What Is Machine Learning?

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Whether we know it or not, we’ve probably been taking advantage of the benefits of machine learning for years. Most of us would find it hard to go a full day without using at least one app or web service driven by machine learning. But what is machine learning? From self-driving cars, fraud detection, online recommendation offers to translate speech, machine learning definition as elaborated below, is driving an explosion in the capabilities of artificial intelligence – helping software make sense of the messy and unpredictable real world.

What Is Machine Learning?

Machine learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience and make accurate predictions when fed data without being explicitly programmed. From this Machine Learning definition,  Machine learning techniques leverage data mining to identify historic trends to inform future models.

Machine learning predictions could range from detecting whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether an email is a spam, etc. The key difference from traditional computer software is that a human developer hasn’t written code that instructs the system how to tell the difference between the banana and the apple. But rather a machine-learning model has been taught how to reliably discriminate between the fruits by being trained on a large amount of data.

Types Of Machine Learning

Machine learning is generally split into two main categories: supervised and unsupervised learning. But we have other types.

Supervised Learning

Supervised learning algorithms are trained using labelled examples, such as an input where the desired output is known. For example, a piece of equipment could have data points labelled either “F” (failed) or “R” (runs). The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors. It then modifies the model accordingly. Examples of ML used in this category are:

  • Classification methods, which predict the output of a given data sample when the output variable is categorical, for instance, dead or alive, sick or healthy.
  • Regression methods, which predict output variables that are real values, such as the age of a person or the amount of snowfall.
  • Ensemble methods, which combine predictions from weaker algorithmic output to predict the new output.

Unsupervised Learning

In contrast, unsupervised learning tasks algorithms with identifying patterns in data, trying to spot similarities that split that data into categories. These methods use only input variables (X), not output variables, and rely on unlabeled training data sets to map the underlying structure of the data. Common examples include:

  • Association methods, which uncover the probability of items in a collection, as in market-basket analysis.
  • Clustering methods, which group samples of objects based on similarity

Semi-Supervised Learning

As the name suggests, the approach mixes supervised and unsupervised learning. The technique relies upon using a small amount of labelled data and a large amount of unlabeled data to train systems.

Reinforcement Learning

These methods allow the user or other designated agent to decide the best next action, based on the current state and learned behaviours that maximize the rewards. This approach is often used in robotics.

Machine Learning Algorithms

Deciding which algorithm to use is very tricky, it is more often an art than a science. That’s because your data set can vary widely, in size, quality, and nature. Choosing an algorithm is also limited to your access to computational power, the urgency of your task, and the ultimate goal. However, below is a list of commonly used ML algorithms;

  • Linear regression
  • Logical regression
  • Classification and regression trees(CART)
  • Naïve Bayes
  • K-nearest neighbour (KNN)
  • Apriori
  • K-means

Importance Of Machine Learning

 Machine learning and data mining, a component of machine learning, are crucial tools in the process to glean insights from massive datasets held by companies and researchers today. There are two main reasons for this:

The scale of data: Companies are faced with massive volumes and varieties of data that need to be processed. Processing power is more efficient and readily available.

Unexpected findings: Since machine learning algorithms update autonomously, the analytical accuracy improves with each run as it teaches itself from the datasets it analyzes.

Applications Of Machine Learning

Companies leveraging algorithms to sort through data and optimize business operations aren’t new. Leveraging algorithms extends not only to digital business models such as web services or apps but also to any company or industry where data can be gathered, according to SAS Insights, including the following:

Marketing and sales

Financial services: Banks use it to identify important insights in data, and prevent fraud.

Brick-and-mortar retail: in product recommendation to previous customers,

Healthcare: the advent of wearable devices and sensors that can use data to assess a patient’s health in real time.

Transportation: Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems

Oil and gas: Finding new energy sources.

Government: agencies like public safety use machine learning to help detect fraud and minimize identity theft.

Enterprise HR: to hire ideal employees, predict risks and track absenteeism..

How To Evaluate Machine-Learning Models

Once training of the model is complete, the model is evaluated using the remaining data that wasn’t used during training, helping to gauge its real-world performance. To further improve performance, training parameters can be tuned. An example might be altering the extent to which the “weights” are altered at each step in the training process.

How To Prepare For A Machine Learning Career

There are a wide variety of software frameworks for getting started with training and running machine-learning models, typically for the programming languages Python, R, C++, Java and MATLAB. You can also become an ML engineer while you work through online training courses which come with a customised program set.

Famous examples include Google’s TensorFlow, the open-source library Keras, the Python library Scikit-learn, the deep-learning framework CAFFE and the machine-learning library Torch.

Available Jobs For Machine Learning Engineers

All of the major cloud platforms: Amazon Web Services, Microsoft Azure and Google Cloud Platform provide access to the hardware needed to train and run machine-learning models, with Google letting Cloud Platform users test out its Tensor Processing Units.

Machine Learning (ML) is an emergent innovative technology which has come to facilitate life for humans in the domain of automation, however; Machine Learning should not scare us as it has not come to replace humans but to accompany humans in their pursuit for better productivity and output.

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Machine Learning Algorithms