Types of Machine Learning

 There are mainly six types of machine learning: Supervised learning, Unsupervised learning, Semi-supervised learning, Reinforcement learning, Deep learning, and Self-supervised learning.

   What is Machine Learning

    Machine learning (ML) is a type of artificial intelligence (AI) task that teaches computers and machines how to learn from humans, working independently, and improving their performance and accuracy through exposure to more data.

  Machine learning is a simple way for computers to learn and teach from data. Machine learning is used more explicitly as a means of extracting knowledge from data through techniques such as neural networks, supervised learning, decision trees, and linear regression. Machine learning is a subset of artificial intelligence.

  Types of machine learning

1. Supervised Machine learning 

2. Unsupervised Machine learning

3. Semi-supervised learning

4. Reinforcement learning

5. Deep learning

6. Self-supervised learning

1. Supervised Machine learning

   Supervised machine learning is a type of machine learning where AI models are trained using labeled datasets. The goal is to create a model that uses the labeled database to train algorithms to predict outcomes and recognize patterns. It associates each input with a corresponding output.
  The algorithm identifies patterns between input features and output. It is trained to accurately predict outcomes.

Examples of supervised learning

• Logistic regression
• Naive Bayes
• Random forest

2. Unsupervised Machine learning

    Unsupervised learning in artificial intelligence is a type of machine learning that learns from data without supervision. Unlike supervised learning, unsupervised machine learning models are given unsupervised answers and allowed to discover patterns and insights without any explicit guidance or instruction. Mostly, this data is commonly used in every form of scientific research, economics and architecture,and  human organizational activity.

Examples of Unsupervised Learning Applications: 

• Customer Segmentation

• Social Network Analysis

• Recommender Systems

• Image Recognition

• Fraud Detection

3. Semi-supervised learning

   Semi-supervised learning is a type of machine learning that uses both labeled and unlabeled data to train a model. Semi-supervised learning is generally used for the same use cases in which one might otherwise use supervised learning methods. It is distinguished by various techniques that incorporate unlabeled data into model training in addition to the labeled data required for traditional supervised learning.

   Semi-supervised learning methods are particularly relevant in situations where it is very difficult or expensive to obtain sufficient amounts of labeled data, but it is relatively easy to obtain large amounts of unlabeled data. In such scenarios, neither fully supervised nor unsupervised learning methods will provide adequate solutions. 

4. Reinforcement learning

  Reinforcement learning is a machine learning training method that trains software to perform certain desired actions. Reinforcement learning is based on rewarding desired behaviors and punishing unwanted ones.

  Reinforcement learning theory comprises three fundamental components: the agent, the environment, and the reward signal. These components collectively facilitate the learning process, enabling the agent to make sequential decisions and optimize its behaviors based on the received rewards.

5. Deep learning

   Deep learning is a type of machine learning that uses artificial neural networks to learn from data. Artificial neural networks are inspired by the human brain. In-depth learning is characterized by which learners who seek to fully understand the meaning of a concept and relate it to daily life. They can be used to solve a wide range of problems, including image recognition, natural language processing, and speech recognition. 

Types of Deep learning 

• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Transformers models 

6. Self-supervised learning

   Self-supervised learning is a methodology that can learn complex patterns from unlabeled data. This allows AI systems to work more efficiently when deployed due to their ability to train themselves. Thus requiring less training time.

   Self-supervised learning (SSL) is particularly useful in fields like computer vision and Natural language processing (NLP) that require large amounts of labeled data to train state-of-the-art Artificial intelligence (AI) models. Because these labeled datasets require time-consuming annotation by human experts, gathering sufficient data can be prohibitively difficult. Self-supervised approaches can be more time- and cost-effective, as they replace the need to manually label training data.