Machine learning is a field of study that focuses on the development of algorithms and models that can be used for computers to learn from and make predictions based on data. Machine learning differs from traditional programming paradigms as it is a way of solving problems without giving the computer explicit instructions on what to do for each and every situation.
Machine learning isn’t a “better” way of solving problems than traditional programming, but it does enable us to solve problems that we would not be able to solve using only traditional programming techniques.
It’s important because it opens up the possibility of solving a wide range of complex problems that previously could not be solved using only a computer. As the quantity and quality of data that’s gathered every day improves, our machine learning algorithms and models will only become better and better at making decisions and predicting outcomes. With that said, we don’t need to wait for the future to use machine learning; here are four applications of machine learning that are used today:
Recommendation systems: ever scrolled through TikTok, browsed Netflix, or launched a Spotify daily mix? Many companies today in completely different fields are using machine learning to power their recommendation systems and provide users with ultra-personalized recommendations.
Image recognition: also known as computer vision, machine learning is being used by your phone to recognize your face, by your car to recognize obstacles on the road, and by hospitals everywhere to help doctors interpret x-rays and MRIs.
Natural language processing: while ChatGPT is all the rage today, natural language processing is nothing new to consumers! Smart Assistants like Alexa and Siri have been using natural language processing for years to improve interactions between humans and computers.
Forecasting: machine learning models have been used for weather forecasting for years—which only goes to show that machine learning is not infallible. But weather is not the only thing it can predict: models can also analyze traffic, the stock market, and even global supply chain networks!
Concepts of Machine Learning
Machine learning is a large field with many different concepts for any aspiring engineer to learn, understand, and master. A few basic concepts are model evaluation, feature engineering, fitting, and bias-variance tradeoff. Let’s go over each one:
Model Evaluation: once a model is trained, a machine learning engineer must evaluate whether the model performs well on unseen data. To evaluate a model, one might use metrics like accuracy, precision, and a receiver operating characteristic (ROC) curve.
Feature Engineering: a difficult skill to learn, feature engineering is the process of selecting input variables (i.e. features) to improve the performance of models. Knowing which features will be the most important for a problem requires domain knowledge and a deep understanding of the raw data.
Fitting: a good model fits the data well—but this doesn’t always happen. Machine learning engineers have to be constantly looking out for underfitting and overfitting. Underfitting is when a model does not react well to subtle changes in the data while overfitting is when a model over-reacts to changes. Both cases result in a model that will not perform well when applied on new data.
Bias-variance tradeoff: this concept is related to underfitting and overfitting. A model that underfits the data will have high bias and may fail to capture real patterns in the data; a model that overfits the data will have high variance and may perceive a pattern where there is only noise.
Machine Learning Algorithms
A machine learning algorithm is the mathematical procedure used to train a machine learning model. Over time, researchers have created several different categories of algorithms that are well suited for solving different kinds of problems. Let’s go over unsupervised learning, supervised learning, reinforcement learning, and deep learning:
Unsupervised learning: as the name implies, this is a method where the machine learning algorithms train on data without any human supervision. Unsupervised learning is generally used to find hidden patterns in data and is associated with tasks such as clustering or anomaly detection; algorithms in this category include decision trees, random forests, and k-nearest-neighbors.
Supervised learning: This method is different from unsupervised learning in the sense that each input datum has an associated output label. The machine learning algorithm therefore learns how to map inputs to outputs and, once trained, the model can be used to predict an output given never-before-seen inputs; algorithms in this category include k-means and hierarchical clustering.
Reinforcement learning: this machine learning method involves an agent (the program) interacting with an environment. After each try, the agent receives a reward or a penalty based on how well it fared in the environment. The agent will develop and use a strategy that maximizes its rewards. This strategy is often used with training video game AIs or in the robotics space; algorithms in this category include Q-learning, policy gradient, and actor-critic.
Deep learning: inspired by the structure of the human brain, deep learning algorithms use multiple layers of nodes to extract different kinds of information from an input. Deep learning models are typically incredibly complex and considered to be a “black-box” in the sense that it may be difficult to understand how or why the model made a certain decision; algorithms in this category include convolutional neural networks and transformer models.
Impact of Machine Learning
We are only at the beginning of the machine learning revolution and yet we’re already seeing a big impact on user experience and operational efficiency. While it’s difficult to predict the full extent of the impact that machine learning will have on society, it’s clear that it will drive significant advancements in technology, make experiences ever more personalized, and automate increasingly complex tasks.
Advancements in technology
The ability to have a personal assistant in the palm of your hand, to be able to translate speech in real-time, and to take a picture of any object and have a computer tell you anything you need to know about that object are each, in their own right, incredibly impressive technological achievements. Soon, machine learning techniques will enable fully automated driving, the creation of better materials, and even the discovery of new medical treatments.
Personalized experiences
For some time now, social media apps have been using machine learning to curate content tailored to individual users. But, social media isn’t where machine learning-driven personalization ends: teachers can use machine learning to create learning plans specific to a student’s needs and doctors will soon be using machine learning to create personalized treatment plans to improve patient outcomes.
Automating Tasks
Not every task needs to be performed by a human. While some things are easier to automate than others, we can already use machine learning to automate data entry, weather forecasting, sales prediction, content curation, and even fraud detection. With many manual and time-consuming tasks able to be automated, humans can spend more time focusing on ensuring correctness, performing more complex analyses, or making more important decisions.
As you can see, the potential of machine learning technology is vast and tech companies are in need of skilled machine learning professionals who can help elevate their strategies. And luckily for you, Ironhack has just introduced our new Data Science & Machine Learning Course which will get you job-ready in just a matter of weeks.
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