Machine learning
Introduction
Artificial intelligence(AI) is a huge topic today, and it has many parameters to understand and so many factors to establish. Machine learning is also a part of AI. Taking from your mobile to the complex machine of satellite AI can be used in many devices for many functions. Machine learning is simply explained as the training of the machine. These machines have the capability to learn for example your smartphone it learns and interpret your particular way of speaking is an example of AI. You are using the AI and machine learning all over the day without thinking about it. We can speak to devices is because of machine learning.
The "Machine learning" is considered as the 4th generation of the technology.
Learning Tools for machine learning
There are coding languages which are most used for Machine learning are "R distribution" "Python distribution" and some "Math basics" to make this happen. Also the initial step taken to start the process is to make the algorithm in any computer language.In Machine leaning the term "Master algorithm" is used in which there are all the ideas and processes are included in it. To prepare Master algorithm the following video will help you.
By the end of the above video you will be aware of "Master algorithm" , parameters and what technology you want, to make the algorithm of the "Machine learning"and"Artificial intelligence" project should be happened.
Data assets or learning the Big data
This technology is basically there will be only 30-40 percent of man power and the total is depend on the Data base as the computer want huge data to store all the outputs or the result which are useful for the next input. For this the algorithm is written as it makes it own algorithm with the desired output. So the companies also searching for the "Data scientists". Who has talent to maintain the data and developing the data-sets.
Usage and Advantages
The present world is using the Machine learning enormously this technology is appearing in many products. But the usability is not completely done as the data-sets for this is limited so the usability or application is limited in the future after installing big data-sets the usage will be high and the accurate values also come in existing for the perfect products. Increase of products leads to the increasing of industries and making the human life even better. Also solving problems which have no solutions even with the present technology.
The developers also have the new opportunity in this new technology as the increase of industries may provide new jobs and training of new technology who are unaware of can make even more products which makes the human life more easier.
Disadvantages
As this technology is the evolving one in the present industrial society the disadvantages are not explained much as we know every technology has its own drawback and limitations. The AI and Machine learning has no much drawbacks as it is the technology which works on its own.
But coming to the data assets it has to take huge data-sets this may be the initial drawback which we have to think of . Due to this the upcoming "Data scientists" have huge scope in this technology.
Applications
When it comes to the applications there are enormous usage in the present condition of the world from the Army weapons to the farming of lands there are many works that can done by this technology. Also this technology is used in the Medical diagnosis, Data mining .
For Army it can make the autonomous weapons
For automobiles we make autonomous driving cars
For medical purpose the Nanobots with Artificial intelligence is also done.
There are many if you can think now the total world can be changed with this technology.
Key topics to understand Machine learning
- Python distribution
- R distribution
- Coding in Python using Anaconda
- Baye's theory
- Support vector machine (SVM)
- Preprocessing data
- Spam filteration
- Feature selection technique
INTERPRETING LEARNING AS OPTIMIZATION
Supervised Learning
Supervised learning occurs when an algorithm learns from the example, that means when input is given to the machine it can identify the object with previously stored data in it and learn the things. Just like the teacher who helps the students to memorize the things with the help of good examples it works like the training process for the machine. This is processed by the following.
- Training data
- Both inputs and outputs
- Classification
- Regression
From the above list the "classification" and "regression" are the two major processes for the machine to learn.
Classification : When the output variable is categorical i.e, 2 or more classes with answers of (yes / no) or (true / false), we make use of classification. It deals with the qualitative variable.
Regressions : It deals with the numerical values or the relationship between two or more variables where a change in one variable associated with the change in another variable.
Unsupervised Learning
Unsupervised learning occurs when an algorithm learns from plain examples without any associated response. It has to determine its own data pattern in other words, it all experience the new things and make them separated according to their behavior and making itself learn with that. This follows the self learning process. The other processing methods are
- Clustering
- Association
- K- Mean
Clustering : It is the process of dividing the objects into clusters which are similar between them and dissimilar to the objects belonging to another cluster.
Association : Discovering the probability of the co- occurrence of items in a collections.
By the above methods the unsupervised makes its own algorithm to study itself with.
Reinforcement Learn
Reinforcement Learning occurs when you present the algorithm with examples that lack label, as in unsupervised learning. However, you can accompany an example with positive or negative feedback according to the solution the algorithm proposes. It is connected to applications for which the algorithm must make decisions.
Conclusion
This topic is began by nothing but my interest in technology and thinking of sharing through blog, the brief explanation of Machine learning with Artificial intelligence is explained with some of references. The source of this writing is from some books I mentioned below and also from other websites which helped me learn about the products of Machine learning.
The consequences of my findings are there are even more subjects that should understand and to be specialize in particulars to maintain the lead in this technology as it is not the one whole subject, it is made of different subjects together make a product of this technology.
References
As it is learned in many ways like books, online classes, outside course,etc., but I personally prefer the books which makes the learning very simple and easy the below are some of the books with hands-on for all the category of Machine learning.
For beginners
For intermediate
For advanced
1 Comments
Artificial intelligence is not old, but its present situation is one of the parts of this field. And AI is one of the things which will covert machine learning development services .
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