Learn 5 Types of Machine Learning- Its Pros and Cons and Applications for Beginners

Machine learning is a subfield of artificial intelligence (AI) that focuses on developing algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed.

There are several types of machine learning, each with its own advantages and disadvantages. In this article, we will explore five core machine learning types: supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning. Each of these machine learning techniques are applied to various real-world problems/use cases, showcasing their versatility and utility across a wide range of domains and industries as shared below.

1. Supervised Learning:

The foundation of supervised machine learning is supervision or observation. In the supervised learning approach, we train the machines using the “labelled” dataset, and then the machine predicts the output based on the training. Here, the labelled data indicates which inputs have already been mapped to which output.

An easy way to understand it is that after training the machine with specific input and related output, we ask it to predict the outcome using test dataset. To map the input variable (x) with the output variable (y), the basic objective of the supervised learning approach.

Real world application of Supervised Learning:

Email Spam Detection:

Supervised learning algorithms can be used to classify emails as spam or not spam based on labeled examples of spam and non-spam emails.

Image Classification:

Supervised learning is employed in image recognition systems, such as identifying objects in photos or categorizing handwritten digits.

Medical Diagnosis:

Supervised learning models can assist in diagnosing diseases based on patient data and medical records.

Let’s now learn the pros and cons aboutSupervised Learning

Pros:

• Well-suited for tasks with labelled data, such as classification and regression.
• Can make accurate predictions when given enough high-quality data.
• Offers interpretability, as the model learns from labelled examples.

Cons:

• Requires a large amount of labelled data for training.
• May not perform well on tasks with noisy or incomplete data.
• Limited in cases where obtaining labelled data is costly or time-consuming.

2. Unsupervised Learning:

Unlike Supervised learning, the Unsupervised learning method need no supervision or observation/regulation. In unsupervised machine learning, the system is trained on an unlabelled dataset which makes output predictions without any human supervision.

In other words, unsupervised learning, the models are trained on data that has neither been categorized nor labelled, and they are then allowed to behave autonomously on that data.

Example, we feed the machine learning model with vegetable pictures. The model has no prior knowledge of the photos, and its job is to identify patterns and groups of items. As a result, when the machine is tested with the test dataset, it will now learn its patterns and distinctions, such as color differences and form differences, and anticipate the output.

Real world application of Unsupervised Learning:

Customer Segmentation:

Unsupervised learning can be used to group customers with similar purchasing behavior, helping businesses tailor marketing strategies.

Anomaly Detection:

Unsupervised techniques can identify unusual patterns in data, such as fraud detection in financial transactions or network intrusion detection.

Recommendation Systems:

Unsupervised learning is behind collaborative filtering methods used in recommendation engines for movies, products, or music.

Let’s now learn the pros and cons about Unsupervised Learning:

Pros:

• Useful for uncovering hidden patterns or structures in data.
• Doesn’t require labelled data, making it applicable to a broader range of problems.
• Can be used for tasks like clustering and dimensionality reduction.

Cons:

• Results may be less interpretable compared to supervised learning.
• Performance evaluation can be challenging, as there are no clear labels to measure accuracy.
• Prone to finding trivial or irrelevant patterns in data.

3. Semi-Supervised Learning:

To overcome the limitations of unsupervised learning and supervised learning algorithms, the concept of Semi-supervised learning is presented.

Semi-Supervised learning is a type of Machine Learning algorithm that falls between Supervised and Unsupervised machine learning. It characterizes the intermediate ground between Unsupervised learning (with no labelled training data) and Supervised (With Labelled training data) algorithms and uses the combination of unlabelled and labelled datasets during the training period.

Although Semi-supervised learning is the middle ground between unsupervised and supervised learning and operates on the data that consists of a few labels, it mostly consists of unlabelled data.  More so Semi-supervised learning is completely different from supervised and unsupervised learning as they are based on the presence & absence of labels.

Real world application of Semi-Supervised Learning:

Text Classification:

In scenarios where only a fraction of text data is labelled, semi-supervised learning can be employed for sentiment analysis or topic categorization.

Speech Recognition:

Semi-supervised techniques can improve the accuracy of speech recognition systems when there’s limited transcribed speech data.

Image Labelling:

In computer vision, semi-supervised learning can aid in labelling images when only a subset is manually labelled.

Let’s now learn the pros and cons about Semi-Supervised Learning:

Pros:

• Combines the advantages of both supervised and unsupervised learning.
• Allows for leveraging both labelled and unlabelled data, which is often more readily available.
• Can improve model performance, especially when labelled data is limited.

Cons:

• Requires careful integration of labelled and unlabelled data, which can be complex.
• Performance may still be limited by the quality and quantity of labelled data.

4. Reinforcement Learning:

With Reinforcement learning, an AI agent (software) autonomously explores its surroundings by taking action, hit and trial, learning from experiences, and increasing performance. Reinforcement learning operates on a feedback-based method.

The method of reinforcement learning is comparable to that of a human child; for instance, a kid learns new things by encounters in his daily life.

The objective of a reinforcement learning agent is to maximize the rewards since the agent is rewarded for every positive activity and penalized for every negative action.

In contrast to supervised learning, reinforcement learning relies only on the experiences of the agents.

Real world application of Reinforcement Learning:

Autonomous Driving:

Reinforcement learning is used to train self-driving cars to make decisions based on real-time sensor data and optimize driving behavior.

Game Playing:

Reinforcement learning agents have achieved superhuman performance in games like Go and Dota 2.

Robotics:

Robots can be trained to perform tasks like picking and placing objects or navigating complex environments using reinforcement learning.

Let’s now learn the pros and cons about Reinforcement learning:

Pros:

• Well-suited for decision-making tasks and sequential data.
• Can learn optimal strategies through interaction with an environment.
• Widely used in robotics, game playing, and autonomous systems.

Cons:

• Requires extensive exploration, which can be inefficient and costly.
• Sensitive to reward design, and poorly designed reward functions can lead to suboptimal behaviour.
• Training can be time-consuming and computationally intensive.

5. Deep Learning:

Deep Learning is a subset of machine learning that employ artificial neural networks as its foundation. It has the capacity to discover intricate patterns and connections inside data. In deep learning, not everything needs to be explicitly programmed. The availability of enormous datasets and improvements in processing capacity have led to an increase in its use in recent years.

Owing to the fact that it is built on artificial neural networks (ANNs), also known as deep neural networks (DNNs). These neural networks are created to learn from massive quantities of data, and they are inspired by the form and function of actual neurons seen in the human brain.

Real world application of Deep Learning:

Image and Object Recognition:

Deep learning is widely used in image classification, object detection, and facial recognition systems.

Natural Language Processing (NLP):

Deep learning powers chatbots, language translation, and sentiment analysis in applications like virtual assistants.

Healthcare Imaging:

Deep learning models assist in medical image analysis, including the detection of diseases in X-rays and MRIs.

Let’s now learn the pros and cons about Deep learning:

Pros:

• Excels at tasks involving large amounts of data, such as image and speech recognition.
• Can automatically learn hierarchical features from raw data.
• State-of-the-art performance in many applications.

Cons:

• Requires large amounts of labeled data for training, which may not be available for all tasks.
• Computationally expensive, often requiring specialized hardware (e.g., GPUs).
• Deep models can be challenging to interpret, leading to concerns about transparency and bias.

In conclusion, the choice of machine learning type depends on the specific problem, the availability of data, and the desired outcome. Each type has its own set of advantages and disadvantages, and practitioners must carefully consider these factors when selecting the most appropriate approach for a given task. Additionally, ongoing research and advancements in machine learning continue to expand the capabilities and address the limitations of these various types.