B.Tech. in Machine Learning: What Are the Different Types?

What is Machine Learning and what does a student learn when pursuing a B.Tech. in Machine Learning program? 

Machine learning is a broad field of study that encompasses and incorporates concepts from a variety of related fields, including artificial intelligence. Learning, or gaining skills or knowledge through experience, is the main focus of the field. This typically entails synthesizing useful concepts from historical data. As a result, as a machine learning practitioner, you may come across a variety of learning styles, ranging from entire fields of study to specific techniques.

This post will give you a gentle introduction to the various types of learning that you might come across while in the machine learning degree program.

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Machine Learning's Different Types

Given that the field of machine learning is centered on "learning," you may encounter a variety of learning styles as a practitioner. Some types of learning refer to entire subfields of research involving numerous different types of algorithms, such as "supervised learning." Others, such as "transfer learning," describe effective techniques you can employ on your projects.

As a machine learning practitioner, you should be aware of approximately 14 different types of learning:

Learning Problems

1. Supervised Learning

2. Unsupervised Learning

3. Reinforcement Learning

Hybrid Learning Problems

1. Semi-Supervised Learning

2. Self-Supervised Learning

3. Multi-Instance Learning

Statistical Inference

1. Inductive Learning

2. Deductive Inference

3. Transductive Learning

Learning Techniques

1. Multi-Task Learning

2. Active Learning

3. Online Learning

4. Transfer Learning

5. Ensemble Learning

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Supervised Machine Learning

There is a set of input variables (x) and an output variable (y) in the supervised learning model. An algorithm determines the mapping function between the input and output variables. The formula is y = f (x).

The learning is supervised or monitored in the sense that we already know the outcome and the algorithm is tweaked each time to improve its performance. The algorithm is tweaked until it achieves a satisfactory level of performance after being trained on the data set.

The supervised learning problems can be classified as follows:

1. Regression problems

This type of machine learning is used to forecast future values using historical data to train the model. speculating on a product's future price.

2. Classification problems

These machine learning types aid various labels in training the algorithm to recognize items in a specific category. Is it better to have a disease or not have a disease, or to eat an apple or an orange, for instance? It's either beer or wine.

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Unsupervised Machine Learning

This method is used when the output is unknown and we only have access to the input variable. The algorithm learns on its own and uncovers a stunning data structure. This is also essential to gain a better understanding of the data, the goal is to decipher the underlying distribution. 

Unsupervised learning problems can be classified as follows:

1. Clustering

This entails grouping together input variables with similar characteristics. For instance, grouping users based on their search history.

2. Association

The rules that govern meaningful associations among the data set are discovered here. People who watch "X" are more likely to watch "Y."

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Reinforcement Machine Learning

Machine learning models are taught to make a series of decisions based on the rewards and feedback they receive for their actions using this method. During the learning period, the machine is rewarded for achieving a goal in complex and uncertain situations. 

Reinforcement learning differs from supervised learning in that there is no predetermined answer, so the reinforcement agent determines how to complete a task. When there isn't any training data available, the machine learns from its own mistakes.