Is Machine Learning Hard? A Guide To Getting Started
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Whenever there’s a mention of machine learning (ML) or artificial intelligence (AI), most people want to know: Is machine learning hard to learn? On the surface, it’s understandable why many people believe it to be a complicated subject to master.
For one, there’s math involved. Secondly, you need to have extensive programming knowledge and be familiar with algorithms and distributed computing. Thirdly, experimentation is a crucial aspect of machine learning, and it requires a lot of time and effort to excel.
While it’s true that you need to know some basic math—including probability, statistics, and linear algebra—have programming knowledge, and be comfortable working with algorithms, it’s not as challenging as you probably think to get into machine learning.
There are plenty of resources out there that can teach you the basics of machine learning, including books, online courses, and even tutorials on specific algorithms.
Once you have a basic understanding of machine learning, you can apply it to real-world problems.
In this guide, we’ll answer your burning question: How hard is machine learning? Plus, we’ll share some helpful resources to get started.
What Is Machine Learning?
Machine learning refers to teaching a computer to think and act like a human brain without human intervention. It’s a data analysis method that automates the process of analytical model building. Machine learning is a branch of AI that works on the principle that machine systems learn from data and make decisions based on the patterns they’ve picked up.
For instance, image and speech recognition are notable use cases of machine learning in today’s world.
Let’s say you want a software to identify objects in images. The first thing you would do is give it a lot of training data, which in this case would be a bunch of images with the objects you want it to identify already labeled. Then, you would use a machine-learning algorithm to teach the AI how to identify those objects.
After the AI has been trained, you can then give it new images, and it will be able to identify the objects in them.
What Makes Machine Learning Hard To Learn?
Is machine learning hard? Yes, it is. But what makes it so hard to learn? Here are some factors that can sometimes prove challenging when you’re getting started with machine learning.
- Extensive programming knowledge. To implement machine learning algorithms, you need to have a strong understanding of programming languages like Python, Julia, and other advanced programming languages.
- Deep learning. Deep learning is a branch of machine learning that uses a deep neural network to develop algorithms capable of achieving human-level performance on complex tasks. To learn deep learning, you’ll need a solid understanding of math and statistics in addition to your programming skills.
- Distributed computing. Machine learning algorithms typically scale when they’re distributed across a large number of computers during the training process. If you want to venture into distributed computing, you will need a certain amount of knowledge in software engineering and cloud computing.
- Difficult algorithms: Machine learning algorithms can be difficult to understand, especially for beginners. Each algorithm has different components that you need to learn before you can apply them. Even then, not all algorithms will work well with your data set or business problem, so experimentation is needed to find the right approach.
- Math skills: It would help if you were comfortable with basic math concepts such as probability, statistics, and linear algebra to understand machine learning algorithms. Mastering these concepts can be difficult due to their complexity. You also need to learn how to use each idea in machine learning, which requires you to understand these topics in-depth rather than just the basics.
Keep reading to discover the best ways to learn machine learning and build a career in the field.
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How Long Does It Take To Learn Machine Learning?
A bachelor’s degree in machine learning takes around four years. You can find a comprehensive list of American universities offering BS courses in machine learning here.
Meanwhile, a master’s degree typically takes an additional two years.
If you previously have formal education in machine learning or relevant subjects, such as artificial intelligence, data science, computer science, or mathematics, you can get started with a short certificate or course. Beginners will need to take detailed courses and should expect a duration of six to 18 months to develop a strong understanding of the subject.
How To Get Started With Machine Learning
If you’re confused about where to start your machine learning journey, these tips will help you navigate in the right direction.
Build Your Foundation
First of all, you need to know how machine learning works. You should have a sound grasp of the basics of machine learning, such as logistics regression, clustering, feature selection, and classification. The main topics in machine learning include:
- Machine learning
- Neural networks
- Expert systems
- Data science
- Logistic regression
- Big data
- Deep learning algorithms
- Speech processing
- Natural language processing
- Evolutionary computation
- Computer vision
Most machine learning models are built in six steps. These are data collection and access, data exploration and preparation, model building, model evaluation, model deployment, and model monitoring.
You should be familiar with these steps because they will help you throughout your machine learning journey.
To better understand machine learning algorithms, you need to learn the basics of linear algebra, linear regression, speech recognition, statistics, and probability. You should know how these mathematical concepts are implemented in modern-day computer science problems such as clustering and support vector machines (SVMs).
Knowing these concepts is beneficial because it will help you with regression and classification.
Utilize Free Resources
Fortunately, many free resources for machine learning are available online. You can find several books and introductory YouTube tutorials, lectures, blogs, etc. on various websites. For instance, Paradigms of Artificial Intelligence Programming is a free book you can read to familiarize yourself with the fundamentals of machine learning.
Google also offers a Machine Learning Crash Course that provides an interactive visualization of algorithms in action.
In addition, there are also free software tools that you can use for data exploration, pre-processing, and modeling.
Take a Course
Once you have built a solid foundation in the field, go ahead and learn the practical side of things through an online course.
Many online machine learning courses teach you how to apply the concepts you have learned in a real-world setting. These courses usually last for six to 18 months and are taught by experts in the field.
If you struggle with Python, you can take this free Machine Learning in Python course to hone your programming skills with hands-on Python tutorials.
But if you already have advanced knowledge of linear algebra, calculus, statistics, and Python, you should consider this Machine Learning Bootcamp to become a machine learning engineer.
A machine learning bootcamp will help you gain the skills required to work on complex machine learning algorithms and escalate your learning journey. You will learn how to implement various machine learning models and optimize them for better performance.
Ask for Help
If you don’t understand something, ask for help and learn from the response. Machine learning has many complicated concepts that you may not understand when trying to learn on your own.
If someone explains the same thing in a different way or wording to you, it may be easier to get new insight into the concept.
You can contact data scientists, machine learning specialists, and other experts or attend online forums to discuss problems and get assistance with specific challenging areas in machine learning.
For instance, James McCaffrey’s The Data Science Lab is a series of articles on machine learning. You can meet like-minded people in the comment sections or through the events section of the website.
Don’t give up if you still don’t understand something after a few attempts. Viewing the problem from different perspectives may help you finally nail it down.
How To Get Better at Machine Learning
Here are some tips to get better at machine learning.
Get a Mentor
A machine learning mentor can help you by providing guidance and feedback on your machine learning work. A mentor can also help motivate you, give constructive suggestions, teach you how to use toolkits, and keep you accountable for completing tasks.
You can find a mentor by joining online forums or an in-person meetup group where people gather to discuss machine learning issues and collaborate on projects together. Speedy Mentors is a great place to find mentors for machine learning.
Practice on Your Own Time
As with so many things in life, the more you practice, the better you’ll get at machine learning.
When you’re practicing, try to focus on one specific skill at a time. This will help you learn and retain the information better.
For example, if you want to become better at unsupervised learning or data pre-processing, work on a project that involves these topics and complete it. This iris flowers classification ML project is an excellent example of this.
You can also work on projects with other people you meet in online forums and groups. This will help you learn many different machine learning skills, such as building models, improving performance, and debugging code.
Participate in Competitions
Machine learning platforms and databases like Zindi, Kaggle, DrivenData, and Alcrowd organize many competitions that are open to the public.
Kaggle is considered one of the best platforms for competition practice because it has many well-organized competitions and forums. For example, the Avito competition focuses on topic modeling and extraction of recommendations for products based on user reviews.
Sign up to participate in machine learning challenges and learn how to handle pressure under time constraints.
Attend Events and Presentations
There are many machine learning events scheduled for 2022 that you can attend to meet other machine learning professionals. These include:
- Computer Vision Summit 2022
- Algorithm Conference 2022
- World AI Cannes Festival 2022
- Deep Learning Summit 2022
- Data Innovation Summit MEA 2022
- Global Tech Innovation Summit 2022
- MarTech Summit Singapore 2022
- CureSearch Summit 2022
Meeting people at these events will help you learn more about machine learning and establish connections with industry experts.
Networking is helpful in every profession because it allows people to share their experiences and to learn from one another. Networking also helps you get jobs and find potential opportunities for growth in your industry.
About Machine Learning as a Career
Before you step into the field of machine learning, you should know some important things about this career field.
What Are the Requirements To Get Into Machine Learning?
The fundamental requirements to get into machine learning are:
- Basic math knowledge including topics like linear algebra, calculus, statistics, etc.
- Programming experience
- Knowledge of machine learning algorithms
- Degree in computer science, information systems, mathematics, operations research, or similar fields of study
- Ability to think critically and solve problems
- Interest in learning new things and tackling complex challenges
Is Machine Learning a Good Career?
Machine learning is undoubtedly a good career, as it offers many opportunities for growth and advancement. Moreover, the job prospects for machine learning are very good because the field is growing rapidly.
According to Indeed’s 2019 report, machine learning engineer was the best job in the United States. The Bureau of Labor Statistics also reports that the employment of computer scientists will grow 22% from 2020 to 2030.
Does Machine Learning Pay Well?
According to Glassdoor, the average salary of a machine learning engineer in the U.S. is $131,001 per year. People with high-level skills earn up to $195,00 per year, while those on the lower end of the scale earn up to $88,000.
While machine learning isn’t the easiest field to break into, it does pay well and has a lot of growth potential.
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