Machine learning is a basic form of artificial intelligence (AI) that allows software to predict outcomes and make independent decisions without having to be programmed to perform specific tasks. This is done by programming algorithms that can collect data and analyze that data through pattern recognition capabilities. Considering the amount of data that businesses sift through to better understand their audience and to improve their ability to convert sales, machine learning algorithms are particularly helpful when it comes to marketing.
One of the reasons why machine learning has become so important in today’s world is because of how much data is available. Many of the decisions that are made and actions that are taken in almost every industry are based on the data that’s available. However, leaving decisions and actions up to individual persons can be incredibly time-consuming. Not to mention that there is an element of human error to account for. Machine learning allows software programs to adapt to the data they collect and change course when needed based on predictive analysis. The benefit is more accurate results by eliminating the element of human error (and also reduces the time and resources that were once needed to analyze the data and make decisions and actions based on that data).
Examples of machine learning are everywhere. Google’s self driving car might be the most obvious form (and one of the most advanced forms) of machine learning. Even commonly used services, such as Amazon and Netflix, use machine learning to determine what to recommend to users based on their user data (such as past purchasing/viewing history).
While understanding the basic concept of machine learning isn’t that difficult, there are several different kinds of machine learning algorithms that use different types of machine learning methods.
Machine learning algorithms are categorized in these three methods:
Algorithms using the supervised learning method function by predicting future events using historical data. To do this, the algorithm is trained using labeled examples where outputs to a set of inputs are already known. The algorithm is able to learn by comparing its actual output to those of the labeled examples, allowing it to identify errors. Supervised learning algorithms use patterns to predict the values of the label on additional unlabeled data using a variety of methods, including prediction, regression, classification, and gradient boosting.
Insurance companies often use supervised learning algorithms to predict when a customer is most likely to file a claim. Credit card companies also use such algorithms as a way to predict when credit card transactions are likely to be fraudulent.
With the unsupervised learning method, the algorithm is not provided with labeled examples, which means that it must figure out what’s being shown on its own. Essentially, it must explore the available data and identify some structure within that data. For example, an unsupervised algorithm might be tasked with exploring a set of customer data to find different segments of customers with similar attributes from that data. This type of learning method is often used on transactional data.
Reinforcement learning involves an algorithm that learns what actions result in the best outcome through trial and error. The objective for an algorithm with such a learning method is to learn how to choose actions that will maximize the expected outcome over a given period of time.
Although there are three main types of learning methods that a machine learning algorithm can be programmed with, there are many types of machine learning algorithms in general. Some are relatively simple, while some are incredibly complex. The following are three of the more common types of machine learning algorithms:
Decision tree models use observations of certain actions to identify the optimal action (or actions) to get the desired outcome.
Neural network algorithms are deep learning models that use large amounts of training data to identify any relationship between variables. This allows the algorithm to learn how to process incoming data.
K-means clustering algorithms group a number of data points into a specific number of groupings based on similar characteristics.
Not all machine learning systems are effective. A good machine learning system will meet the following requirements:
Machine learning algorithms require complete datasets that are formatted in a specific way. As a result, for a machine learning model to be effective, raw data must be properly prepared. This is done by ensuring that there’s not a substantial amount of missing data, incomplete data, or improperly formatted data. Fortunately, there are tools available that can help with the data preparation process.
The function of the machine learning model will depend on the algorithm, which establishes the set of instructions that the model follows to perform the desired tasks. As previously mentioned, there are three main categories of machine learning algorithms: supervised learning, unsupervised learning, and reinforced learning. Choosing the right algorithm depends on what the function of the machine learning model is intended to be.
Without automation, data scientists would be needed to oversee the iterative processes (the number of times the algorithm’s parameters are updated). These processes are complicated and time-consuming as well. Using automated machine learning software, these iterative processes can be performed automatically.
A machine learning model must be able to continuously analyze new incoming data over time, which means that scalability is important since the amount of data it will have to analyze is only going to grow.
Ensemble modeling is when multiple learning models are combined to produce a strong learner. A stronger learner is produced because models that make predictions based on a collection of rules are much more robust than a model that makes predictions based on individual rules.
Let’s take a more in-depth look into how machine learning is successfully used in the real world:
Search engines use machine learning algorithms to generate SERPs (search engine results pages) that contain links relevant to their user queries. In fact, search engine algorithms use machine learning in many different ways. When combing through content across the internet, they employ pattern recognition to identify duplicate content and spam, they identify ranking signals to improve their search results, and they are able to identify similarities between the keywords used in a search query. Search engines essentially use machine learning to continuously improve their search results, ensuring that users are presented with high-quality, relevant results to their queries.
Websites that allow photos to be uploaded, specifically, social media networks such as Facebook, use machine learning to leverage photo tagging applications. For example, Facebook uses face recognition software that uses machine learning to recognize faces, food, landscapes, and more. It can identify individuals (as long as those individuals have accounts and have previously uploaded photos that have been tagged) in newly uploaded photographs and suggest the proper tags. They use the same type of machine learning software to identify photos that violate their terms of service (such as photos that contain inappropriate or offensive content).
Spam has continued to be an issue over the years. Email spam has been a problem for businesses in particular, not only because email spam has resulted in diminished productivity, spent bandwidth, and increasing maintenance costs, but also because it’s been a popular method for cyber criminals to obtain sensitive information. Filtering spam from non-spam has been a challenge, especially since users won’t want to accidentally lose important emails that may have been mistaken as spam. Machine learning is one of the most effective ways to filter spam and to determine if incoming messages are legitimate. Decision tree algorithms are most commonly used to perform this task.
Speech recognition has become vital to the function of virtual assistants, such as Amazon’s Alexa and Apple’s Siri. These programs use machine learning algorithms to recognize the user’s speech and execute verbal commands. While machine learning has allowed such programs to recognize how the sequence of words and the letters in those words are statistically related, they are limited in that there’s no innate understanding of what the words actually mean or how broader concepts may be related to those words.
AI researchers have spent decades attempting to create an algorithm that could detect pre-specified shapes. A type of machine learning (more specifically, deep learning) algorithm called CNN (convolutional neural network) has led to enormous advances in computer vision. Instead of attempting to understand an entire image, a CNN will scan it in localized regions. Early CNNs could only recognize handwritten numbers — these days, they can recognize complex 3D objects from multiple angles. Image recognition is used by Tesla for its self-driving cars.
There are still huge challenges with speech and image recognition, which means that machine learning for robotics (which involve touch and motor control) presents an even bigger challenge. For example, a robotic arm still has difficulty picking up a simple item. This is because such an action is incredibly complex, despite how simple it is for humans to do. Picking up an object requires determining how heavy that object will be, how its mass is distributed, how much friction the surface has, how much force needs to be applied, and more. Additionally, there are countless permutations of how an object could be grasped. Currently, researchers are using the reinforcement method of learning to teach their programs to learn from trial and error. This has allowed a robotic arm to learn different methods for picking up an object that are successful and that are not successful, learning from each instance.
Computers have shown the ability to use strategy for some time now. Take for example the well publicized chess games between various chess masters and super computers. These days, reinforcement learning is used to teach machine learning algorithms reasoning. Researchers are continuing to develop algorithms that can learn reasoning more effectively as this kind of information could be invaluable in a number of real world settings, including in military settings. The biggest challenge, however, is that since a reinforcement learning algorithm can only learn from outcome data, it needs a person’s input to define what that outcome should be. This means that reinforcement learning at the moment isn’t much help in strategic situations in which the outcome isn’t clear.
Machine learning applications are being developed and improved on a daily basis. It wasn’t that long ago that machine learning was merely a concept thought to be limited to science fiction. Now, machine learning is being used extensively to the point that many people don’t realize their day-to-day lives depend on it. As researchers and developers continue to work on improving machine learning algorithms, new applications will emerge that can perform tasks that were previously unheard of. Even the success of the self driving car is something that people are only just starting to wrap their heads around.
Here are a few more specific ways in which machine learning can be applied:
Predictive analytics uses new and historical data to predict activities, behaviors, and trends. Machine learning algorithms use predictive analytics for a variety of different applications. For example, Facebook’s News Feed uses machine learning to personalize each user’s feed to determine what new content will be relevant based on how they’ve engaged with content in the past (such as sharing, liking, or commenting on content).
Business Intelligence is all about collecting and analyzing data, so it should come as no surprise that machine learning plays an important part in helping businesses to automatically identify important data points. For example, human resources departments can use machine learning software to collect data on employees and to identify what the characteristics of their most effective employees are. This information can then be used to hire the best job candidates for positions that need to be filled.
CRM (customer relationship management) systems use machine learning to collect and analyze customer data to determine what actions marketing and sales personnel should take. For example, they can analyze customer emails to identify what messages the sales team need to respond to first. More advanced machine learning programs can even recommend responses that could potentially be effective.
Self-driving cars use deep learning neural networks to identify objects in the road. Using machine learning, they can not only identify on-coming objects, but they can also predict what actions the car should take to avoid a collision with those objects, like braking or steering to the left or right.
Virtual assistants, such as Siri and Alexa, have become quite popular. Besides their speech recognition capabilities, they also use machine learning to take actions based on the user’s previously defined preferences or schedule. For example, if a user regularly books restaurant reservations through Siri and then typically books an Uber to get to that restaurant, Siri will eventually pick up on the correlation and offer to book an Uber the next time the user makes a restaurant reservation.
There are many ways in which reinforcement learning is used by marketing software. For example, email automation software can use reinforcement learning by identifying the best time to send out emails. It can do this by making note of open rates for emails that are sent out and identifying at what times those open rates are highest. Eventually, it will pinpoint the worst times and best times to send emails to your email list.
Although machine learning involves data analysis just like data mining and deep learning, they are not the same. Here is a brief overview of how machine learning differs from data mining and deep learning.
Data mining is a more research-based process in which insights are extracted from the data, whereas machine learning is a process in which a program learns about the data it analyzes in order to make intelligent decisions. The data mining process focuses on the discovery of previously unknown properties, whereas machine learning focuses on learning how to make predictions based on known properties. Data mining also uses a number of different methods to gain insights from its data, including text analytics, statistical algorithms, time series analysis, and more. Data mining also involves data storage and manipulation.
Deep learning involves much larger sets of data than machine learning. Machine learning analyzes data and learns from it to make informed decisions. Deep learning creates an artificial neural network by layering algorithms. This allows deep learning software to learn and make intelligent decisions on its own. Deep learning is considered a subfield of machine learning, but some feel it is the next evolution of machine learning.
Machine learning software using the unsupervised learning method will use data mining as a process to discover existing properties to improve its ability to learn. When a machine learning model is developed, multiple datasets are typically used. These datasets include the following:
The training dataset is a set of sample data used to fit the parameters of the model. A supervised learning method is used to train the model using the training dataset, which consists of an input label and a corresponding output label. The training dataset is then used to test the model by producing a result. This result is then compared to the output label for every input label in the training dataset. Adjustments to the parameters of the model are then made based on the results of the comparison.
Once the model has been trained using the training data, it will be used to predict the responses for observations in a validation dataset, which is a dataset of examples used to tune the hyperparameters. Validation datasets are often used for regularization by early stopping, meaning that training will be stopped when the error on the validation dataset increases. When this occurs, it’s a sign that the model was overfit to the training dataset.
The testing dataset is the last dataset that is used, and provides an assessment of the final model fit on the training dataset. It does not use the training dataset; however, it does follow the same probability distribution.
Machine learning can be seen as a subset of AI, but they are not the same thing. While they certainly both use data to make more informed decisions, the goal of AI is to simulate natural intelligence to solve a complicated problem, whereas the goal of machine learning is to learn from data to improve the performance of a certain task. AI is much, much more complex than machine learning.
Machine learning is used across many organizations throughout the world. Sectors where machine learning is commonly leveraged are:
Machine learning has already proven to be incredibly useful across multiple industries, and new applications for machine learning are popping up every year. Not only is machine learning improving as a result of advancements made in research and technology, it’s also improving as a result of access to new data over longer periods of time. Machine learning models are designed to learn, which means that if they are effective, they will continue learning, making more accurate predictions the longer that they are in place. Machine learning will only continue to improve and become more important in the coming years.
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