The world is abundant with data: images, documents, social media accounts, websites, audio files, and videos. All these data pieces are generated by people, computers, phones, tablets, IoT, and other devices. The amount of data is rapidly increasing and can be dealt with only if we bring Machine Learning (ML) into the picture.

When the amount of data was surmountable, people used to do the greater part of data analysis on their PCs (basically, using Excel sheets) to create data patterns right off the bat. As the amount of data is gradually growing, this approach becomes less and less efficient (and totally useless when we think Big Data), and we have to turn to complex, ML-empowered computer systems for help.

In this post, I will let you in on the specifics of those computer systems. In particular, I will walk you through the basics of ML to find out what is Machine Learning. Then, we will explore how ML-powered systems work and how they collect, process, and manage data. And finally, we will deep dive into specific cases of ML utilization in business.

Let’s dive in, then!

What Is Machine Learning?

Machine learning is a subset of artificial intelligence (AI) that allows a computer to learn without the need for direct programming.

In the meantime, AI is a field of computer science that explores and implements specific capabilities of the machines to perform tasks that require human-like intelligence. Basically, AI is aimed at helping machines to mimic and copy cognitive functions that humans demonstrate.

The AI industry is on the unstoppable rise. According to ABIresearch report, the CAGR for AI’s Enterprise Adoption is projected to hit 162%, from 2017 to 2021. 75% of commercial enterprises will use AI by 2021.

Machine learning, which is often associated with AI, is a natural part of AI adoption in business. You can cost-efficiently train a computer system to perform tasks that humans are not good at (e.g. calculation at scale). Rather than giving specific commands for the computer to perform separate actions, ML enables the computer to independently find a solution by using data for self-study.

The more data the computer has access to, the more efficiently it learns and the more intelligent it becomes (in theory). It improves its own accuracy and performance over time.

All of that is hardly a science fiction. We routinely come across machine learning in everyday life: Spotify or Apple Music offer us personalized playlists; personal assistants like Siri or Alexa collect our speech patterns; Tinder and Twitter select the best image for posts and profiles. Google trains its ML system by letting us choose correct images in Captcha using image recognition.

How Machines Work, Exactly

Machine Learning is not easy. Yet, if you are going to reinforce your business with a few of its elements, or become a data scientists (data scientists will not be replaced by automation), it makes sense to grind through the basics as soon as possible. And this all starts with how ML-powered computer systems work (i.e. collect, clean, train, and enhance data for further use).

1. Data Collection

Machine learning is all about data. Data scientists should always be on the lookout for more data. However, they need high-quality data; that is, data that is relevant to the task they are trying to complete. For sure they should also assess their capabilities for data collection, diligently analyze its source, and require the format.

2. Data Cleansing

Data can be generated from a wide variety of sources, in various formats and types. Generally it cannot be “fed” to the machine until properly processed and structured aka cleansed.

So the job of a data scientist here is to remove the irrelevant information. The quality and credibility of the results (i.e. how ML model works) are impacted by how well the database has been prepared.

Once the data is cleansed, it is usually divided into two groups: the first one will be used by the algorithm (training set), and the second one for its evaluation (testing set).

3. Data Training

Data training is a process of “teaching” the machine how to complete this or that task with a data scientist’s assistance. Specific tasks are performed one by one while the machine follows the commands.

It it worth noting that data training methods differ based on the type of model you choose. For instance, you approach will be completely different if you try to build regression lines in a linear model or to generate a decision tree using a specific.

In simplified terms, the training looks like this: The algorithm take a data piece, processes it, measures the processing efficiency, and automatically adjusts its parameters (backpropagation method) until it can produce the specified result with required accuracy.

When the algorithm works off well on training data set, test its efficiency using unprepared data. The algorithm may require additional adjustments. Implement them to avoid a complete training overhaul, or retraining. Should your algorithm work well only with pre-trained but cannot manage unprepared data pieces, you have done a bad job with it.

4. Data Enhancement/Augmentation

Finally, the processed, or “cooked” data gets integrated into the application, code or system. The final result has to be optimized by a data scientist so that it does not overload the core, overburdens the memory, and operates rapidly overall.

Major Machine Learning Models

Supervised Learning

Supervised learning algorithms generate predictions and make forecasts using pre-processes scripts and real-world examples.

For instance, stock prices can be predicted by analyzing the data of previous stock price changes. The determinant here is stock values. The controlled learning algorithm looks through and finds out specific patterns in these values. It uses relevant information: day, month, the company’s financial state, industry, politically important events, news feed, and more. Then each algorithm analyzes different data types that positively and negatively influence the prices. Finally, it generates the best pattern and predicts unallocated test statistics — future stock prices.

Machine Learning Algorithms for Supervised Learning

Classification. Classification predicts categories. You tag the data with features and categories. The algorithm learns to define information based on these tags. Classification is used for classifying emails (e.g. Spam, Ads, Social), recognizing figures, characters, images, analyzing music preferences to suggest a playlist, and searching for suspicious bank transactions.

Regression. This one is more analytics- and finance-oriented. It is a sort of classification, but with figures. It is used to analyze the number of traffic jams in the city, price intelligence, and demand research. It is displayed as a scale with line and a lot of spots around it, where the spots are customers, and the line shows demand. The algorithm calculates the distance between the dots to come up with a prediction; for example, what time is best to sell new smartphones.

Abnormalities detection. ML can be used to identify unusual patterns in data. Say, the card fraud has happened. The ML system has to find out which actions are suspicious and which are not. The number of abnormal situations can be counted dozens. The machine should rely on the programmed log of normal activities, their amount is much smaller. It simplifies the identification of abnormal situations.

A popular example of supervised ML is decision trees. Banks use it to help the manager decide whether to provide a loan or not. The machine analyzes the data of previous clients, programmed scripts and commands, the data of the current client and provides the manager with advice.

Decision trees are many, but the following three types are the most important:

  1. Data Description. Decision trees store information about data in a compact form; you can store a decision tree that contains an exact description of the objects.
  2. Classification. Decision trees are good at assigning objects to one of the previously known classes. The variable of the target should have discrete values.
  3. Regression. If the target variable includes continuous values, decision trees allow to set the dependence of the target variable on independent (input) variables. Numerical prediction (prediction of values of the target variable) belong to this class.

Unsupervised Learning

Unsupervised learning classifies objects by an unknown attribute. The machine come up with the best option on its own.

The algorithm in the learning process does not feature any predetermined answers. Its goal is to find semantic connections between individual data and to identify patterns. Clustering, as a type of unsupervised learning, is oftentimes used in product recommendations systems (“also bought with this item”).

The unsupervised learning algorithm strives to place data in a specific order, or describe its structure. This may mean grouping data into clusters or searching for various ways to analyze complex data to simplify or improve how it is organized.

Machine Learning Algorithms for Unsupervised Learning

Clustering. Clustering is also a classification, but without any known classes. The algorithm searches for similar objects and classifies them into clusters. The number of clusters can be set in advance or entrusted to the machine. The machine determines the similarity of objects using pre-assigned features. Objects that have a lot of similar characteristics are grouped into one class.

Clustering is used for market segmentation (types of customers, loyalty), image compression, analysis and markup of the new data, and detection of abnormal behavior.

Dimension Reduction. Data scientists can teach the machines to handle abstractions.

Let’s say you set the parameters: a big square with a circle inside, and a little display on the upper part of the square. The machine learning algorithms (image recognition) will figure out that it is a washing machine, but it would not report the features or manufacturer.

This algorithm is used for topic modeling to find out what topics, for example in the article, or document, are covered. That is how latent semantic analysis has appeared. It defines the topic by analyzing the amount of specific words or word combinations. In doing so, it can differentiate between an article about ML from an article about celebrities.

This type of training combines clustering and dimension reduction. Companies use it for tasks with a higher level of complexity because it requires an interaction with the IoT environment. The machine gets data from IoT environment and then learns from it.

The application of this method is extensive: from controlling robotic hands and finding the most effective combination of movements to developing robot navigation systems, where the “avoid collision” behavioral algorithm is learned empirically by receiving feedback when facing an obstacle.

This training method is also often used in logistics, scheduling, and tactical task planning.

The Examples of Machine Learning in Business

Before implementing ML in business, it makes sense to collect and analyze all the data about previous activities. This process is common among enterprises. For example, they analyze sales and advertising projects to determine their results and profitability before deep diving into machine learning. This is done to avoid feeding incorrect or entirely false data to ML algorithms.

Then, they usually approach forecasting. Collecting data and using it to predict a specific result allows businesses to improve the workflow and simplify decision-making:

  • Atomwise involved machine learning predictive models to reduce drug manufacturing time
  • Deep6 Analytics searches for suitable by criteria patients for clinical trials

The next one is recommendations:

  • E-commerce startups Lyst and Trunk Archive have implemented ML to offer relevant and high-quality content to their users
  • Apple displays relevant apps to users in the App Store
  • Intuit offers a relevant FAQ page when a user searches for a tax return form

Machine learning can be used in logistics and production:

  • Rethink Robotics uses machine learning to train their robots to be more human and to increase production speed
  • JaybridgeRobotics automates the industrial-level vehicles to make them work more efficiently

Machine Learning Is Here to Stay. Are You on Board?

Conversations about artificial intelligence usually result in heated debates about the future of the job market. Yet, AI is mostly about future, while its natural component – machine learning – is already used almost at any site. Lacking limitations, its can be successfully implemented in many areas and industries.

Machine learning is driven forward by such corporations like Amazon, IBM, and Microsoft. They have become the main providers of cloud ML environments. Cutting-edge startups not only follow their lead, but often offer new ways of using ML to empower businesses and customers.

Companies are looking forward to implementing ML in their organizations because it provides them with a competitive edge.

Not sure? Just look at your “also bought with this item” section. Machines do a better job of offering the stuff you need much better than humans would. Also, do not forget to thank intelligent robots when they save you from a credit card fraud.

Machine learning is here to stay. And you should decide now whether you opt for Machine Learning consulting services or lose to your competition.