Clearly Explained: 4 types of Machine learning algorithms

Definitions, purpose, popular algorithms and use cases- all explained

Machine Learning has come a long way from being just a science fiction subject to being the most reliable and diverse business tool that enhances the multi-facets of every business operation.

Its influence on the performances of various businesses has become so significant that the implementation of top-notch machine learning algorithms is required to ensure the survival of many industries in this highly competitive world.

The implementation of ML into business operations requires a significant amount of resources and is a strategic step. Therefore, now that we understand WHY machine learning algorithms are important, the next step is to understand HOW you can use ML algorithms to your rescue.

The first step towards this is to clearly understand what is the business problem that you would like to solve using machine learning and to clearly understand the amount of resources and efforts required in different kinds of machine learning algorithms, for you to be able to choose the best algorithm out of the many that are on the table.

In this post, we’ll cover the major types of machine learning algorithms, explain the purpose of each of them, and see what their benefits are.

Types of Machine Learning Algorithms

1. Supervised Machine Learning Algorithms

Supervised Learning Algorithms are the easiest of all the four types of ML algorithms. These algorithms require the direct supervision of the model developer. In this case, the developer labels the sample data corpus and sets strict boundaries upon which the algorithm will operate.

It is a spoonfed version of machine learning:

• you select what kind of information output (samples) to “feed” the algorithm
• you determine what kind of results are desired (for example “yes/no” or “true/false” or “the value of sales/net credit loss/house price” etc )

From the machine’s point of view, this process is more or less a “connect the dots” routine.

The primary purpose of supervised learning is to scale the scope of data and to make predictions of unavailable, future or unseen data based on labeled sample data.

Supervised machine learning includes two major processes: classification and regression.

• Classification is the process of learning from past data samples and manually train the model to predict the essentially binary outcomes (yes/no, true/false, 0/1) . For example: whether a customer will be attrited in the coming 9 months or not, whether someone has cancer or not etc. The classification algorithm recognizes certain types of objects and categorizes them accordingly to predict one of the two possible outcomes.
• Regression is the process of identifying patterns and calculating the predictions of continuous outcomes . For example: predicting the house rates or next month’s sales forecast etc.

The most widely used supervised algorithms are:

• Linear Regression;
• Logistical Regression;
• Random Forest;
• Gradient Boosted Trees;
• Support Vector Machines (SVM);
• Neural Networks;
• Decision Trees;
• Naive Bayes;
• Nearest Neighbor.

Supervised Learning Algorithms Use Cases

The most common fields of use for these algorithms are price prediction and trend forecasting in sales, retail commerce, and stock trading. These algorithms use incoming data to assess the possibility and calculate possible outcomes.

2. Unsupervised Machine Learning Algorithms

Unsupervised learning algorithms do not involve direct control from the developer. The main point of the requirement for supervised machine learning is that we should know the results beforehand for the past data to be able to predict the results on unseen data but in case of unsupervised machine learning algorithms the desired results are unknown and yet to be defined .

There are times when you don’t want to exactly predict an Outcome. you just want to perform a segmentation or clustering. For example- A bank would want to have segmentation of its customers to understand their behavior. This business problem requires the use of Unsupervised machine learning algorithms as there are no specific outcomes being predicted here.

Another big difference between the two is that supervised learning uses labeled data exclusively, while unsupervised learning feeds on unlabeled data.

The unsupervised machine learning algorithm is used for:

• exploring the structure of the information;
• extracting valuable insights;
• detecting patterns;
• implementing this into its operation to increase efficiency.

In other words, unsupervised machine learning attempts to describe the information supplied to it by sifting through it and making sense of it.

Unsupervised learning algorithms apply the following techniques to describe the data:

• Clustering: It is an exploration of data used to segment it into meaningful groups (i.e., clusters) based on their internal patterns without any prior knowledge of group credentials. The credentials are defined by similarity of individual data objects and also aspects of its dissimilarity from the rest (which can also be used to detect anomalies).
• Dimensionality reduction: Most of the time, there is a lot of noise in the incoming data. Machine learning algorithms use dimensionality reduction to remove this noise while distilling the relevant information.

The most widely used unsupervised algorithms are:

• K-means clustering;
• t-SNE (t-Distributed Stochastic Neighbor Embedding);
• PCA (Principal Component Analysis);
• Association rule.

Unsupervised Learning Algorithms Use Cases

Digital marketing (used to identify target audience groups based on certain credentials — it can be behavioral data, elements of personal data, specific software setting or else) and ad-tech (This algorithm can be used to develop more efficient targeting of ad content and also for identifying patterns in the campaign performance) are the main industries that employ unsupervised learning algorithms. These are also used where customer information exploration and adjustment of relevant services is required.

3. Semi-supervised Machine Learning Algorithms

Semi-supervised learning algorithms represent a middle ground between supervised and unsupervised algorithms. In essence, the semi-supervised model combines some aspects of both into a thing of its own.

Let’s understand how semi-supervised algorithms work:

1. A semi-supervised machine-learning algorithm uses a limited set of labeled sample data to shape the requirements of the operation (i.e., train itself).
2. The limitation results in a partially trained model that later gets the task to label the unlabeled data. Due to the limitations of the sample data set, the results are considered pseudo-labeled data .
3. Finally, labeled and pseudo-labeled data sets are combined , which creates a distinct algorithm that combines descriptive and predictive aspects of supervised and unsupervised learning.

Semi-supervised learning uses the classification process to identify data assets and clustering process to group it into distinct parts.

Semi-supervised Learning Algorithms Use Cases

Legal and Healthcare industries, among others, manage web content classification, image, and speech analysis with the help of semi-supervised learning.

In the case of web content classification, semi-supervised learning is applied for crawling engines and content aggregation systems. In both cases, it uses a wide array of labels to analyze content and arrange it in specific configurations. However, this procedure usually requires human input for further classification.

In the case of image and speech analysis, an algorithm performs labeling to provide a viable image or speech analytic model with coherent transcription based on a sample corpus. For example, it can be an MRI or CT scan. With a small set of exemplary scans, it is possible to provide a coherent model that can identify anomalies in the images.

4. Reinforcement Machine Learning Algorithms

Reinforcement learning is commonly understood as machine learning artificial intelligence.

Essentially, reinforcement learning is all about developing a self-sustained system that, throughout contiguous sequences of tries and fails, improves itself based on the combination labeled data and interactions with the incoming data.

Reinforced ML employs a technique called exploration/exploitation . The mechanics are simple — the action takes place, the consequences are observed, and the next action considers the results of the first action. It’s an iterative algorithm.

Using this algorithm, the machine is trained to make specific decisions. It works this way: The machine is exposed to an environment where it trains itself continually using trial and error. The machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions.

Most common reinforcement learning algorithms include:

• Q-Learning;
• Temporal Difference (TD);
• Monte-Carlo Tree Search (MCTS);
• Asynchronous Actor-Critic Agents (A3C).

Reinforcement Learning Algorithms Use Cases

Reinforcement Machine Learning fits for instances of limited or inconsistent information available. In this case, an algorithm can form its operating procedures based on interactions with data and relevant processes.

Modern NPCs and other video games use this type of machine learning model a lot. Reinforcement Learning provides flexibility to the AI reactions to the player’s action thus providing viable challenges. For example, the collision detection feature uses this type of ML algorithm for the moving vehicles.

Self-driving cars also rely on reinforced learning algorithms as well. For example, if the self-driving car ( Waymo , for instance) detects the road turn to the left — it may activate the “turn left” scenario and so on.

The most famous example of this variation of reinforcement learning is AlphaGo that went head to head with the second-best Go player in the world and outplayed him by calculating the sequences of actions out of the current board position.

On the other hand, Marketing and Ad Tech operations also use Reinforcement Learning. This type of machine learning algorithm can make retargeting operation much more flexible and efficient in delivering conversion by closely adapting to the user’s behavior and surrounding context.

Also, Reinforcement learning is used to amplify and adjust natural language processing ( NLP ) and dialogue generation for chatbots to:

• mimic the style of an input message
• develop more engaging, informative kinds of responses
• find relevant responses according to the user reaction.

In summary, the most important ML algorithm types to remember are Supervised, Unsupervised, and Reinforcement.

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