8 important things you need to know about Machine Learning
Last Updated on September 7, 2021 by iSchoolConnect
AI is the future. As more and more data gets generated everyday, the need for smart AI technologies to manage and analyse it has grown. Here is a complete guide on all you need to kickstart your career in Machine Learning.
Most industries today work with huge amounts of data. They analyze this data and make decisions based on the insights. The increase in access to technology has caused a boom in data, so much so that it has become impossible for humans to evaluate it manually. Machine Learning (ML) uses algorithms to find correlations and patterns in large data sets and present an accurate analysis.
Its applications are all around us – from our healthcare to entertainment and media and even our governance systems. Even popular applications like Snapchat, Netflix, and Tinder use Machine Learning techniques to gain insights about their users and keep them engaged. If this sounds interesting to you, read on to know more about ML and how you can pursue a career in it!
What is Machine Learning?
Machine Learning is the field of study that deals with the computers’ capability to learn without being programmed. It is essentially a subset of Artificial Intelligence and a part of Data Science.
Its primary focus is on analyzing the input data of the system and creating algorithms with it. These algorithms imitate the way humans learn and help the machine in making accurate conclusions. Consequently, this helps machines make uncover important insights and even make future predictions!
What do Machine Learning engineers do?
ML engineers work towards solving real-time challenges by designing highly functional algorithms that can extract and analyze patterns from the input data. They use this analysis to draw conclusions and make informed decisions. ML engineers usually work with a team of software engineers, product managers, and analysts.
What skills do you need as an ML engineer?
As an ML engineer, you will be required to have a working knowledge of Computer Science and related subjects. Listed below are the skills you should have to become a successful ML engineer-
- Basic knowledge about ML
- Applied Mathematics
- Computer science fundamentals
- Data Science
- Communication skills
- Problem solving skills
- Desire to learn
What subjects will you learn?
Machine learning courses are offered at various levels of specializations. Given below are few topics that are typically included across all levels-
- Programming Languages like Python, Java, C++, R, etc.
- Artificial Intelligence
- Supervised Learning
- Unsupervised Learning
- Semi-supervised Learning
- Reinforcement Learning
- Deep Learning
- Artificial Neural Network and its applications
- Application of Machine Learning techniques
These subjects will vary in difficulty across different levels of specialization. Moreover, in an innovative field like Machine Learning, you will keep learning new things as you progress in your career.
How much scope is there in Machine Learning?
Even though AI has made machines smarter, computers are still machines. For instance, a machine’s ability to extract data from an image, video, or audio still falls short in comparison to humans. These challenges, however, can be dealt with using deep learning modeling techniques – something you can learn while you study ML.
Let’s look at the key differences between Machine Learning and Deep Learning and notice how much further we have to innovate until we can entirely rely on ML for our daily tasks-
|S.No.||Machine Learning||Deep Learning|
|1.||Uses algorithms to learn from the input data and make informed decisions||Uses algorithms in layers to create an “artificial neural network” that can make decisions on its own|
|2.||Techniques can be applied to less data||Techniques can only be applied to a huge amount of data|
|3.||Output is in a tangible, numerical form||Output can be in text or sound|
|4.||Less time to train devices||More time required to train devices|
Now that you know that ML is still an emerging industry, let’s take a look at the top colleges where you can learn it.
Top Universities for Machine Learning
There are numerous courses and certifications available online. But if you have an interest in developing intelligent systems and are intrigued by ML concepts, it is best that you pursue a degree in it.
Here is a list of top Universities that offer Machine Learning courses –
- University of Oxford
- Stanford University
- University of Cambridge
- Yale University
- Imperial College London
- University College London
- Technical University in Munich
- KTH Royal Institute of Technology
- New York University
- University of Pennsylvania
Most often people choose to pursue a master’s course in Machine Learning. However, you can specialize in the subject during your undergrad as well.
Meanwhile, if you do choose to apply to any of the above-mentioned universities, take time to research the kind of syllabus they have, their application requirements, and deadlines. Choosing the right course and the right university is imperative. It may seem like a daunting task but you can always look for a good study abroad counselors to help you out.
While the application requirements of all universities may be different, there are a few common documents you will need to provide, including-
- Statement of Purpose
- Letters of Recommendation
- Proof of language proficiency (IELTS, TOEFL and PTE test scores
- GRE or GMAT scores
Job Prospects in ML
Machine Learning is one of the most sought-after career options today. According to the Emerging Jobs Report published by LinkedIn, there are approximately ten times more ML engineers than there were five years ago.
Machine Learning professionals are hired in different roles depending on the level of experience and area of expertise. A few of them are-
- ML engineer
- ML analyst
- Data Science Lead
- NLP Data Scientist, and
- ML Scientist
ML specialists earn somewhere between $68,000 to $140,000 a year, a number that can even go up to $200,000 a year, depending on your level of expertise!
Now that you know a bit about Machine Learning, do you want to learn it?
Let me know in the comment section.