Machine Learning: Different Types and Their Applications

Machine Learning or ML is an AI branch that allows systems to learn from a large database, improve previous experience, and solve particular problems. This powerful tool allows computers or systems to think and comprehend like humans without being explicitly programmed. 

According to Fortune Business Insights, the global market size of ML is projected to grow at a CAGR of 38.8% with US $209.91 Billion by 2029. The market has expanded due to ML integrations in robots. Several machine learning algorithms are used to develop complex models. Each algorithm can be divided into a specific category. 

Here are some common machine learning types with their advantages and applications.

1.      Supervised Machine Learning

This type of machine learning trains machine learning models using labeled data, which already have input and output parameters. The models only require mapping the inputs to the relevant output. As the name suggests, this method requires external supervision for training ML models and guidance to provide the desired outcome.

Common Supervised Learning Algorithms

Classification and Regression are two main categories of supervised machine learning. Classification requires predicting categorical target variables like discrete labels or classes. Decision tree, naïve bayes, support vector machine, logistic regression, random forest, and KNN(K-nearest neighbors) are common classification ML algorithms.

Regression deals with predicting continuous target variables. For example, predicting home prices based on facilities, size, and location. Linear, decision tree, random forest, polynomial, lasso, and ridge are some regression algorithms.  

Applications: Supervised learning is used in NLP (natural language processing), image classification, speech recognition, fraud detection, predictive analytics, weather forecasting, customer support, and more applications.

Advantages: Supervised learning involves an interpretable decision-making process and saves resources and time when building new models from scratch. Since these models are trained on labeled datasets, they can have high accuracy.

2.      Unsupervised Machine Learning

Unsupervised learning, as opposed to supervised learning, uses unlabeled datasets to train machines and identify patterns and relationships. It doesn’t involve providing labeled target outputs. Instead, it discovers patterns and comprehends data trends to get the output. The model labels the data based on input data features.

Common Unsupervised Learning Algorithms

Clustering and association are two main categories of unsupervised machine learning that professionals can add to their AI career. Clustering involves grouping data points into clusters based on similarity. It is primarily used to recognize patterns and relationships in data without requiring labeled examples. DBSCAN, independent component, principal component, K-means clustering, and mean-shift are common clustering algorithms.

Association rule learning involves identifying relationships between dataset items. FP-growth, Apriori, and Eclat are common association rule learning algorithms.         

Applications: A few of the top unsupervised learning applications include customer segmentation, anomaly detection, content recommendation, market basket analysis, topic modeling, and genomic data analysis.

Advantages: Unsupervised machine learning algorithms are good for identifying hidden patterns and relationships between the data without requiring labeled data. It eliminates the need for data labeling.

3.      Semi-Supervised Machine Learning

This machine-learning algorithm uses a combination of labeled and unlabeled data. It is useful when accessing labeled data is resource-intensive, time-consuming, and costly. This algorithm is selected when labeled data needs pertinent resources and skills for learning or training from it.

Common Semi-Supervised ML Algorithms

Graph-based learning, co-training, label propagation, self-training, and GANs (generative adversarial networks) are some common semi-supervised ML algorithms. Graph-based learning uses a graph to show the data points’ relationships. Two different ML models are trained using a co-training algorithm on various subsets of unlabeled data.

Label propagation iteratively propagates labels from labeled to unlabeled data points. Self-training involves training a model on labeled data and then predicting labels for unlabeled data using trained models. GANs algorithms are used to produce synthetic and unlabeled data.

Applications: NLP, image classification and object recognition, recommendation systems, healthcare imaging, and speech recognition are common applications of semi-supervised machine learning.

Advantages: Semi-supervised machine learning algorithms can be applied to a variety of data and leads to effective generalization.

4.      Reinforcement Learning

Another type of ML algorithm that AI and ML engineers and other AI professionals must understand is reinforcement learning. This type of algorithm is used to train a machine to perform certain tasks and enhance its rewards in a specific situation. Actions and rewards are generated using an environment and an agent.

Common Reinforcement Learning Algorithms

SARSA, Deep Q-learning, and Q-learning are common reinforcement learning algorithms. SARSA (State-Action-Reward-State-Action) is a model-free RL algorithm that updates the learned Q-function for the action actually taken. Q-learning is also a model-free RL algorithm. It learns a Q-function that predicts the assumed reward of acting in a specified state. Deep Q-learning, an integration of Q-learning and deep learning, represents the Q-function using a neural network.

Applications: We have different applications in reinforcement learning algorithms. It includes training robots, game playing, autonomous vehicles, NLP, healthcare, recommendation systems, VR, AR, education, adaptive personal assistants, etc.

Advantages: Reinforcement machine learning has autonomous decision-making that can be learned for making a sequence of decisions. It helps in achieving long-term results and solving complex problems.

To deeply understand the workings of each ML algorithm type, it is worth enrolling in machine learning certifications, offering basic and advanced knowledge.

Wrapping Up

As you read above, each machine learning algorithm has different working, applications, and advantages for improved data prediction capabilities. Since machine learning is used in several industries, developing expertise in machine learning will open numerous career opportunities for you. So, don’t miss the chance to ride a high wave of data revolution. Sharpen your key skills and become part of this rapidly growing technology.

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