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What is machine learning.?**
Well machine learning is a subfield of artificial intelligence, that focus on building system that learn and improve performance based on what data is fed to them.
Machine learning technology is advancing at a fast rate & we are just scratching the surface of its capabilities
Types of machine learning include:
• Supervised machine learning
Both the input and output of the algorithm are specified. Algorithm are supplied with labeled training data and defined variables
• Unsupervised machine learning
Vice-versa of the supervised where trains on unlabeled data. The algorithm scans through data sets and find meaningful connections.
• Semi – supervised machine learning
Free to explore data fed on its own and develop its own understanding of the data sets.
• Reinforcement machine learning.
Algorithm is programmed to complete a task and give it positive & negative as it work out how to complete a task but for the most part , the algorithm decides on its own what steps to take along the way.
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Machine learning languages :_
They include
python – mostly for natural language processing, sentimental analysis & analytics
C & C++ - games and robot locomotion
Java - network security , cyber attack , fraud detection
JavaScript – web APIS
R – statistical and visualization mostly
The importance of machine learning?
Well it helps make informed decision as it helps enterprises a view of trends in customer behavior and business operations patterns.
It can handle varieties and large quantities of data
Able to identify trends and patterns from data.
It can be applied to solve problems, such as fraud detection, facial recognition, enable self-driven cars .
It also drives speech recognition technology and Medical diagnosis.
It increases productivity since its ability to automate repetitive tasks .
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The disadvantages is_ that it is time consuming and
Requires a lot of space. May contain errors if fed on wrong analytics.
Wrapping up
To wrap up, choosing the right algorithm depends on :
Visualization of data, the size of data, characteristics and type of data and training time required.
The accuracy and speed of the data. The parameters ( numbers that will affect) and features(quantifiable variable of the problem).
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