How to Get Started With Machine Learning?
If we look at the current state of things in the world of technology, we can see the popularity of machine learning is rapidly increasing almost every day.
It has become one of the most popular career choices and according to some studies, being a machine learning engineer was the most wanted job of 2019, with an average annual base salary of $146,085 and a 344% growth.
With all this in mind, machine learning is a buzzword that’s echoing through the world, affecting every vertical of modern society and impacting the modern industry. However, learning about machine learning is quite a challenge as this advanced technology is based on complex mathematical processes and tedious computations.
The best way to get to the root of the problem is by starting slow and simple. So, to help you get started with machine learning, we’re going to delve deeper into the basics of machine learning to help you understand how to start learning about this technology.
Understand the Prerequisites of Machine Learning
Unless you’re a genius, we suggest you start your learning curve with some prerequisites that you need to know. These include:
- Python – while some newbies skip the other steps, Python is something you simply can’t skip. Out of all other programming languages, Python is probably the most user-friendly one, and it is perfect for machine learning. More importantly, this language has a range of libraries that can make learning machine learning and artificial intelligence more streamlined and effective.
- Statistics – machine learning is the game of data where you constantly need to achieve the goal of gathering and filtering data. Statistics is the process that handles the collection, analysis, and interpretation of data.
- Multivariate Calculus and Linear Algebra – both prerequisites are essential to machine learning. If your interest in machine learning extends to maths-heavy applications, then you need both MC and LA to nail your ML game.
Get Ahead of Various ML Concepts
While we understand dealing with prerequisites isn’t fun at all, it’s necessary for the next phase we have in store for you. That’s the fun part simply because you get to actually learn ML.
Start with simple basics and work your way up to the more complicated stuff. Here are the two basic concepts to focus on – ML terminologies and types of ML.
ML terminologies include:
- Model – also known as a hypothesis, a model is a specific representation obtained from analyzing data gathered by applying an ML algorithm.
- Feature – every piece of data gathered has measurable properties, which are called features. A set of numeric features, also known as a feature vector, is used to measure individual properties of the collected data. Since your ML model needs data input to perform operations with the desired results, you need feature vectors to create the needed data input for your model.
- Label/target – once your model gets fed with the proper input, it will predict the target value or the wanted result.
- Training – if your model delivered the wanted result, it’s time to test it by feeding a set of data inputs and expected outputs to see how it interacts with the given data.
- Prediction – a fully ready ML model will use the fed data to provide predicted results (outputs).
Types of ML include:
- Supervised learning
- Unsupervised learning
- Semi-supervised learning
- Reinforcement learning
Practicing Machine Learning
Data collection is the most time-consuming part of ML simply because it requires preprocessing, cleaning, integration, interpretation, etc. However, the more you practice data collection, the more you’re able to collect top-quality data.
However, manually going through large amounts of data is impossible, which is why you need a functional machine learning model that will lend you its power of automation to save time and effort while going through chunks of unprocessed, raw data.
Use real datasets as your practice targets and learn from different ML models. It can help create your learning curve around which types of ML are suitable for different real-world scenarios.
We also recommend paying particular attention to interpreting the results obtained by experimenting with different machine learning models. It will help you get deeper into various tuning regularization methods and parameters applied to different models and purposes.
Machine Learning Resources for Further Studying
We warmly recommend that you immerse yourself in various offline and online resources, both paid and free, to further deepen and widen your ML knowledge. It’s a great way to learn more about machine learning from trustworthy experts and reputable professionals in the field.
Consider taking courses for a broad introduction to the matter. You can find courses for a thorough introduction to ML, self-study guides with real-life scenarios, videos, and spoken lectures, as well as hands-on practice studies.
There are also offline courses where you can explore various machine learning concepts, get some practical experience in dealing with and implement different types and models in a controlled environment, and more.
Conclusion
There’s a bright future ahead of machine learning as its full potential is yet to be explored. There’s no doubt that this AI technology can help make the world a better place. Still, it can also help us find solutions to the world’s leading problems, including pollution and other ongoing problems that bother our modern society.
Machine learning will keep evolving, allowing us to discover new solutions with each passing day. It takes time to see where all this is heading, but we do not doubt that the direction leads us to a better tomorrow.