Machine Learning (ML) is an emerging technology that companies are rapidly integrating to automate their business processes. As businesses are recognising the potential of ML, the need for skilled professionals has increased significantly. So, if you are planning to enrol in a Machine Learning master’s degree program, this is the guide for you.

Key Highlights

• Why should you pursue a Masters in Machine Learning?
• Machine Learning masters programs: A brief overview
• Masters (MS) in Machine Learning: At a glance
  • MS in Machine Learning syllabus
• MTech in Machine Learning: Key highlights
  • MTech in Machine Learning Syllabus
  • Difference between MTech and MS courses
• Machine Learning top universities
• Masters in Machine Learning requirements

Also Read: What is Machine Learning and what students must know about it

Why should you pursue a Masters in Machine Learning?

• Machine Learning is a skill of the future. So, you can experience robust career growth by pursuing higher education in this domain.
• As per the latest study, ML tops the chart as one of the most sought-after AI (Artificial Intelligence) skills with the most number of jobs.  
• Master’s program in Machine Learning prepares students for in-demand job roles, like Data Scientists, Data Engineers, Machine Learning Engineers, etc.

Machine Learning masters programs: A brief overview

Machine Learning is a branch of AI that enables computers to learn from data automatically and make predictions with minimal human intervention. The master’s programs in ML impart adequate technical knowledge and soft skills so students can tackle the challenges of the real world and thrive in this competitive industry. Classroom learning, group projects, industry internships, workshops, and guest lectures are integral to the master’s courses. The duration of the study program can be around 1-4 years. As per interests and career goals, students can pursue a Master of Technology (MTech), a Masters in Machine Learning or a Master of Science (MS/MSc) in Machine Learning. Some universities offer master’s degrees in both AI and ML. Please remember the AL-ML courses will be rigorous. However, if you strive to gain industry-specific skills, you can experience career success.

Also Read: Complete Guide To Study Abroad

Masters (MS) in Machine Learning: At a glance

This post-graduate program focuses on providing fundamental knowledge of Machine Learning, its related tools, and their domain-specific applications in solving real-world problems. Some career roles students can take up after MS, MTech or MSc in Machine Learning are Data Mining Expert, Research Scientist, Big Data Analyst, etc.

• MS in Machine Learning syllabus:

  Students have to study both core (compulsory) subjects and electives (where students choose subjects as per their learning preferences). Some core subjects are Introduction to Machine Learning, Machine Learning in Practice, Probability & Mathematical Statistics and Deep Learning. Some popular electives are Algorithms, Machine Learning for Text Mining, Machine Learning with Large Datasets and Intelligent Robotics.

MTech in Machine Learning: Key highlights

The program equips students with principles and concepts of ML algorithms and intelligent systems, the development of algorithms, their implementation by using industry-standard tools, etc. This course covers the widest range of areas, making students experts in offering new-age Machine Learning solutions.

Also Read: Master of Science in Information Systems: A descriptive guide

• MTech in Machine Learning syllabus :

  The syllabus includes core and elective subjects. Some core subjects are Machine Learning, Introduction to Statistical Methods, Artificial and Computational Intelligence, and Deep Reinforcement Learning. The popular electives are ML System Optimisation, Advanced Data Mining, and Distributed Machine Learning.

• Difference between MTech and MS courses :

  MS courses cover theoretical aspects of Machine Learning and are ideal for PhD programs or if you want to build a research career. On the other hand, MTech courses focus on technical applications and will prepare you for challenging technical roles.

Machine Learning top universities

Some of the top-ranked universities offering MS, MSc, or MTech courses in Machine Learning are as follows:

• University of Arizona, the US
• Carnegie Mellon University, the US
• Drexel University, College of Computing & Informatics, the US
• Imperial College London, the UK
• University College London, the UK
• The University of Adelaide, Australia
• The Australian National University, Australia
• University of Tubingen, Germany
• University of Waterloo, Canada
• University of Alberta, Canada

Masters in Machine Learning requirements

• A bachelor’s degree in Computer Science, Mathematics, Data Science, or a related field with a minimum of 50% from a reputed university
• Academic transcripts
• International English Language Testing System (IELTS) or Test of English as a Foreign Language (TOEFL)
• Graduate Record Examinations (GRE)/Graduate Management Admission Test (GMAT)
• Letters of Recommendation (LOR)
• Statement of Purpose (SOP)
• Work experience (if any)

Also Read: All you need to know about a master’s degree in Robotics

So, if you are planning to pursue a master’s degree in ML and are facing financial troubles, opt for an education loan. At Avanse Financial Services, we offer customised education loans with multiple benefits. Get in touch with us today to know details like documents required to avail of an education loan without collateral or with collateral, how to calculate EMI for an education loan, and more.