humans to develop computer models that can learn from these insights. 

• What are the advantages of Machine Learning
• Accuracy: With ML, the system can reinvent itself frequently to match the latest trend.
• Understands patterns: ML uses algorithms and statistical methods to recognise similar patterns to function accurately.
• Useful for every business: Since it can be programmed to perform various tasks, it has a wide scope and can be used in almost every sector.
• Time efficient: Since the technology can adapt to changing patterns, it comes with lesser interruptions and quicker solutions.

• Interesting examples of ML
• Facial recognition
• Email automation and spam filtering
• Mobile voice-to-text and predictive text
• Facial recognition
• Product recommendations

What is AI and Machine Learning connection
Machine Learning is a branch of Artificial Intelligence (AI). So, AI’s goal is to build smart computer models, and ML’s objective is to teach the systems to learn from patterns and provide end results.

What is Deep Learning
Deep Learning is an ML technique that teaches computer systems to achieve accuracy. This technology is efficient in classifying images, texts and sounds. The best example of Deep Learning is a driverless car which achieved accuracy after learning from millions of images and several videos.

• What are the advantages of deep learning
• Deep Learning algorithms are designed to handle large volumes of data.
• It can work based on multiple inputs, such as images, natural language processing, and computer vision.
• It can be deployed on a Cloud platform to work with large data.

What is Supervised Learning
Supervised Learning is a branch of ML that works with labelled datasets to develop algorithms to classify data. Some popular algorithms used in Supervised Learning are neural networks, logistic regression, and linear regression.

What is Unsupervised Learning
Unsupervised Learning is deployed when a function needs to look for hidden patterns in the absence of labelled datasets. The objective is to classify the data based on similarities.
Popular universities for Machine Learning

Since ML is booming, pursuing a formal education in this field can help you build a successful career. Many international varsities offer master’s level courses in Machine learning such as MS and PhD study programs. The duration of the course can be around 1-3 years, depending on the university and course. Here are some popular universities offering master’s courses in ML.

• Stanford University - the US
• University of California - the US
• Harvard University - the US
• University of Toronto - Canada
• University of Oxford - the UK
• Nanyang Technological University - Singapore
• National University of Singapore - Singapore
• University of Cambridge -the UK
• University of British Columbia - Canada
• Catholic University of Leuven - Belgium
• University of Sydney - Australia
• University of Alberta

The eligibility criteria for ML courses

• Graduation with any computer-related stream will be an added advantage. But non-IT students with a basic understanding of statistics and algebra can pursue ML courses too. But please check university-specific requirements.
• Graduate Record Examinations (GRE)
• Any of the following English proficiency tests:
• International English Language Testing System (IELTS)
• Test of English as a Foreign Language (TOEFL)
• Pearson Test of English (PTE)
• Duolingo English Test (DET)

So, if you are inspired to pursue higher education in ML to build a promising career in this area, you can take the first step by planning your academic journey. However, don’t let the financial hurdles bother you. Loans for students are a fantastic way to fund your education independently. If you have any queries related to the education loan rate of interest, application or documentation process, please remember we are just a call away.