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How to Become a Machine Learning Engineer

What Is Machine Learning?

Machine learning uses statistics to find patterns in massive amounts of data, including numbers, words, images, clicks and anything else that can be stored digitally.

Machine learning powers many of the services we rely on today, including recommendation systems like those found on Netflix, Disney+, YouTube, and Spotify. Machine learning is also key to the functionality of search engines, social-media feeds like Facebook and Twitter, and voice assistants like Siri and Alexa.

In all of these instances, these platforms use machine learning to collect as much data about you as possible — which directors you like, what links you’re clicking, which statuses have provoked a reaction from you — and then make a highly educated guess about what you might want to buy, watch, or click next. Voice assistants, meanwhile, use machine learning to surmise which words match best with the sounds coming out of your mouth.

The process is, in fact, not that complicated: find the pattern, apply the pattern. But it pretty much runs the world. That’s in big part thanks to an invention in 1986, courtesy of Geoffrey Hinton, today known as the father of deep learning.

Deep learning is machine learning taken to the next level: it gives machines an enhanced ability to find — and amplify — even the subtlest patterns. This technique is called a deep neural network — deep because it has many, many layers of simple computational nodes that work together to munch through data and deliver a final result in the form of the prediction.

How Does Machine Learning Work?

Machine learning is a form of artificial intelligence (AI) that teaches computers to learn and improve upon past experiences and it works by exploring data and identifying patterns with minimal human intervention.

Almost any task that can be completed with a data-defined pattern or set of rules can be automated with machine learning. This gives companies the opportunity to transform processes that were previously only possible for humans to perform — for example, customer service calls, bookkeeping, or reviewing resumes.

Machine learning uses two main techniques:

  • Supervised learning allows you to collect data or produce a data output from a previous machine learning deployment. Supervised learning actually works in much the same way humans learn. In supervised tasks, we give the computer with a collection of labeled data points called a training set (for example, a set of readouts from a system of train terminals and markers where they had delays in the last three months).
  • Unsupervised learning helps you find all kinds of unknown patterns in data. In unsupervised learning, the algorithm tries to learn some inherent structure to the data with only unlabeled examples. Two common unsupervised learning tasks are clustering and dimensionality reduction.

A major part of what makes machine learning so valuable is its ability to detect what the human eye misses. Machine learning models are able to catch complex patterns that would have been overlooked during human analysis.

What Is the Difference Between AI and Machine Learning?

Artificial intelligence is the larger concept of machines having the capacity to carry out tasks in a way that we would consider “smart,” while machine learning is an application of AI based on the concept that if we give machines access to data, they are capable of learning for themselves.

Over the years, our idea of what constitutes AI has changed. Rather than increasingly complex calculations, work in the field of AI has concentrated on mimicking human decision making processes and carrying out tasks in ever more human ways.

What Is Bias in Machine Learning?

Machine learning bias is a problem that arises when an algorithm produces results that are systemically prejudiced due to incorrect assumptions in the machine learning process.

Also sometimes called algorithm bias or AI bias, machine learning bias usually occurs because the people who design and/or train the machine learning systems create algorithms that reflect unintended cognitive biases or real-life prejudices, or it’s because they’re using incomplete, faulty or prejudicial data sets to train and/or validate their machine learning systems.

What Is Overfitting in Machine Learning?

Overfitting refers to a situation in which the training data is modeled too well, meaning that a model has learned the detail and noise in the training data to an extent that it has a negative effect on the performance of the model on new data.

This means that the noise or random fluctuations in the training data are learned as concepts by the model. The problem? These concepts do not apply to new data and negatively impact the ability of the model to generalize.

Overfitting is more likely with nonparametric and nonlinear models that have more flexibility when learning a target function. That’s why many nonparametric machine learning algorithms also include parameters or techniques to limit and constrain how much detail the model learns.

What Is Regression in Machine Learning?

Regression in machine learning consists of a set of methods that allow us to predict a real or continuous outcome variable from the value of one or multiple predictor variables. A continuous output variable is a real-value, such as an integer or floating point value, and they are often quantities –– in other words, amounts and sizes.

The goal of regression models is to develop equations that define one variable as the function of another variable.

While many different models can be used, the simplest is the linear regression. It tries to fit data with the best hyper-plane which goes through the points.

What Is Regularization in Machine Learning?

Regularization in machine learning is the process that regularizes or shrinks the coefficients toward zero to prevent overfitting.

In other words, regularization discourages learning a more complex or flexible model.

The basic idea is to penalize the complex models – for instance, adding a complexity term that would give a bigger loss for complex models.

What Is Cross Validation in Machine Learning?

Cross-validation is a technique for evaluating machine learning models by training several machine learning models on subsets of the available input data and evaluating them on the complementary subset of the data. Cross-validation is used to detect overfitting.

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