Is Machine Learning Hard? A Guide to Getting Started

Machine learning is one of the most cutting-edge fields in technology today, powering innovations like Netflix recommendations and speech-to-text recognition on your smartphone. A career in machine learning blends maths, computer science, and coding, requiring a solid educational foundation and practical training.

Whilst mastering machine learning involves learning programming languages like Python and staying updated with AI trends, the wealth of educational resources available make gaining the skills and experience needed for success more accessible. Discover if machine learning aligns with your goals and how you can start your journey today. 

What 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.

You might already be using many devices or applications based on machine learning algorithms. Voice-to-text technology, employed by both iPhones and Androids, relies on machine learning—specifically deep learning. It analyses speech and translates to text based on the software’s established knowledge of interpeting audio as language.

The rise of machine learning

Machine learning caught some mainstream attention in 2011 when IBM’s Watson, a supercomputer, competed on the American TV quiz show “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 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 its programming to handle certain types of data. Machine learning could potentially automate anything with an organised set of rules, guidelines, or protocols.

The importance of machine learning

Machine learning is driving innovation and efficiency in today's technology, from automating simple tasks like data entry to enabling significant advancements like dynamic pricing and real-time alerts. The following explains in more detail the benefits and impact of machine learning.

• Automating manual tasks: Machine learning programs aim to automate tasks and draw conclusions from data sets more quickly than humans could by manually analysing them. This can save you 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 that it thinks you will like based on your preferences.

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

• 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 analyse 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.

What factors can make machine learning hard to learn?

Learning machine learning offers a rewarding challenge, as it involves developing a strong foundation in maths and computer science. The process of optimising algorithms and debugging code provides valuable experience in handling complex tasks and refining your skills. Embrace the opportunity to grow and excel in this exciting field, knowing that each step forward brings you closer to mastering machine learning. The following list highlights some of the factors you may encounter.

You need extensive programming knowledge.

Machine learning requires knowing 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 scalable.

You'll need to embrace the challenge of optimising algorithms.

Each machine learning application needs its algorithm optimised 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.

You'll need maths.

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 the useful applications of AI expand. According to a report from Nasscom, the AI market in India will likely expand at a CAGR of 25 to 35 per cent until 2027 [1]. This means that companies will want employees who can upskill in ML and AI to bridge the emerging skill gaps. 

Job roles in machine learning

Take a look at several other jobs in machine learning and their respective average annual salaries:

• Machine learning engineer: ₹11,20,000 [2]

• Data scientist: ₹14,25,000 [3]

• AI engineer: ₹11,50,000 [4]

• Software developer: ₹6,70,000 [5]

How to start in machine learning

To get started in machine learning, begin building your foundation in maths and statistics. Then you can start exploring additional topics in machine learning. A career path in machine learning can begin today through formal and self-taught education.

1. Build your foundation in maths and computer science.

Start with learning the basics of maths (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 explore topics related to machine learning. For example, MIT offers a free video lecture series on machine learning. You also can find data sets to train your skills for working with AI on Google and Kaggle.

Coding languages are essential for machine learning, and you can find plenty of resources available to help you with this task. Learn Python 3 the Hard Way by Zed Shaw is an easily accessible e-book that walks through Python. Another book, An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani offers the basics of statistics.

3. Take online courses.

Check out the courses available online to learn ML.

Andrew Ng's Machine Learning course by DeepLearning.AI offers 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. Try the Mathematics for Machine Learning: Linear Algebra course from Imperial College London.

The University of Washington also offers a Specialisation 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.

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, whilst 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.

Next steps in machine learning with Coursera

With proper study you can gain the skills needed to land an in-demand job in machine learning. If you’re ready to learn about ML try Andrew Ng’s Machine Learning Specialisation on Coursera. It 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.