Many people can use the perfect instrument, but only a few can design and create one to be used by the many.
We live in a privileged era of technological advancements that have made life so much easier and enjoyable. Many of these technologies that we may take for granted are powered by “Artificial Intelligence (AI).”
AI is the science behind creating systems and machines that can emulate humans in performing various tasks. Often, Machine Learning (ML) and Artificial Intelligence are used together. If the goal of AI is to try to emulate humans, then machine learning is a method by which we can train a machine to learn and recognize patterns from data. The machine is trained enough to perform specific tasks for us, with minimal human intervention. Machine Learning, hence, forms a key role in achieving AI.
Machine Learning is a topic that’s on the mind of just about every industry. The major hiring happening in all top tech companies today is in search of those specially skilled machine learning engineers who can build the perfect algorithms.
Today, machine learning capabilities are being widely applied, and it is changing the business landscape in dynamic ways. Some of the biggest corporations use machine learning, and we can be sure that its influence is only going to grow more in the coming years. Learning machine learning can position one for a variety of exciting careers in a growing number of industries.
While machine learning is becoming a more widely accepted and adapted technology, individuals who are considering their future career options are still somewhat hesitant to step into the machine learning arena.
The reason for this hesitation could be that the technology involved in machine learning is still a little elusive for many, which has those considering this career path wondering how difficult it is to master machine learning and whether it’s a career they’re well suited for.
Let’s answer these questions by taking a more in-depth look at what exactly machine learning is and what it takes to excel in the field.
What is Machine Learning?
Machine learning is the technique of creating systems that can ‘analyze’ pre-existing data, and ‘learn’ patterns and make decisions/predictions/classifications or other tasks on similar data, with minimal human intervention. An analogy can be made with how humans learn with experiences. As humans learn from the experiences of the past to make better decisions in the future, ML is the technique of training a computer to learn from historical data to perform tasks for us in a better manner in the future.
A natural comparison between machine learning and traditional programming can be made. However, machine learning differs from traditional programming in several ways. With traditional programming, a human workforce feeds data into a computer and develops a program that’s designed to transform that data into the desired output. Computer programming is a very human-involved process, and it’s limited to our capabilities of structuring and defining data.
In contrast to traditional programming, with machine learning, we feed data into the machine and based on what has been previously fed, the machine develops its logic based on the desired output. In other words, machines learn without us interfering in the process. It may seem like something that's limited to research work, but there are many machine learning applications that we encounter daily.
Machine learning is not programmed; it is taught with data.
Let's take Google, for example. They’re masters at applying machine learning to enrich their user’s search engine experience. Through machine learning, Google can better understand the intent of each of your search queries and place the most relevant results in front of you. It’s what makes Google so well-loved.
Machine Learning is used in practically every industry. It’s widely employed by social media networks to create a more personalized, enjoyable experience for social media users. But machine learning also has more practical applications. For instance, machine learning technology is being leveraged in the healthcare field to help improve patient care and help avoid lapses that occur due to human error. It allows doctors more efficiently to use diagnostic tests and equipment to detect diseases such as early-stage cancer that often go unnoticed in the beginning stages.
Machine learning is a critical component of Artificial Intelligence. We can break AI into several distinct levels. Machine learning, where machines use algorithms to learn from previous experiences and data inputs, is the base level of AI technology. Also, just as the basis of AI is machine learning, the foundation of an effective machine learning implementation is useful data, i.e., data science.
Why Learn Machine Learning?
“Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise, you’re going to be a dinosaur within three years.” - Mark Cuban
As mentioned earlier, It’s impossible to look into the future and not see AI as being a driving force behind economic growth in one capacity or another. There is a tremendous growing need for businesses in all industries to adapt to AI and machine learning, and that means they’re going to need machine learning engineers who can lead them into the future.
In 2016, machine learning was a $1.03 billion industry. By 2022, that number is expected to grow to $8.81 billion. Machine learning is moving well past being a niche area of computer science. The average person encounters some form of machine learning practically daily, often without realizing it. This alone illustrates how entrenched machine learning already is in our lives and the potential for it to enrich our lives further.
Machine learning has experienced exponential growth in recent years, and there’s currently a demand for machine learning engineers that can help companies throughout various industries identify opportunities for implementation of the technology and the most effective, profitable ways to use it. Machine learning is becoming so important that many companies are seeking to fill their range of IT positions with individuals who bring a background or experience with machine learning to the table.
Life as a Machine Learning Engineer
Careers as a machine learning engineer are quickly becoming one of the most sought-after positions in the IT field. More companies are adopting AI technologies, including machine learning, and even more plan on doing so within the next five years. This means they’re going to be looking to bring on machine learning engineers that will help them acclimate to the new technologies and integrate them more efficiently into their operations.
The life of a machine learning engineer looks similar to that of a computer programmer, except they’re focused on creating programs that provide machines with the capabilities to self-learn and act without the direction of a person or specific program. Machine learning engineers can find exciting positions in a variety of industries, many of which will enable them to have a significant contribution to how society interacts with technology and how it enhances our lives.
Considering how quickly the world of machine learning is moving, your day-to-day life as a machine learning engineer could be quite exciting. Apart from the models and algorithms you have to work on and develop as part of your role, you would be keeping in touch with some of the most exciting developments in the world of AI, such as the myriad of technologies being developed at the AI research company, OpenAI or the UK based company DeepMind Technologies (acquired by Google in 2014) that has created a neural network that learns how to play video games like humans and is also heavily involved in the development of self-driving cars.
An individual who seeks a position as a machine learning engineer has an exciting career path ahead of them. In addition to developing applications that enable machines to self-learn and perform without specific human programming, machine learning engineers can work towards a position as an architect who works to develop application prototypes.
Machine learning engineers can work in a range of professional capacities, filling positions that include:
- Machine learning engineer
- Lead machine learning engineer
- Senior machine learning engineer
- Principal machine learning engineer
- Machine learning software engineer
- Machine learning research scientist
- Data scientist positions
According to Glassdoor, the average salary for a machine learning engineer is $121, 863, with a yearly salary range spanning $84,000 to $163,000 based on experience and location.
Machine Learning Engineer Roles and Responsibilities
Now that we know what one is, what exactly does a machine learning engineer do? As mentioned earlier, machine learning engineers work with Big Data; specifically, they feed data into models, the latter which have been designed by data scientists (see where the overlap can occur?).
Additionally, machine learning engineers are responsible for taking theoretical data science models and scaling them out to production-level models so that they can handle the resulting terabytes of real-time data. They also build programs for controlling robots and computers, of course.
Ultimately, machine learning engineers develop algorithms that enable a machine to look at its programming data and identify patterns in it, thereby teaching itself how to understand commands and eventually think for itself. This is how learning is achieved.
For those who like lists, the responsibilities of a machine learning engineer are:
- Understanding and using computer science fundamentals, including data structures, algorithms, computability and complexity, and computer architecture
- Using mathematical skills for performing computations and working with the algorithms involved in this kind of programming
- Producing project outcomes and isolating issues that require resolution, to make the programs more effective
- Collaborating with the data as mentioned above engineers to build data and model pipelines
- Managing the infrastructure and data pipelines that are necessary for bringing code to production
- Demonstrating an end-to-end understanding of applications that are being created
- Building machine learning algorithms based on statistical modeling procedures, and building and maintaining scalable machine learning solutions in production
- Using data modeling and evaluation strategy to find patterns and predict unseen instances
- Applying machine learning algorithms and libraries
- Taking the lead on software engineering and software design
- Communicating and explaining complex processes to laymen
- Keeping in contact with stakeholders to analyze business problems, clarifying requirements, and then defining the needed resolution scope
- Analyzing large, complex datasets to extract insights, as well as for deciding on the appropriate techniques
- Researching and implementing best practices to improve the existing machine learning infrastructure
- Providing support to engineers and product managers in applying machine learning in the company’s products
It should also be pointed out that there are several categories of machine learning engineers. There’s the software engineer, who specializes in computer science fundamentals and programming, and software engineering and system design; the machine learning engineer, who focuses on computer science fundamentals and programming, which covers the application of machine learning algorithms and libraries; and finally the core machine learning engineer, who masters computer science fundamentals and programming and is responsible for the application of machine learning algorithms and libraries, data modeling, and evaluation.
A Career in Machine Learning
For individuals considering a career in machine learning, it’s important to understand the prerequisite knowledge and experience that will make entry into this field more accessible. It is a field that can be entered by anyone, but your starting point will weigh heavily on which path to learning you’ll find most useful.
To step into a career in machine learning, you’ll want to take a certification course that guides you through fundamental technical areas like classical machine learning, deep learning, and reinforcement learning, along with the more complex mathematical and statistical concepts that are used regularly within the field. Someone moving towards this career without a basic understanding of these fundamentals will have a more challenging time and should consider acquiring this knowledge first before participating in a certification course.
Machine learning certification courses are ideal for software engineers, data scientists, statisticians, domain experts, and those with a depth of knowledge in statistical fundamentals and advanced mathematics that includes an understanding of algorithms. When you work in this domain, you’ll spend a lot of time working with algorithms and massive amounts of data. A comfort level with these disciplines is beneficial for success in a machine learning career.
Some basic prerequisites for learning machine learning include:
- A basic understanding of the fundamentals of Python programming
- Additional programming skills in R, C++, and Octave
- Ability to grasp some advanced mathematical concepts, including linear algebra, calculus, and graph theory
- Experience in data analysis
- An understanding of data modeling
- A solid understanding of statistics and probability
- A fundamental level of data visualization skills
Many people who are considering a career in machine learning want to know if all of these prerequisites are necessary, or if they can be gained through a certification program and hands-on experience. Some of these can be acquired through certification training; however, it’s crucial to approach machine learning with a solid foundation in data, statistics, and mathematics. Linear algebra and multivariable calculus are both fundamentally important to machine learning.
The next question is which topics will be covered in a machine learning certification program? Which skills do you need to bring to the table, and which ones will be acquired in a comprehensive certification program? When completing a certification program, you should expect to cover the following topics.
- Revisiting key mathematical principles, including algorithms, to gain a mastery of concepts necessary to complete hands-on projects
- A detailed introduction of both supervised and unsupervised learning to the point that mastery of the concepts can be achieved and also preliminary understanding of deep learning and reinforcement learning
- Analysis and implementation of the statistical and heuristics components of machine learning
- Hands-on experience with multiple end-to-end projects that cover every aspect of machine learning
- Implement machine learning models that include logistic regression, K-means clustering, support vector machines, and more
- Advanced training in Python, including data science and programming with Python
- Strong written and verbal communications
Career Benefits of Completing an ML Certification Course
Completing a PG in AI Machine Learning Course positions you to step into a new and exciting role in several growing industries. It can provide you with the knowledge and skill-set you need to scale up within the company you currently work for or work towards a career as a machine learning engineer with greater than average potential.
A certification course makes it easy for individuals who already work as a data scientist or statistician to build upon their skills, boost their resumes and make them more attractive as consultants or employees in the tech industry.
In recent years, AI-related job postings have increased by well over 100% on top career sites like Indeed. Of the most in-demand AI-related careers, machine learning capabilities ranked in the top 3 of the highest sought-after skills. Within the next coming years, AI and machine learning are expected to create millions of new employment opportunities.
If you’re looking to enhance your career in IT, data science, or Python programming and enter into a new field that’s full of potential, both now and in the future, taking on the challenge of learning machine learning will get you there.
You can also take-up the AI and Machine Learning certification courses with Purdue University collaborated with IBM. This program gives you an in-depth knowledge of Python, Deep Learning with the Tensorflow, Natural Language Processing, Speech Recognition, Computer Vision, and Reinforcement Learning.
The comprehensive Post Graduate Program provides you a joint Simplilearn-Purdue certificate, and also, you become entitled to membership at Purdue University Alumni on course completion. IBM is the leading player in AI and Data Science, helping professionals with relevant industry exposure in the field of AI and Data Science, providing a globally recognized certificate, complete access to IBM Watson for hands-on learning and practice. The game-changing PGP program will help you stand in the crowd and grow your career in thriving fields like AI, machine learning, and deep learning. Our machine learning interview questions and answers will help you to prepare for the job interview.
Simplilearn is committed to helping professionals thrive in fast-growing tech-related industries. If you are on your road to learning machine learning then enroll for our Caltech Post Graduate Program In AI And Machine Learning. Get job-ready in the AI domain with Capstone projects, practical labs, live sessions, and hands-on projects.