A-Z Guide to the Types of Machine Learning Problems

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A-Z Guide to the Types of
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Last Updated: 23 Aug 2022
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The world today is flooded with applications of machine learning and artificial
intelligence. Machine learning applications are found in many areas, such as
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digital assistants or cancer detectors. Hence, machine learning has become a
core aspect of everyday life, making it an essential topic to acknowledge.
There are various machine learning types; each type has its specific practice. It
is enough for the average computer user to understand the types of machine
learning and their implementation in day-to-day applications. Meanwhile,
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computer scientists ought to deeply comprehend machine learning and its
types to know how to create and enhance machine learning applications.
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Table of Contents
Relevant Projects
What is Machine Learning ?
How does Machine Learning Work?
Types of Machine Learning
The 3 Different Types of Machine Learning
Machine Learning Projects
Data Science Projects
Python Projects for Data Science
1) Supervised Learning Algorithms
Supervised Machine Learning Categories
Data Science Projects in R
Machine Learning Projects for
Beginners
Supervised Machine Learning Applications
Deep Learning Projects
2) Unsupervised Learning Algorithms
Unsupervised Machine Learning Categories
Unsupervised Machine Learning Applications
3) Reinforcement Learning Algorithms
Neural Network Projects
Tensorflow Projects
NLP Projects
Reinforcement Machine Learning Categories
Kaggle Projects
Reinforcement Learning Applications
IoT Projects
Types of Machine Learning - A Sneak Peek Into Hybrid Learning
Big Data Projects
Problems
Hadoop Real-Time Projects
1) Semi-Supervised Learning Algorithms
2) Self-Supervised Learning
3) Multiple Instance Learning
Algorithms for Statistical Inference
Examples
Spark Projects
Data Analytics Projects for
Students
1) Deductive Inference
2) Inductive Learning
3) Transductive Learning
Machine Learning Methods
1) Active Learning
2) Transfer Learning
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Types of Machine Learning in a Nutshell
FAQ’s on Different Types of Machine Learning
1) What is machine learning?
2) How many types are available in machine learning?
3) What are the main types of machine learning?
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What is Machine Learning ?
Machine Learning Algorithms
The famous computer scientist Arthur Samuel defined machine learning as a
‘computer’s ability to learning without being explicitly programmed’. Machine
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learning (ML) is a subfield of artificial intelligence (AI) mainly concerned with
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teaching machines to learn from data without the interference of a human
being. Machine learning allows the computer to constantly enhance
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performance and make predictions.
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How does Machine Learning Work?
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Despite the presence of different types of machine learning techniques, the
process of how a machine learning algorithm works is split into three principal
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fragments:
1. The Decision Process
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The goal of a machine learning system is to predict or classify an output
based on some input variables. The input data can be either labeled or
unlabeled.
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2. The Error Function
An error function is used to assess the prediction of the machine
learning model. If there is no new data, i.e., the data is labeled, then the
error function can compare the forecast with the known examples to
evaluate the model's accuracy and check for unusual data points.
3. The Model Optimization Process
The model optimization process is concerned with re-evaluating the
learning model to check if it can better fit data points into the training
operation. If the model can be enhanced, then the weights will be
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updated to decrease any inconsistencies between the known instances
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and the model's evaluation. The evaluation and optimization process is
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autonomously repeated until an accuracy threshold is met.
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Types of Machine Learning
Step by Step Guide
The most commonly used machine learning algorithms are supervised,
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unsupervised, semi-supervised, and reinforcement learning. That said, ten
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more types of machine learning models are also used.
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You can group each set of machine learning techniques according to the
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problems they solve and their purpose. Machine learning algorithms like
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supervised learning and unsupervised learning solve learning problems, while
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others like semi-supervised learning and multi-instance learning solve hybrid
learning problems. Moreover, some machine learning models, like inductive
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learning, aim to reach an outcome or decision.
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Learning Algorithms
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1) Supervised Learning Algorithms
Supervised learning algorithms are supervision-based machine learning
techniques, meaning the machine utilizes labeled data for the training process
to predict the output. Labeled data means the machine knows input data and
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its corresponding output during training, then predicts the output in the test
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process. Properly trained models should provide an accurate prediction close
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to the real-world outputs for a new input data set.
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To illustrate how supervised machine learning algorithms work, here is an
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example. A machine is provided with a training dataset of bicycles and cars.
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The labeled training data should allow the machine to understand the features
of the images. After training, an input image of a car is given to the machine to
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identify its output. If the training is successful, the machine should correctly
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categorize the image as a car.
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Logistic Regression
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(SVM)
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Supervised Machine Learning Categories
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Supervised machine learning algorithms have two main categories:
classification and regression.
i) Classification
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Predicts the label of a class
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Predict the dataset's categories
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Example: "Yes" or "No"
Commonly Used Algorithms:
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Logistic Regression
Random Forest Algorithm
Support Vector Machine Algorithm
ii) Regression:
Predicts the numerical label/continuous variables
Example: weather prediction
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Decision Tree Algorithm
Lasso Regression
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Analysis
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Supervised Machine Learning Applications
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2) Unsupervised Learning Algorithms
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Unlike supervised learning, unsupervised machine learning does not require
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supervision while training. To explain, it uses unlabeled data to train the model,
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and the output prediction is unsupervised. Unlabeled data is data that only
exists in the machine without prior knowledge. In unsupervised algorithms, the
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machine is exposed to unlabeled data and is expected to teach itself what that
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data is. The core unsupervised learning task is to recognize and correctly
classify objects independently.
For example, suppose the machine inputs images of random toys that are not
categorized. In that case, the machine will have to study the features of each
toy individually and categorize them accordingly. Then, when prompted with an
image of a toy, it should correctly categorize it.
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Management
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Hadoop HDFS Tutorial
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Unsupervised Machine Learning Categories
Unsupervised machine learning is classified into two types: association and
clustering.
I) Association
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Finds relations between variables in a large dataset
Goal: discover and map data dependent on the other to produce
maximum profit
Example: web usage mining
Commonly used algorithms:
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II) Clustering
A method of grouping each set of similar objects into a cluster
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Goal: discover inherent groups from the dataset
Example: retail marketing
Commonly used algorithms:
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Neptune
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DBSCAN Algorithm
and its use cases
Independent Component Analysis
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Mean-Shift Algorithm
Principal Component Analysis
Unsupervised Machine Learning Applications
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its use cases
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3) Reinforcement Learning Algorithms
Another machine learning algorithm is reinforcement learning, the closest
equivalent to how humans learn using trial and error. In deep reinforcement
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learning, the agent learns by communicating with its environment through
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actions and getting a reward in return (either positive or negative).
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Reinforcement learning comprises three significant components: agent,
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environment, and actions.
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-The Agent: the learner/decision-maker
-The Environment: anything the agent interacts with
-The Actions: what the agent does
A reinforcement learning algorithm does not have labeled data; the agents only
learn from interacting with their environment.
Reinforcement learning is based on feedback, and the agent attempts to make
multiple actions to maximize the positive rewards.
Image Name: Reinforcement Learning Algorithms
Reinforcement Machine Learning Categories
There are two main categories of reinforcement learning; positive
reinforcement learning and negative reinforcement learning.
I) Positive Reinforcement Learning
Positive reinforcement learning is an event that occurs as a result of a
particular behavior. This type of reinforcement learning strengthens the
behavior and increases its frequency, positively affecting the actions taken by
the agent.
Positive reinforcement learning maximizes the performance and sustainability
of change over an extended period.
II) Negative Reinforcement Learning
As opposed to positive reinforcement, negative reinforcement learning
decreases the frequency of the occurrence of a behavior.
Reinforcement Learning Applications
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Self-driving Automobiles
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Types of Machine Learning - A Sneak Peek
Into Hybrid Learning Problems
1) Semi-Supervised Learning Algorithms
The semi-supervised machine learning algorithm is another machine learning
technique that is a 'middle ground' between supervised and unsupervised
learning algorithms. Semi-supervised learning uses both labeled and unlabeled
data during training data.
Semi-supervised learning uses a small amount of labeled data and a
considerable portion of unlabeled instances so that the model can learn and
make predictions on new data.
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2) Self-Supervised Learning
Self-supervised machine learning is a process where machine learning models
focus on self-learning or self-training a part of the input (labeled data) from
another part of the input.
Self-supervised machine learning fixes the issue of unsupervised learning by
turning it into a supervised learning problem by generating the labels. This
process identifies any unseen bits of the input from any seen part of the input.
Image Name: Self-Supervised Machine Learning Problems
One of the primary applications of self-supervised machine learning is
predicting text in language processing, commonly known as auto-complete.
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3) Multiple Instance Learning
Multiple instance machine learning is a weakly supervised learning method
where the training data is arranged in groups, known as bags, and the entire
bag is given a label instead of labeling each training data individually.
Image Name: Multiple Instance Machine Learning Problems
Multi-instance learning is utilized in problems where labeling data is expensive,
such as in medical imaging, video or audio tags, and marketing. Thus, multiinstance learning can provide cheaper data storage costs and better resource
management.
Algorithms for Statistical Inference
Statistical inference means reaching a statistical decision by using statistical
methods. In machine learning, the inference is to fit a model and make a
prediction.
There are three primary approaches for statistical inference in machine
learning: deductive inference, inductive, and transductive learning.
1) Deductive Inference
Conclusion --> Observation
Deductive inference or deductive reasoning is a reasoning approach that
involves reaching a conclusion based on knowledge or information that is
presumably true. A deductive learning system learns or studies facts or
verifiable knowledge.
Deductive learning is a top-down reasoning type that studies all aspects before
reaching a specific observation.
2) Inductive Learning
Observation --> Conclusion
Inductive learning or inductive reasoning is also a mechanism in data science
that, in contrast to deductive reasoning, predicts outcomes based on little
evidence.
Inductive learning is a bottom-up reasoning approach that utilizes a specific
observation as evidence to conclude.
3) Transductive Learning
Transductive or transduction learning is a famous term in the machine learning
field. It is used in the field of statistical learning hypothesis to reference the
prediction of specific instances given particular examples from a domain.
Transduction does not require generalization; unlike Induction Learning it only
Transduction does not require generalization; unlike Induction Learning, it only
utilizes specific training examples.
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Machine Learning Methods
1) Active Learning
Active learning is a method where the machine learning model can question a
human operator during the learning process to find an answer to any present
vagueness during the learning cycle.
Active learning is a semi-supervised learning technique to achieve similar or
improved results to traditional supervised learning while maintaining fewer
training data.
2) Transfer Learning
Transfer learning is a machine learning technique that reuses pre-trained
models to develop new machine learning models. You can also use it to train
deep neural networks with relatively small data, which is beneficial in real-world
problems that do not have numerous labeled data points. Transfer learning
works sequentially, meaning tasks are learned in order rather than in parallel.
Transfer Learning Applications
You can apply transfer learning methods in different fields, such as artificial
neural networks, image classification, or natural language processing.
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3) Multi-Task Learning
Multi-task learning is a supervised learning process where the model attempts
to concurrently learn and perform tasks while simultaneously optimizing
multiple loss functions. The model utilizes all available training data across
various tasks in this machine learning approach and teaches the model to
generalize valuable data representation in different contexts.
Multi-Task Learning Applications
Multi-task learning is used in various domains. It is most commonly
implemented in:
Computer vision
Natural language processing
Recommendation systems
4) Ensemble Learning
Ensemble learning is a machine learning method that integrates two or more
machine learning models to create an ideal predictive model.
There are three basic strategies for ensemble learning; bagging, stacking, and
boosting.
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5) Online Learning
Also known as incremental or out-of-core learning, online learning is another
method that combines multiple machine learning techniques to stay updated
with the latest data. Online learning utilizes available data and constantly
updates the model before making a prediction or after the latest observation.
Online machine learning is specifically beneficial when the number of
observations exceeds the memory limit. Hence, the learning is carried out
incrementally over observations. In other words, the online learning model is
continuously updated using a real-time data stream.
Online Learning Applications
You can use Online learning in any industry where the data constantly changes
over time.
Types of Machine Learning in a Nutshell
Artificial intelligence and its branches are an integral part of the digital
revolution, and machine learning is one of the most crucial artificial intelligence
branches. Machine learning focuses on imitating how humans learn and
process information to create a machine learning system that thinks and acts
like a human being. Each algorithm has a specific purpose for different types
of machine learning problems and techniques. This blog looked at the most
famous machine learning techniques - supervised, unsupervised, semisupervised, and reinforcement learning.
Machine learning has numerous applications in today's world. The
implementation of machine learning in day-to-day life ranges from digital
personal assistants like Siri to more complex fields such as cyber security.
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FAQ’s on Different Types of Machine
Learning
1) What is machine learning?
Machine learning is a subtype of artificial intelligence (AI) and computer
science that uses data and various efficient algorithms, such as supervised
and unsupervised learning, to emulate the way humans learn.
2) How many types are available in machine
learning?
There are 14 types of machine learning:
Supervised learning algorithm
Unsupervised learning algorithm
Semi-supervised learning algorithm
Reinforcement learning algorithm
Self-supervised learning algorithm
Multi-instance learning algorithm
Deductive learning algorithm
Inductive learning algorithm
Transductive learning algorithm
Active learning algorithm
Transfer learning algorithm
Multi-task learning algorithm
Ensemble learning algorithm
Online learning algorithm
3) What are the main types of machine learning?
The main types of machine learning are:
Supervised Learning
Unsupervised Learning
Semi-Supervised Learning
Reinforcement Learning
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