Is Machine Learning Hard? A Guide to Getting Started

Machine learning is one of the most trendy fields in technology today. It fuels the technology behind Netflix recommendations and smartphone speech-to-text recognition. A mix of math, computer science, and coding, a career in machine learning requires extensive education and training to land a job as an engineer.

So, is machine learning hard to learn? You'll need to learn programming languages like Python, practice using and modifying algorithms, and keep up with trends in AI. Plenty of educational resources online can help you, such as courses and specializations, to gain the skills and experience you need for a career in machine learning.

Explore the field of machine learning, some key things you can do in the field, and some factors that make machine learning a challenging but rewarding field to get into.

What is machine learning?

Machine learning (ML) is a branch of artificial intelligence that imitates how humans learn. It is also a division of computer science that uses algorithms and data to adjust its actions as it gathers more information.

Machine learning is in many applications you use daily. Voice-to-text technology, which iPhones and Androids use, uses machine learning because it analyzes speech and translates it to text.

The rise of machine learning

Machine learning caught some mainstream attention in 2011 when IBM’s Watson, a supercomputer, competed on “Jeopardy!” and convincingly beat each of its human competitors. Arthur Samuel, a notable scientist who worked at IBM for 17 years, was a pioneer in the field of machine learning and defined the term in 1959. Samuel developed software that could “learn” on its own how to win a game in computer checkers. Samuel’s computer made each move based on the highest chance of “kings” and remembered every position it faced on the board.

How does machine learning work?

Machine learning works by imitating the way humans learn. A machine identifies patterns in the data and then creates a prediction using its training and programming based on that data. Machine learning could potentially automate anything with an organized set of rules, guidelines, or protocols.

The importance of machine learning

Machine learning can automate simple tasks, such as data entry or compiling contact information lists into a particular format. It can also make significant technological changes, such as dynamic pricing for event tickets or public transportation delay alerts. The following explains in more detail the benefits and advantages of machine learning.

• Automating manual tasks: Machine learning programs automate tasks and conclude data sets more quickly than humans could by manually analyzing them. It also saves us a lot of time.

• Spotting trends and patterns: Machine learning detects patterns in data and recommends actions based on those patterns. Netflix's algorithm spots patterns in your TV watching to recommend shows you will like based on your preferences.

• Range of applications: From "smart homes" to self-driving cars, machine learning informs many recent groundbreaking technological innovations.

• Constant improvement: Careful attention to an algorithm and the data sets fed into it, as well as the use of programming languages such as Python, can identify areas of improvement for a machine learning application to offer quality assurance. Adjusting an algorithm as often as possible helps uphold AI ethics to establish avoidable bias.

• Rapid handling of multi-dimensional data: Machine learning applications allow us to analyze data and draw conclusions at a faster pace and a higher level of sophistication than humans can do on their own. For example, banks use AI to detect money laundering or fraud. To achieve this without machines would require too many employees, who would likely miss a significant amount of illicit activity.

Is machine learning hard to learn? Factors to consider

Machine learning can be difficult to learn because it requires in-depth knowledge of math and computer science. Optimizing algorithms is a meticulous task, and debugging them requires inspecting multiple code dimensions. Some factors, such as programming knowledge, deep learning, distributed computing, optimization methods, and extensive math knowledge, make up the intricacies of machine learning. Learn more about each factor below.

You need extensive programming knowledge.

Machine learning requires knowledge of programming languages such as Python, R, C++, or JavaScript. A detailed grasp of these languages is the foundation for machine learning.

Deep learning is complex.

Deep learning is a subset of machine learning that attempts to replicate how the human brain works. It uses a neural network of three or more layers and aims to gather insights from data on a deeper level than one layer could manage. The additional layers refine information and make it more accurate.

You'll need to know distributed computing.

Distributed computing is where cloud computing and computer engineering come into machine learning. Machine learning applications train using networks of computers to scale up operations. Distributed computing, also known as distributed processing, is the process of joining two or more computer servers into a cluster to coordinate processing power and share data. This practice combines the power of multiple computers, saves on energy costs, and makes machine learning projects more easily scaled up.

Algorithms can be difficult to optimize.

Each machine learning application needs an algorithm optimized for its specific function. Attention and repeated experimentation with complex algorithms can prepare you for the trial-and-error of adjusting algorithms. Adjusting existing algorithms to new applications takes creativity and tenacity.

You'll need math.

Machine learning combines several intermediate to advanced mathematical concepts, such as linear algebra, probability, and statistics. Your in-depth knowledge of these critical concepts should prepare you to learn even more about machine learning.

Machine learning career overview

Machine learning jobs are growing as AI's useful applications expand. The US Bureau of Labor and Statistics expects computer and information research occupations to grow 26 percent between 2023 and 2033 [1]. On average, these occupations earn a median salary of $145,080 [1].

Job roles in machine learning

Below are several other jobs in machine learning and their respective average salaries.

• Machine learning engineer: $122,394 [2]

• Data scientist: $117,576 [3]

• Computational linguist: $97,014 [4]

• Software developer: $103,728 [5]

Machine learning landed at number eight on Indeed’s 2023 list of the best jobs in the US [6]. Machine learning engineer jobs are growing in number far better than any other job, with Indeed reporting that machine learning engineer listings increased by 53 percent from 2020 to 2023.

How to get started in machine learning

A career path in machine learning can begin today, whether that involves formal or self-taught education. Start with a foundation in math and statistics, and then read up on everything machine learning you can get your hands on.

1. Build your foundation in math and computer science.

Start by learning the basics of math (calculus, algebra, and more) and computer science. You'll need this foundation to understand how algorithms and machine learning models work.

What are the requirements to get into machine learning?

As you prepare for a career in machine learning, you will want a strong basis in computer science, programming, linear algebra, calculus, and statistics. A bachelor’s degree in computer science, information systems, or mathematics can be helpful, but you can also use continuing learning resources and online courses to get up to speed if you already have a bachelor's in another subject.

2. Read everything you can about machine learning.

Use free resources online to learn everything you can about machine learning.

Many resources online can introduce you to machine learning. MIT offers a free video lecture series on machine learning, for example. Data sets to train your skills for working with AI can be found on Google and Kaggle.

Lots of free resources are available for learning coding languages, which are essential for machine learning. Learn Python 3 the Hard Way is an easily accessible e-book that walks through Python. Another free book, Statistical Learning by Gareth James, offers the basics of statistics.

3. Take online courses.

Utilize courses online to learn machine learning.

For example, Andrew Ng's Machine Learning course from DeepLearning.AI is a comprehensive overview. Skills and practice you can gain from this course include logistic regression, artificial neural networks, and machine learning algorithms.

Linear algebra is another building block for machine learning. For example, you might be interested in the Mathematics for Machine Learning: Linear Algebra course from Imperial College London.

The University of Washington also offers a deep dive specialization in Machine Learning. IBM has a Professional Certificate in Machine Learning. These courses are comprehensive and take several months to complete, but you'll take away a strong grasp of machine learning.

4. Ask for help.

Having someone in your corner can be a tremendous asset when learning something as advanced as machine learning. You can find academic mentors through online services such as MentorCruise or Speedy Mentors.

How long will it take to learn ML?

A bachelor’s degree in machine learning usually takes four years when attending school full-time, while a master's degree can take an additional two years. So, the answer depends on where you are in your education and career path. Gaining the skills necessary to land an internship or entry-level job can take several months if you already have a bachelor's degree and work experience.

Getting started with machine learning on Coursera

Machine learning is a vast field combining traditional computer science with linear algebra, programming, and algorithms to imitate human learning. Continue exploring the topic with courses and programs like Andrew Ng’s Machine Learning Specialization on Coursera. This three-course series provides a comprehensive introduction to modern machine learning, including supervised learning, unsupervised learning, and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation.