“[M]achine learning will bring about not just a new era of civilization, but a new stage in the evolution of life on earth.”
Machine learning is a marriage between technology and science. The most revolutionary examples of evolution enable a machine to perform a set of tasks based on learned behaviour. Although teaching a machine begins with specific human intervention, with data and models called algorithms. There was a time when the prospect of computers driving the world seemed like a half-hearted sci-fi joke! However, today machine learning is one of the hottest career options. Maybe the joke wasn’t funny, but the apparent difference in the situation indeed is. We have been reading headlines about the incidents when Alexa suddenly began to laugh. Perhaps not the best case to bring up, but machine learning as a career choice is an intriguing prospect with a completely unique work structure than what we know of a typical profession to be. Becoming a machine learning professional is not as complicated as the algorithms you will be employing once you land your dream job. Let’s get started, shall we?
Is it easy to study machine learning?
Due to the tremendous demand for experts who master the art of machine learning, there is better availability of academic resources that facilitates faster skill and knowledge development. Creativity, tenacity, and experimentation are indispensable qualities for an aspirant interested in this field. Many students tend to believe that the application of intense mathematics makes machine learning a challenging subject to conquer. On the contrary, the subject does not need you to employ in-depth mathematical solutions. The harder nut to crack is about building a foundation for what tools you can leverage to obtain the solution. The skills required to learn machine learning are rather picked up by the student by the exposure to present models and algorithms. Debugging your algorithm based on two dilemmas, why it does not work, and why it does not work as well- are the two prominent cases that make the subject a bit scary for beginners. The issue can be frightening to approach, but machine learning is no longer a developing subject. Yes, perhaps debugging is still an infant sub-domain, but the resources you need to gain the skills and start into the industry are ample.
What can machine learning be used for?
Machine learning is THE big thing today, and for a good reason. As technological evolution takes a step forward, it creates opportunities for better and extensive applications. In the present times, some areas where machine learning is experiencing a rapid inclusion include- Social media, Ecommerce, Google ecosystems, Digital or web security, Virtual assistants, Self-drive cars, transportation, mapping and navigation systems, etc.
These predictions could be answering whether an animal in a photo is a kangaroo or a koala, spotting an object in the road in front of a self-driving car, whether the use of the word “book” in a sentence relates to a paperback or a hotel reservation or whether an email is a spam. The key difference from traditional computer software is that a human developer hasn’t written code that instructs the system how to tell the difference between the kangaroo and the koala. Instead, a machine-learning model has been taught how to reliably differentiate between the animals by being trained on a large amount of data. In this instance, likely a vast number of images labeled as containing a kangaroo or a koala.
What is the difference between artificial intelligence and machine learning?
For the common eye, machine learning and artificial intelligence are the two of the trendiest buzzwords that are also often used mutually with each other. However, from a more in-depth perspective of an aspirant or an expert, both terms are two different unique dimensions. Artificial intelligence is a wider abstraction that encompasses machines capable of conducting tasks in a smarter or strategic manner.
Machine learning, on the other hand, is using the application of artificial intelligence. However, it revolves more around the idea and practice of feeding smart and relevant data to machines. In a way, machine learning trains the machine to learn to do tasks in a smarter way via the use of algorithms.
What is ablation study in machine learning?
Prior to being associated with machine learning, ablation was purely a medical term used to denote the process of removing body tissue. The term was profoundly used in context to neural networks in the body. Experiments wherein regions of animal brains were extracted to study the effect of removing the same parts on the animal’s behaviour. The same idea of ablation study is used in context with machine learning to explain removing a specific part of the machinery’s artificial neural parts. Similar to the biological neural system, the complexity, and proliferation of the artificial neural systems, ANNs, in short, ablation study became an integral part of understanding and developing new models for the machine.
In simple terms, we all are aware that we used several components to build a model. Removal of some of these components from the complete structure causes an effect on the model. Learning about these effects is the coarse purpose of the ablation study. Typically, the target component is the ‘feature’ within the model, which after removal is studied on how it affects the efficiency of the model.
Does machine learning require coding?
Machine learning algorithms are implemented in code. Programmers like implementing algorithms themselves to understand how an algorithm works. However, machine learning is all about making computers perform intelligent tasks without explicitly coding them. This is achieved by feeding the computer with lots of data and letting them make accurate decisions. Solving a problem is more than an algorithm.
Should I learn machine learning or data science?
Comparatively, data science is a much broader term than machine learning. Meaning many disciplines of data science apply to machine learning. The former might or might not draw from the evolutions of mechanical process inclusive within the latter, which encompasses several subject items, including clustering and regression. There is a relatively equal amount of hype for data science, machine learning, and artificial intelligence, leaving aspirants confused about making a definite career choice. The answer to which one you should pick largely depends on your career goals. If you are more inclined towards research or the scientific aspect of a profession, data science is the best channel.
However, if you are more into the technical aspect of building better software models, contributing to the evolution of artificial intelligence, and upgrading algorithms for intelligent machines, the field of machine learning is better aligned with your skills and aspirations. We recommend analyzing your choices and knowledge of the industry before making a decision.
Should I learn maths before machine learning?
Certainly, having a strong background in mathematics will make it easier for you to understand machine learning at a conceptual level. Linear Algebra is the most critical maths in machine learning. A data set is represented as a matrix. Also, rudimentary knowledge in calculus, matrices, probabilities, statistics, etc., will be beneficial. Thorough knowledge of mathematics will equip you with the best abilities to select the right algorithms, parameters, strategies, and for identifying the fundamental trade-offs as well as the abilities to estimate the perfect intervals and certainties.
The central dilemma isn’t if you need mathematics or not. The main question is, what level of mathematics do you need? For machine learning, your interest and professional goals best describe the amount of dexterity you need for the subject. As the research in machine learning is still ongoing, having an essential hold over multidimensional concepts such as linear algebra, statistics, probabilities, complex optimizations, and other subdomains of topology, analysis, metrics are crucial.
How can I become a good machine learning engineer?
- The first step you take to become a machine learning engineer is the most crucial. You will need to choose the right courses and capstone projects to help you get a firm grip on various concepts related to machine learning.
- Understand the rudimentary concepts of Machine Learning and the very origin of it. If you want to out-master the tech, you need to go deep down to the roots before penetrating the stem & branches.
- Practice rudimentary maths for a while again. Go back to high school and see if you still remember Baye’s theorem topic from Probability.
- Learn a programming language, the best for Machine Learning is:
- Learn the Basic Algorithm & Data Structures.
- Get acquainted with Supervised Learning and Reinforced Learning.
- Make some contribution towards an open-source project, it will not only widen your approach and help you practice more, but it also will connect you with like-minded people across the ML software engineering community.
- Create your mini project addressing any problem and try using any of the mentioned algorithms for the same.
- Work with people who both know more than you and are good at explaining things.
- Be a scientist – think of what you are doing as research, and to treat the process like you would if you were in a chemistry lab. Write down your experiments, get used to disappointment, and analyze your results.
- Keep learning. Find the papers coming out of NIPS or ICML. Read following books: Kevin Murphy’s book (Machine Learning), Chris Bishop (Pattern Recognition), Daphne Koller (Probabilistic Graphical Models), David MacKay (Information Theory, Inference, and Learning Algorithms).
How can I study machine learning?
To get the basics, you can start with online courses at Udemy, Coursera, Datacamp, EdX, or Fast.ai.
The best Machine Learning courses at Australian Universities:
Machine Learning Degrees in Australia
- Bachelor of Science (Mathematics), Charles Stuart University has a general science course in which students can major in Mathematics.
- Maths and Statistics Units, The University of New England offers numerous online maths and statistics units that can be used as credit towards degrees.
- Bachelor of Science, The University of Southern Queensland offers two maths majors as part of its online.
- Bachelor of Computer Science, Deakin University’s Bachelor of Computer Science has a robust data analytics focus.
- Bachelor of Computer Science (Data Science), University of New England’s
- Bachelor of Software Engineering (Artificial Intelligence), Torrens University Australia
- Bachelor of Computer Science (Artificial Intelligence), Western Sydney University
- Bachelor of Computer Science (Data Science and Artificial Intelligence), Griffith University
- Bachelor of Computer Science (Artificial Intelligence), University of Wollongong
Machine Learning Degrees in Australia, Best Masters
- Master of Computer Science (Machine Learning and Big Data) University of Wollongong
- The two-year Master of Machine Learning and Computer Vision (MMLCV) program is the first in Australia. We provide students with specific knowledge that equips them with competitive professional and technical skills to build their career in this rapidly growing field. Canberra
- Master of Information Technology (Artificial Intelligence), The University of Melbourne
- Master of Artificial Intelligence, RMIT
- Master of Machine Learning, Australian Institute for Machine Learning.
Best six online Machine Learning Courses for 2020:
- Machine Learning course by Andrew Ng — Coursera.
- Deep Learning Specialization — Coursera.
- Machine Learning with Python — Coursera.
- Advanced Machine Learning Specialization — Coursera.
- Machine Learning — EdX.
- Introduction to Machine Learning for Coders — Fast.ai.
Is Machine Learning a good career? How much can I earn?
There is no doubt Machine Learning is an exciting field to be in. It will be lucrative and rewarding for you if you are good at mathematics, statistics, programming, and problem-solving. As a Machine Learning engineer, you will be involved with cutting edge technologies: Robotics, Self-driven cars, Personal Assistants, Healthcare, Prescriptive Information Fusion. From manufacturing to health care, every field is gradually adopting the ML techniques and tools to move to the next level and ensure error-free functioning. It’s not only intellectually stimulating, but its results also appear near magic to the public.
The field is so new; there are massive shortages in graduates educated with the relevant skills; therefore, salaries have, of course, rocketed. Within the market, machine learning shows vital signs of reaching a worth of a whopping USD 8.81 billion by the year 2022. The graph is estimated to grow by 44.1 percent, meaning the demand for machine learning professionals will get a boost by 60 percent soon.
Machine Learning in itself isn’t a career as such. It’s an area of Computer Science and a class of methods used by people in some careers, most notably in Data Science.
Machine Learning Career Paths
- Machine Learning Engineer
- Data Scientist
- NLP Scientist
- Software Developer/Engineer (AI/ML)
- Human-centered Machine Learning Designer