Is Machine Learning Hard? A Guide to Getting Started

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Machine learning is one of the most cutting-edge fields in the tech industry. Learn how to gain machine learning skills, become a machine learning engineer, how to make machine learning easier, and more with this guide from Coursera.

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Machine learning is an advanced field that incorporates many aspects of mathematics, computer science, and coding. A career in machine learning typically requires a Master’s of Science degree. The education and training involved in machine learning can require intense dedication, depth of knowledge, and attention to detail.

You can get started with machine learning by learning coding languages, practicing fine-tuning algorithms, and paying close attention to artificial intelligence applications for products and services. Everything from the technology of a Tesla vehicle, Netflix’s recommendation algorithms, c or speech-to-text recognition on your iPhone represents an innovation in machine learning.

You can find information about machine learning from a breadth of free, accessible resources. Because machine learning is an essential field in the tech industry, there’s plenty to learn about its continuing effect on our devices each day. This access to information gives you plenty of opportunities to plan your career path in machine learning, though the material may be arduous and require intense dedication. Online courses, textbooks, and various articles and speeches from experts can help you get involved.

Learn more about machine learning with this guide. Discover machine learning concepts, what can make them difficult, and how they relate to real-world applications. You can also map out an education or career path in machine learning and learn more about the job market and salaries for machine learning engineers, data analysts, and computer scientists.

What exactly is machine learning?

Machine learning 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 used in many applications we use daily. Voice-to-text technology, which iPhones and Androids use, is created with machine learning—specifically deep learning—because it analyzes speech and translates to text based on the software’s established knowledge of how audio can be interpreted as language. 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 is often credited for first defining 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 data and determines actions based on how it is programmed to handle certain types of data. Machine learning could potentially automate anything with an organized set of rules, guidelines, or protocols.

Machine learning uses two basic techniques: supervised learning and unsupervised learning. Supervised learning provides the machine with a training sample to teach it which patterns to recognize. By doing this, you are supervising its learning. The machine analyzes data that you label and classify, predicting outcomes. An example of supervised learning is a spam email folder: The machine analyzes the senders and email subjects and sorts them accordingly.

Unsupervised learning entails that the machine draws unknown parallels and findings from unlabeled data. An example of this is clustering, a method in which the machine separates data points into clusters. Each cluster contains points that are similar to each other and in another way dissimilar from data points in the other clusters. On its own, the machine identifies patterns in the data, leading to insights or recommended action. One example of unsupervised learning is customer segmentation, in which a business’s customer data is grouped based on patterns in their purchases and interactions with the company.

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.

Automation

A machine learning program aims to automate a task or draw conclusions from a data set much more quickly than humans could by manually analyzing it. Your coding skills are used here because algorithms must be fine-tuned. It takes intuition to recognize when an algorithm isn’t working or when it isn’t working well enough.

Trends and patterns

Technology that uses machine learning finds patterns in data and often recommends actions based on those patterns. For example, a Google Home Mini in your bedroom interprets your voice and learns from phrases you commonly use in questions to improve accuracy and speed.

Extensive range of applications

From a smart speaker to a self-driving car, machine learning informs many recent groundbreaking inventions or innovations to existing technology. Google’s Real Tone, which seeks equity in displaying photos of people of color more accurately, is a meaningful recent innovation in AI and machine learning. Real Tone’s AI uses more nuanced auto-white balance and more advanced auto-exposure technology.

Scope of improvement

You can identify areas of improvement to a machine learning application through careful attention to its use of an algorithm. Programming languages such as Python can identify areas of improvement for a machine learning application.

Improvements for machine learning applications seek to explain the AI’s actions or, in other words, determine whether those actions fix the problem the application was invented to solve. Some techniques for testing applications include testing the machine against humans, establishing avoidable bias, and adjusting the algorithm to be as accurate as possible. Software is developed to accelerate or assist data analysis, data entry, and interpretation.

Efficient handling of multidimensional and multi-variety data

Applications that use machine learning can analyze data and draw conclusions or make suggestions at a faster pace or higher level of sophistication than a human being would be able to make on their own. A paper published by MIT and Michigan State found that its machine learning technology could analyze data and arrive at a solution 100 times faster than humans.

An excellent example of this comes in banking. Banks such as Chase and Citi, among many others, use artificial intelligence to detect money-laundering activity or fraud. Not only would it be impossible to employ enough people to detect these trends in financial transactions manually, but those employees would also likely miss a significant amount of illicit activities.

What factors can make machine learning hard to learn?

Factors that make machine learning difficult are the in-depth knowledge of many aspects of mathematics and computer science and the attention to detail one must take in identifying inefficiencies in the algorithm. Machine learning applications also require meticulous attention to optimize an algorithm. Debugging machine learning algorithms is difficult because the code includes multiple dimensions where information can be incorrect.

Extensive programming knowledge

Programming languages such as Python, R, C++, or JavaScript are important for machine learning. A detailed grasp of these languages is the foundation for machine learning.

Read more: Python or R for Data Analysis: Which Should I Learn?

Deep learning

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.

Distributed computing

Distributed computing is where cloud computing and computer engineering come into machine learning. Machine learning applications are trained 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.

Difficult algorithms

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

Math skills

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.

How long does it take to learn machine learning?

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.

While studying for an undergraduate degree, you can learn the basics of machine learning, though the in-depth machine learning material will typically come later in education. The undergraduate education to prepare for machine learning includes linear algebra, probability, statistics, and coding.

US News and World Report ranked the top universities with artificial intelligence programs in 2022 are Carnegie Mellon University, Massachusetts Institute of Technology (MIT), Stanford University, University of California-Berkeley, and Cornell University [1].

Some undergraduate courses of study can help you prepare for machine learning later on in education. These include majors in mathematics, data science, computer science, and computer engineering.

Formal preparation in machine learning is typically complete with a master’s degree. Education in a master’s program includes specialized learning about artificial intelligence and exploration of specific applications, plus internships, fellowships, or other forms of experiential learning. Continuing education is also required for machine learning to stay informed of and contribute to new developments.

How to get started with machine learning

There are many points where you can start on your journey toward a career in machine learning, whether that path includes formal education or training on your own. Establishing a basis of knowledge in several mathematics concepts and some comfort with basic programming can provide you with the foundation for an in-depth education focused on machine learning. You can then specialize in deep learning, neural networks, or any other subset of machine learning.

Build your foundation in machine learning.

You can approach the task with the help of many resources, as creating a foundation in machine learning can be a long process. Machine learning contains many layers and intersecting pieces of mathematics, data science, and computer science.

Utilize free resources.

You can find many resources online to gain an introduction 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.

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

Take a course.

Google offers a Machine Learning Crash Course, including more than 30 exercises, 25 lessons, and real-world case studies. The course takes about 15 hours to complete. Another major innovative company in the field, IBM, offers online Starter Kits that include guides through machine learning scenarios.

Linear algebra is another building block for machine learning. MIT offers a free linear algebra course to learn the basics.

Stanford University, known as one of the top universities for machine learning education, offers a Machine Learning course on Coursera taught by Andrew Ng, founder of DeepLearning.AI. The course overviews how computers can act without being explicitly programmed. Skills and practice you can gain from this course include logistic regression, artificial neural networks, and machine learning algorithms.

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Supervised Machine Learning: Regression and Classification

In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries ...

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Regularization to Avoid Overfitting, Gradient Descent, Supervised Learning, Linear Regression, Logistic Regression for Classification

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.

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. You will use these subjects often in your machine learning education and work experience. A bachelor’s degree in computer science, information systems, or mathematics can be a sound basis for continuing education in artificial intelligence.

Your desire to learn more, improve existing systems and create useful applications for machine learning technology will take you far and help establish your career aspirations. 

Machine learning engineer job overview: Salaries, careers, and courses

Machine learning jobs are growing as the useful applications of AI expand. According to the US Bureau of Labor and Statistics, computer-related occupations are expected to grow 13 percent between 2020 and 2030 [2].

Average earnings per year

Machine learning engineers in the US earn, on average, $99,870 per year [3]. Highly-skilled machine learning engineers can earn up to $227,000 per year. A few additional roles that require machine learning skills include the following salary information:

  • Data scientist salaries range from $71,000 to $248,000 and average $99,051 [4].

  • Machine learning developer salaries range from $65,000 to $275,000 and average $96,002[5].

  • Computational linguist salaries range from $53,000 to $325,000 and average $86,228 [6].

  • Software developer salaries range from $60,000 to $280,000  and average $86,177 [7].

Career outlook in machine learning

Machine learning topped Indeed’s 2019 list of the best jobs in the US [8]. Machine learning engineer jobs are growing in number far better than any other job, with Indeed reporting that machine learning engineer listings increased by 344 percent from 2015 to 2018. Employment of computer scientists in the US is expected to grow 22 percent from 2020 to 2030, according to the US Bureau of Labor Statistics [9].

Find out more about machine learning.

The breadth of information about machine learning is vast and detailed, and you can dig into anything related to the field through the resources highlighted here.

Find various learning opportunities on machine learning and related subtopics on Coursera. Gain more knowledge and practice and sharpen your skills with online degree programs or Specializations in topics such as deep learning, applied AI, applied data science, and neural networks.

Related articles

Article sources

1. US News and World Report. "Best Artificial Intelligence Programs, https://www.usnews.com/best-graduate-schools/top-science-schools/artificial-intelligence-rankings." Accessed April 26, 2022.

2. US Bureau of Labor Statistics. "Computer and Information Technology Occupations, https://www.bls.gov/ooh/computer-and-information-technology/home.htm." Accessed April 26, 2022.

3. Glassdoor. "Machine Learning Engineer Salaries, https://www.glassdoor.com/Salaries/machine-learning-engineer-salary-SRCH_KO0,25.htm." Accessed April 26, 2022.

4. Glassdoor. "Salary: Data Scientist, https://www.glassdoor.com/Salaries/data-scientist-salary-SRCH_KO0,14.htm." Accessed April 26, 2022.

5. Glassdoor. "Machine Learning Developer Salary, https://www.glassdoor.com/Salaries/machine-learning-developer-salary-SRCH_KO0,26.htm." Accessed April 26, 2022.

6. Glassdoor. "Computational Linguist Salary, https://www.glassdoor.com/Salaries/computational-linguist-salary-SRCH_KO0,22.htm." Accessed April 26, 2022.

7. Glassdoor. "Software Developer Salary, https://www.glassdoor.com/Salaries/software-developer-salary-SRCH_KO0,18.htm." Accessed April 26, 2022.

8. Indeed. "The Best Jobs in the U.S. in 2019, https://www.indeed.com/lead/best-jobs-2019?gclid=Cj0KCQjw_4-SBhCgARIsAAlegrVPJdcryrrCemK4pHIcchzUr9AvmKiZF-EDD16amxbzTPlwE-BhTJsaAm2GEALw_wcB&aceid=." Accessed April 26, 2022.

9. US Bureau of Labor Statistics. "Computer and Information Research Scientists: Occupational Outlook, https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm#:~:text=in%20May%202020.-,Job%20Outlook,on%20average%2C%20over%20the%20decade.” Accessed April 26, 2022.

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