Spring Boot Tutorial ( beginners to professionals) part -1

 Machine Learning

 

 

 

 

 

What is machine learning?

Machine learning (ML) is a sort of artificial intelligence (AI) that allows software programs to improve their prediction accuracy without being expressly designed to do so.
In order to forecast new output values, machine learning algorithms use past data as input.

Machine learning is frequently used in recommendation engines.
Fraud detection, spam filtering, malware threat detection, business process automation (BPA), and predictive maintenance are all common applications.



Why is machine learning important?

Machine learning is significant because it allows businesses to see trends in consumer behavior and operational patterns, as well as aid in the creation of new goods.
Machine learning is a major aspect of the operations of many of today's leading organizations, like Facebook, Google, and Uber.
For many businesses, machine learning has become a key differentiation.



What are the different types of machine learning?

The way an algorithm learns to become more accurate in its predictions is how traditional machine learning is frequently classified.
supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning are the four fundamental methodologies.
The algorithm that data scientists employ is determined on the sort of data they wish to forecast.

Data scientists feed algorithms with labeled training data and identify the variables they want the algorithm to examine for correlations in supervised learning.
The algorithm's input and output are both provided.

Machine learning algorithms that train on unlabeled data are known as unsupervised learning.
The program looks for relevant connections between data sets.
The data used to train algorithms, as well as the forecasts or suggestions they provide, are all predetermined.

Semi-supervised learning is a hybrid of the two previous approaches to machine learning.
Although data scientists may feed an algorithm largely labeled training data, the model is allowed to explore the data and establish its own knowledge of the set.

Reinforcement learning is a technique that data scientists use to train a machine how to finish a multi-step process with precisely stated rules.
Data scientists design an algorithm to perform a task and provide it with positive or negative feedback as it figures out how to do so.
However, the algorithm, for the most part, selects what actions to take along the road on its own.



How does supervised machine learning work?


The data scientist must train the algorithm with both labeled inputs and desired outputs in supervised machine learning.
The following tasks benefit from supervised learning algorithms:

• Binary classification is the division of data into two groups.
• Choosing between more than two categories of answers is referred to as multi-class classification.
• Predicting continuous values using regression modeling.
• Combining the predictions of numerous machine learning models to get an accurate prediction is referred to as assembling.

How does unsupervised machine learning work?


Machine learning methods that are unsupervised do not require data to be labeled.
They dig through unlabeled data in search of patterns that can be utilized to divide data into subgroups.
Unsupervised algorithms make up the majority of deep learning algorithms, including neural networks.
The following tasks are well-suited to unsupervised learning algorithms:

• Clustering is the process of dividing a dataset into groups based on their similarity.
• Anomaly detection is the process of identifying unexpected data points in a set of data.
• Identifying groups of objects in a data collection that commonly occur together is known as association mining.
• Reducing the number of variables in a data set is known as dimensionality reduction.

How does semi-supervised learning work?


A modest quantity of labeled training data is fed to an algorithm in semi-supervised learning.
The algorithm then uses this information to learn the data set's dimensions, which it may subsequently apply to fresh, unlabeled data.
When algorithms are trained on labeled data sets, their performance tends to improve.
However, classifying data takes time and money.
Semi-supervised learning falls between between supervised and unsupervised learning performance.
Semi-supervised learning may be found in the following areas:

• Machine translation is the process of teaching computers to translate a language using a small number of words rather than a complete dictionary.
• Fraud detection is the process of identifying fraud situations when there are just a few positive examples available.
• Data labeling: Algorithms trained on tiny data sets may automatically apply data labels to bigger ones.

How does reinforcement learning work?


Reinforcement learning is based on the programming of an algorithm with a specific goal and a set of rules for achieving that objective.
The algorithm is also programmed to seek positive rewards (which it receives when it performs an activity that helps it get closer to its ultimate objective) and avoid negative rewards (which it receives when it performs an action that causes it to move further away from its ultimate goal).
Reinforcement learning is widely employed in a variety of fields, including:

• Robotics: Using this method, robots may learn to do tasks in the physical environment.
• Reinforcement learning has been used to train bots how to play a variety of video games.
• Resource management: When faced with limited resources and a clear aim, reinforcement learning can assist businesses in determining how to allocate resources.

Who's using machine learning and what's it used for?


Machine learning is being applied in a variety of applications.
The recommendation engine that drives Facebook's news feed is perhaps one of the most well-known instances of machine learning in operation.


Machine learning is used by Facebook to customise how each member's feed is presented.
If a member often reads a certain group's postings, the recommendation engine will begin to prioritize that group's activity in the feed.


The engine is working behind the scenes to reinforce recognized trends in the member's online activity. The news feed will be adjusted if the member's reading habits change and he or she fails to read postings from that group in the following weeks.


Other applications of machine learning, in addition to recommendation engines, include:

Customer relationship management is the management of customer relationships.
CRM software may evaluate email using machine learning models and push salespeople to react to the most essential communications first.
Advanced systems can even make recommendations for possible beneficial solutions.

Intelligence for business.
Machine learning is used by BI and analytics software suppliers to detect potentially valuable data points, patterns of data points, and anomalies.

Machine learning models may be used by human resource information systems to sort through applications and find the best applicants for an available position.

Automobiles that drive themselves.
A semi-autonomous automobile might even distinguish a partially visible item and inform the driver using machine learning techniques.

Virtual assistants are a type of virtual helper.
To analyze spoken speech and provide context, smart assistants often blend supervised and unsupervised machine learning models.