April 27, 2022 
3,5 MIN READ 
A branch of artificial intelligence (AI) and computational science, machine learning has become increasingly influential in recent years. What started as a trivial concept now looks set to become the future of computer science.
Machine learning is essentially the idea of programming a computer system to program itself. By enabling a computer to learn, it can improve and adapt, bringing greater efficiency and accuracy.
Where does the term ‘machine learning’ come from?
As with most computational terms, it all started with IBM. Computer scientist and self-confessed checkers fanatic Arthur Samuel coined the term when his research led him to lose a game of checkers to a computer. Samuel may have lost the game and a touch of pride, but his research is today considered a landmark moment in the study of artificial intelligence.
What are the key ML elements?
There are three main elements in machine learning:
Representation
Neural networks, graphical models, decisions trees – there are a number of ways machine learning algorithms can be represented.
Evaluation
This is the hypothesis by which you can make sense of the data. The three main examples when it comes to evaluating a model are accuracy, prediction and recall.
Optimisation
This is the method used to find the hyperparameters of a machine learning algorithm, in order to optimise performance as measured on a validation set.
What can you do with machine learning?
Machine learning enables us to understand and explore trends, behaviors and operational patterns, as well as develop future products, features and technologies.
What’s machine learning used for?
Machine learning is already being used to break new ground across several different industries. Here are just some of its uses today on a daily basis:
- Speech recognition software – Alexa, Siri, etc.
- Recommendation engines – algorithms for the likes of Google, Netflix, YouTube
- Customer support – chatbots
- Search engine results – Google etc.
- Online fraud detection – used by the likes of Paypal to compare genuine transactions to fraudulent ones
- Traffic predictions – traffic flow for cities and online transportation networks (Uber etc.)
- Robotics – autonomous machines and self-driving cars
Why is it important?
As data plays a more influential role in our daily lives, machine learning – the key to unlocking the true value of data – becomes all the more important.
Whether it’s gaining a greater understanding of a customer base, producing technologies that take UX to a new place, or creating new models that help to streamline performance, machine learning will play a pivotal role in the future of pretty much every industry.
What skills do you need for a career in machine learning?
Python
A very simple code to learn and implement, Python is used by almost 70% of data scientists and ML developers. This fundamental programming language makes it very easy to build a complex set of models and algorithms for a quick and effective machine learning test.
Applied mathematics
From probability theory and statistics to linear algebra and algorithms, maths plays a big part in machine learning. Aside from these core competencies, a machine learning engineer needs to have an all-round enthusiasm and expertise in the general concepts of mathematics.
Building neural networks
By using examples of how the human brain works, ML engineers can help model and simulate a more accurate and effective model, through recognising patterns and solving problems.
Data modelling and evaluation
As ML engineers work with large amounts of data, understanding how to model and evaluate this data is essential. Data modelling and evaluation concepts such as classification accuracy, logarithmic loss and confusion matrix are key.
Reinforcement Learning
A behavioural learning model that enables an intelligent system to make incremental improvements while building towards a successful outcome. Machine learning engineers need to have a firm understanding of this fundamental concept of ML.
What are the different machine learning styles?
Depending on your data output and input, as well as the kind of results you’re expecting, your algorithms can be categorised by different learning styles:
- Supervised machine learning – data is labelled and you have an specific expectation of the results. Examples are linear or logistic regression and support vector machines
- Unsupervised machine learning – data isn’t labelled and you have no expectation of the results. Examples are anomaly detection
- Self-supervised machine learning – data isn’t labelled but the task is supervised. This method is is used to reduce data labelling costs, while getting results from an unlabelled data pool
- Reinforcement machine learning – data isn’t labelled and a feedback loop is used to strengthen the model and achieve the desired outcome
How do you get into machine learning?
The first step is to acquire the technical skills needed for the job. CodeOp’s Data Science bootcamp equips you with a complex data science skill set, from fundamental concepts to specialist knowledge.
You’ll also learn from hands-on, real-life application cases, with courses led by expert instructions from a variety of backgrounds. After graduating from our eight-module curriculum, we’ll provide career coaching, personal growth workshops, training and mentorship programs, making sure you’re completely ready for your new career in machine learning.
Want to know more? Download our Data Science Bootcamp course guide and prepare yourself for a career in tech.
3,5 MIN READ