Machine learning technology is the field of science in which we learn how computers act like a human being. Improve their learning process with the passage of time in an independent way, by storing that data or information and make observation in real world environment. It is one of the most basic practices of using algorithms. It parses the data, observe that data and make a prediction how integrate it in the real world. This technology totally based on algorithms without depending on rule based programming. These algorithms figure how to perform all basic tasks by learning from existing techniques.
What are the Challenges and Limitations that we face in ML?
Two major challenges that we face in machine learning are over fitting and dimensional. In over fitting sense, model goes towards unfairness of data and does not consider the new data or new variance. In dimensional sense algorithms works in very high dimension and make understanding level more difficult. Data access size is very large which cause problem to handle it. Data testing is not successfully done which cause the illusion in success. Another problem is with algorithm when it does not work properly which leads to the sociability issue.
What is Difference between Machine Learning and Artificial Intelligence?
It’s a technology in which machine learn on its behalf without being unambiguously programmed. Basically it is an application of artificial intelligence which can generate a program by the integration of input and output of that program.
- ML responsible to increase accuracy level not care full about success level.
- It generates output after taking input from program.
- It increases the performance of machine by learning from data on certain task.
- From accepted data it allows system to learn new things.
- Its main task is self-algorithm creation.
- This technology follows given instruction and lead data accordingly.
Artificial intelligence is the combination of two words artificial and intelligence. Artificial means which is not natural developed by human beings and intelligence means this technology understand ability.
- This technology enhances the chances of success rather than accuracy.
- Its working flow is same like computer program.
- It follows natural intelligence techniques to solve complicated tasks.
- AI is machine make decision by its own rather following data instructions.
- It leads intelligence for the finding of optimal solutions.
Top 3 Applications of Machine Learning:
Now a day’s machine learning is used in many applications than we expect or imagine. We do not how many times we use an algorithm in a single process. Many companies are using ML to boost up their businesses to generate more productivity, to detect diseases, weather forecasting, and many other applications. These are major applications where Machine Learning is being used.
Online Search Engines:
There are many search engines like google, Bing and many others rank website very efficiently. This whole process is made possible by using complex machine learning algorithms which ranks the web pages very precisely.
Many photo tagging applications like Face book or many others photo tagging apps have ability to tag photos with friends. ML makes this system possible by recognizing face through face recognition algorithm.
When our mailbox fills we remove it from inbox to avoid from the trouble when finding specific email. These email can recovered from trash or spam through a mail application algorithm.
With the advancement of technology and in this competitive environment this is not enough to understand the data or information that we have. But we also need to organize more tools for the data that we will obtain in future. To do this and achieve this target we have need to build more efficient intelligent machines.
Basic machine learning algorithms:
Here are some major machine learning algorithms which are influencing in their own ways.
Supervised machine learning algorithms:
In this algorithm future predictions are made by implementing past data. This is started from the identified data-set and the learning algorithms create inferred functions to forecast about output values. ML learning algorithm can also compare results with output to identify errors and in order to modify the model according to its own way.
Unsupervised machine learning algorithms:
These algorithms are used to describe a hidden configuration from unlabeled data by studying how systems can suppose a function. System cannot shape out right output but it explores the data and describes hidden data sets.
Semi structured Algorithm:
Semi structure ML use both kind of small amount labeled and large amount unlabeled data. System which uses these methods provides excellent accuracy. Semi supervised when labeled data needs relevant resources and high class skills.
Reinforcement ML algorithm is used to interact with its environment by generating actions and discovering rewards. The major characteristic of this algorithm is to search errors and delays. By doing so it increases the performance of software within a specific environment.
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