With machine learning skills, you can work in fields such as data science, AI, and medical technology. Here are some tips on how to get started.
Machine learning is a subset of AI and is used in many real-world scenarios such as customer service, recommender algorithms, and speech recognition software.
Machine learning is so widely used that it’s a great area to get familiar with. A very simple way to describe machine learning and how it works is to think of it as a computer that uses algorithms and data to mimic the way humans learn.
Let’s take a look at some concepts you should know in machine learning. After learning some basics, you may end up focusing on one of these areas.
the term
neural network
Neural network architecture is sometimes called “deep learning”. It consists of algorithms that can mimic the way the human brain processes and perceives relationships between large datasets.
Neural networks are used in areas such as market research and large-scale data industries.
There are three main types of neural network training. These are supervised learning, unsupervised learning and reinforcement learning. In this article, we will look a little more closely at the differences between supervised and unsupervised.
regression analysis
It consists of a set of machine learning methods that predict a continuous outcome variable based on the values of one or more predictor variables.
Regression analysis can be used, for example, to predict the weather or predict the price of a product or service given its features.
clustering
Clustering, as the name suggests, is primarily about identifying patterns in the data so that the data can be grouped together.
This tool uses machine language algorithms to create groups of data with similar characteristics. It can do this much faster than humans.
Supervised and Unsupervised
Supervised machine learning relies on labeled input and output data, while unsupervised machine learning does not. Unsupervised machine learning can process raw, unlabeled data.
Clustering uses unsupervised machine learning to group unlabeled data.
machine learning skills
As we have made clear, machine learning professionals frequently manipulate data. Data science skills are required as well as software engineering knowledge.
Coursera’s Machine Learning Skills article recommends learning data science languages such as SQL, Python, C++, R, and Java for statistical analysis and data modeling.
Now let’s move on to math. Understanding the data science component of machine learning requires a fairly solid foundation in statistics and mathematics.
Being able to think critically about why you use a particular machine learning technique is also very important, especially when you need to explain the technique and why to colleagues who don’t have a technical background.
Earlier this year, Yahoo’s Zuoyun Jin shared some learning tips based on his experience as a machine learning research engineer.
learning resources
If you want to brush up on Python for machine learning, this guide from SiliconRepublic.com points you in the direction of a useful resource.
To get a basic overview of machine learning, check out some of the beginner courses online. This Understanding Machine Learning program from Datacamp claims to get you started with no coding required.
If you’re looking for something more advanced, this course from MIT gives learners an introduction to machine learning and how this technology can be used in the enterprise. The main purpose is to apply the techniques in business situations.
Last but not least, Google’s Machine Learning Crash Course is a 25-lesson program featuring lectures on the subject from Googlers.
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