Machine Learning – ML basics, benefits and challenges

Machine learning types

Machine learning is often categorized by the way an algorithm learns to improve the accuracy of its predictions. The four basic types of machine learning are:

• supervised machine learning
• unsupervised machine learning
• semi-supervised learning
• reinforcement learning

However, many machine learning algorithms and techniques are not limited to just one form of learning. They are often adapted to multiple types depending on the dataset and the problem to be solved.

Supervised learning

In supervised learning, algorithms are given labeled training data and variables are defined for the algorithm to evaluate to create correlations. Both the inputs and outputs of the algorithm are specified.

Supervised learning algorithms are applied to tasks such asbinary classification, which splits data into two categories,multiclass classification, which selects from multiple response types,ensemble learning, which combines the predictions of multiple ML models to achieve more accurate results, and regressionmodeling, which predicts continuous values based on relationships in datasets.

Some of the most popular algorithms for learning with a tutor include the support vector method, neural networks (sometimes referred to as deep machine learning), Bayesian classifier, decision trees, etc.

Unsupervised learning

Most machine learning algorithms initially worked with supervised learning, but unsupervised approaches are becoming increasingly popular.

Unsupervised learning algorithms do not require labeled data. They go through the unlabeled data and look for patterns that can be used to group data points into subsets.

Unsupervised learning algorithms do not require are used to:

• Clustering, which divides a dataset into groups based on similarity.
• Anomaly detection, which identifies unusual data points in datasets.
• Association rule mining.
• Discovery of sets of items that often occur together.
• Dimensionality reduction, which reduces the number of variables in datasets.

Among the most widely used algorithms for clustering is K-means, for association it is the Apriori algorithm for association rule generation.

Semi-supervised learning

Semi-supervised learning works by training the algorithm on a small amount of labeled data, from which it learns features of the dataset, which it then applies to new unlabeled data. The performance of algorithms usually improves when trained on labeled datasets, but labeling data can be time-consuming and expensive.

This type of machine learning strikes a balance between the performance of supervised learning and the efficiency of unsupervised learning. Semi-supervised learning can be used in areas such as learning algorithms to translate languages based on an incomplete dictionary, detecting deception when there are only a few positive examples, and learning from small training datasets how to apply labels to larger datasets.

Reinforcement learning

Reinforcement learning works by programming an algorithm with a specific goal and set of rules to achieve it. The algorithm can be set up to gain rewards for actions that help it reach the goal, and avoid punishments for actions that take it away from the goal.

Reinforcement learning is often used in training bots to perform tasks, teaching AI bots to play computer games, and helping companies solve complex resource allocation problems. A model example for reinforcement learning is the game of chess, where we create an agent, define its allowed moves and a rule for winning. We reward him if he discards his opponent’s piece or wins, punish him if his piece is discarded or loses.

Dataset

A dataset is a set of training data from which the model learns to identify patterns and relationships in the data. A dataset always consists of two groups of features – attributes (features) and labels (labels).

Features are the properties of the object being investigated. These features do not have to be all listed in the dataset, only those that can reliably distinguish objects from each other are sufficient.
Labels are labels of object properties, or the resulting model values. These labels are known in the dataset so that the model can learn to estimate the results. However, real data does not have labels and the program must be able to compute them.

An example would be the identification of fruit based on its shape, colour and size. Features or characteristics would be: shape – round , colour – red, size – medium, weight – 200g. The label would be an apple. The shape would be able to distinguish it from, for example, a banana, the colour from an orange, and the size and weight from a strawberry. With enough training on a lot of good quality data, the model would be able to identify the fruit given these four attributes.

The process of creating a model in machine learning

Developing the right machine learning model to solve a business problem requires thoroughness, experimentation and creativity. We offer you seven steps to create an effective model:

1. First you need to understand the business problem and define the criteria for success. The goal is to transform the knowledge of the problem and project goals into a suitable problem definition for the machine learning model.
2. It is necessary to be able to identify the structure of the necessary data. Determine what data and how much of it is needed to build the model and whether the data is in a state to be processed by the model.
3. We need to collect and prepare data to train the model. Often we need to clean and label the data first, replace incorrect or missing data, enrich and augment the data, reduce data noise and remove ambiguities, anonymize personal data, and split the data into training, test, and validation sets.
4. Once the data has been processed, the properties of the model can be determined and the right algorithms and machine learning techniques can be selected. The hyperparameters that drive the training process are set and adjusted, and the model is trained, validated, and optimized.
5. After training the model, we need to evaluate the performance of the model and set benchmarks. This work includes calculations of the so-called confusion matrix, key performance indicators, machine learning metrics, and model quality measurements to analyze performance and determine if the model can meet the business objectives.
6. Now the model can be deployed and its performance monitored in production. Once deployed, the model is continuously monitored and iterated as needed to improve its performance.
7. Even if the model is functional, we recommend to continuously modify and improve the model. Even after the model is deployed, work continues. Business requirements, technology capabilities, and real-world data can change unexpectedly, which can create new requirements that require adjustments to the model.

Application areas of machine learning

In addition to being widely applicable across a variety of industries, machine learning is an integral part of the software that drives organizations. Here are a few examples of how different business disciplines and software tools use machine learning:

Business intelligence

BI tools and predictive analytics software use machine learning algorithms to identify significant data points, patterns and anomalies in large datasets.

Human resources (HR)

Machine learning models built into HR information systems make it easier to select candidates by filtering job applications and identifying the best candidates for open positions.

Customer relationship management (CRM)

Key applications in CRM software include analyzing customer data for customer segmentation, predicting purchasing habits, recommending products, setting prices, optimizing email campaigns, providing support via chatbots, and detecting fraudulent transactions.

Security and compliance

Advanced algorithms identify anomalies in network behaviour, which is key in detecting potential cyber attacks.

Supply chain management

Machine learning techniques optimize inventory levels, streamline logistics, improve supplier selection, and proactively address supply chain disruptions.

Natural language processing

Machine learning models allow virtual assistants such as Alexa, Google Assistant and Siri to interpret and respond to human language.

Prerequisites for successful machine learning – data, data, data

Machine learning is an excellent tool for solving many problems, improving business operations and automating tasks, but it also brings its own challenges and pitfalls:

Sources

Machine learning is a complex process that requires deep expertise and significant material and financial resources. Machine learning projects are led by highly paid data scientists and use expensive hardware and software, which increases the cost of developing, tuning and running ML models.

Accuracy and data volume

The algorithms are trained on datasets that are incomplete and can certainly contain errors, which can then lead to inaccurate models. Therefore, a prerequisite for successful ML is a good quality dataset representing the widest range of possibilities.

Data analysis and interpretation of results

The results can be difficult to understand, especially those produced by complex algorithms such as neural networks used in deep learning, an advanced form of ML.

Prerequisites for learning machine learning

If you are intrigued by the fascinating world of ML, the following requirements are useful for further study and experimentation with machine learning:

1. Basic knowledge of programming languages, especially Python, which is most commonly used in the ML world.
2. Advanced knowledge of mathematics, especially statistics and probability.
3. Basic knowledge of linear algebra.
4. Knowledge of how to clean and edit raw data into the desired format.
5. Of course, the key is to have powerful hardware to process and train the data.

Machine learning is a key factor for progress

Machine learning has driven many innovations and transformations across many industries, from healthcare to transportation, finance to manufacturing. Modern hardware has made it possible to process and analyse even more data (data) in parallel than ever before, further accelerating the research being done by universities and companies around the world and bringing us a new era of artificial intelligence and autonomous cars.

Machine Learning provides unprecedented opportunities for organisations to improve their performance, predict trends, gain a competitive advantage over competitors and respond to changing market and customer needs. In the future, we can expect Machine Learning to push the boundaries of what is technologically possible even further and contribute to creating a smarter and more efficient world where automation and human creativity complement each other harmoniously.