Machine learning is a type of artificial intelligence. Using data and algorithms, machine learning allows systems to imitate the way humans learn without specific programming. There’s a wide range of applications including internet search engines, email filters, personal recommendations on websites, and much more! We’ve only just scratched the surface of what machine learning can accomplish, but it’s already an important part of companies like Google and Facebook. Machine learning engineers are in high demand, so how long does it take to learn the skills necessary to succeed in this field?
If you’re starting from scratch, it can take a few years to develop a strong data science foundation. For those with established backgrounds in data or computer science, you can reach intermediate machine learning skills in under a year. With another 6+ months of practice, you can become a machine learning expert.
What is machine learning?
As we mentioned, machine learning is a type of artificial intelligence. The goal is for computers to learn on their own without human involvement. You start with data or observations. Machine learning is only as good as the data. For the best results, systems need huge amounts of data to work with. Machine learning also trains algorithms to look for patterns and trends, so the systems can give better predictions and insights.
There are three types of learning algorithms: supervised, unsupervised, and reinforcement. With supervised learning, the system gets fed labeled datasets during its training. Labeled test data is then presented to the system, but the algorithm doesn’t know the labels. This tests the accuracy of the algorithm. Supervised algorithms tend to only work well if there are lots of correctly-labeled data and you know what you’re looking for in the data. For unsupervised algorithms, the training data isn’t labeled. The algorithm works freely and can find patterns humans weren’t aware of. It’s best applied when you don’t have data on any desired outcomes, like for recommender systems and targeted marketing campaigns. Reinforcement learning trains models to make a series of decisions in service of a goal. The environment the system works in is uncertain and complex, so the system uses trial-and-error to identify a solution. Reinforcement learning could be applied to self-driving cars, industrial automation, and healthcare.
How long it takes to develop beginner skills in machine learning – building a data science foundation, understanding programming languages, understanding databases
If you already have a background in data science or computer programming, you can start learning machine learning right away. If you don’t, it can take around 6-9 months (with 6-7 hours of work per day) to learn the basics of data science, according to Data Science Nerd. Unless you’re in school for data science, most people don’t have that many hours a day to study, so expect it to take longer. A bachelor’s degree in data science takes the usual four years while a master’s degree (which isn’t always required to be a machine learning professional, but it’s useful) takes two years. Most machine learning engineers have at least a bachelor’s in data or computer science.
Before starting machine learning, you’ll need to build a strong foundation and learn about concepts like programming languages (Python is very popular for machine learning engineers), analytical tools like SAS and R, unstructured data, and SQL databases. With a data science background, you’ll be familiar with the concepts required to start machine learning, like programming, statistics, probability, and other mathematics.
How long it takes to reach intermediate machine learning skills – understanding machine learning fundamentals and becoming more familiar with data types, models, etc
When you come from a data science background, you have a leg up on machine learning and can quickly develop intermediate skills. With consistent practice, leveling up can take as little as six months. You’ll become very familiar with the fundamentals of machine learning, like data types, larger databases, analysis, deep learning, AI, and will learn how to apply them.
There are countless machine learning courses available online that can help you develop intermediate skills. Boot camps are also an option, though bear in mind that they’re intense and require true commitment. Boot camps aim to teach practical skills very quickly, so a person can get a job by the end of the curriculum. The Springboard boot camp, which the Neptune.ai blog describes, takes 6 months with 15-20 hours of study per week. During the camp, participants learn to clean and transform data, design a machine learning/deep learning system, and deploy a running application.
How long it takes to develop advanced machine learning skills – creating software and models, managing large databases, and working with deep learning, AI, and algorithms
Once you have a solid base in data science and intermediate machine learning, it can take less than a year to become an expert. In a Quora answer on how long it takes to become a machine learning expert, Sairam Uppugundla (Founder and CEO, Codegnan IT Solutions OPC Pvt Ltd) suggested about six months. It depends on how many hours you can practice each day. At the advanced level, machine learning engineers know how to develop and improve software, create automation tools and learning models, and manage large databases and big data tools. They also have lots of project experience and a deep understanding of how AI and algorithms work.
We can also get a clearer idea of what’s considered “advanced” by looking at courses on advanced machine learning. Coursera hosts an advanced machine learning and signal processing course from IBM where students use a real-life example from IoT, learn the fundamentals of Linear Algebra, and work on a self-created dataset. The course is part of a 4-month advanced data science specialization but takes about 28 hours on its own.
Why should you learn machine learning?
Machine learning is only getting more and more exciting as the world gets more clarity on its potential. Here are three reasons why you should learn machine learning:
#1. The applications of machine learning can improve society
Most people want careers that positively impact the world. Thanks to the many applications of machine learning, workers in this field can help improve healthcare, make self-driving cars safer, improve environmental protections, and much more.
#2. It’s a growing industry
According to the Bureau of Labor Statistics, jobs in computer and information science in the United States are projected to increase by 22% between 2020 and global trends also project a sunny outlook for machine learning jobs. As more companies identify all the ways machine learning can help their business, they’ll be on the lookout for machine learning experts.
#3. Machine learning engineers earn excellent salaries
Machine learning is a lucrative field. According to Indeed.com, the average base salary for a machine learning engineer is $120,549/year. The page also lists great benefits like flexible schedules, dental and vision insurance, stock options, and profit-sharing. Even for entry-level engineers (0-4 years of experience), the average salary is still an impressive $97,090, according to a 2019 Springboard article by Andrew Zola.
What skills do you need to learn machine learning?
There’s a suite of skills necessary for machine learning, but here are three of the most important:
Skill #1: Data science
Data science is the foundation for machine learning. You need a strong understanding of programming languages, data modeling, statistics, and so on. You’ll also need good software engineering skills and an understanding of how computer architecture works.
Skill #2: Problem-solving
A lot of machine learning is based on trial-and-error and experimentation. To excel in this field, you need to love the thrill of solving puzzles and problems. People who are easily frustrated are unlikely to enjoy the challenges of machine learning.
Skill #3. Good communication
Good communication skills are essential for every job, but they’re very important for machine learning as you work on complex problems with data scientists, product teams, and other engineers.
Tools used by machine learning engineers
To learn machine learning, you need access to a variety of systems, frameworks, and hardware. We can’t list them all, but here’s a brief list to give you an idea of what you’ll need:
Tool #1: Hardware
What hardware works best depends on your goals. You may only need a CPU (a computer with a central processing unit), but for larger projects, a more powerful GPU is better. Writing for Einfochips.com, Himanshu Singh says a laptop with a high-end graphics card should work.
Tool #2: Software
There are lots of open-source software tools available for people to practice with, like Weka, which is a collection of data preprocessing and modeling techniques. It’s known for being good for beginners. Machine Learning Mastery recommends it, along with the Python Ecosystem for intermediate engineers and the R Platform for advanced engineers.
Tool #3: Tutorials/courses
The only way to learn machine learning is to do it, but it can be confusing when you’re just starting. Tutorials and courses can be extremely helpful as you’re building your skills and familiarity with systems and processes.
How to learn machine learning
What your learning process looks like depends on your background. If you’re starting with no knowledge or experience in data science, it could take a while until you feel comfortable diving into machine learning. If you have a solid foundation already and practice a lot, it’s possible to become a machine learning expert in less than two years. Because machine learning is a rapidly evolving field, there are always new and interesting things to discover. Experience is essential to success, so work on projects as often as you can to strengthen your skills. Courses and boot camps are great for building knowledge and experience – especially if you’re unsure where to start – but you can work on your own if you want. The only way to learn is to practice, so be ready to take the time you need!