Data Analytics Course in Hyderabad

MACHINE LEARNING COURSE IN HYDERABAD

Complete Guide to Machine Learning

Ever wondered how Netflix recommends you shows and movies without even, explicitly, knowing the genres that interests you, or how Amazon recommends you the products you are more likely to buy? All these are due to the Machine Learning capabilities of the system. Even our social media feeds use this technique. Posts in our feeds are not shown in order of the time they were posted, but in the order of higher probability of the user to check a particular account's post.

Often described as a subset to Artificial Intelligence (AI), Machine learning relies on reading how a user interacts with the system he is using and uses this data on the basis of algorithms to carry out specific tasks. In layman's terms, it allows the computer systems to write the code for themselves when required, rather than the programmer writing it every time.

Machine learning is different from the traditional programming paradigm. While the latter relies on the explicit definition on how a particular action of a user result in a particular response of the system. Machine Learning interprets how the user is interacting with the system and forms statistical models from this data. These mathematical data are used by algorithms to trigger a specific task effectively without the explicit use of instructions. It is a direct modern-day application of Artificial Intelligence realm of Computer Science. Machine Learning improves the user experience automatically over time. 

The advantage of this is that it allows the system to independently adapt on exposure to new data and produce reliable results without any misbehavior of the system without any interference of the developer, as opposed to the older Machine Learning sciences.

Machine learning has helped the computer systems to interpret and decode complex patterns in data which is difficult for the human mind to even perceive. 

Following are some of the machine learning methods:

• Supervised machine learning algorithms:

The system applies what it has learned in the past to new data, predicts the output, compares it with the correct output, find errors and accordingly modify its model to give the desired result.

• Unsupervised machine learning algorithms: This technique is used when the data is unlabelled and unsorted. The system analyses the data to find patterns in it. It doesn't find the correct output but, over time, figures out the hidden structures in the datasets and draw the best possible inferences.
• Semi supervised machine learning algorithms: For this technique to come into play, there must be two sets of data, one labelled and the other unlabeled. This technique lies somewhere between the above two- it analyses the labelled data from what it already knows and infers the unlabeled data to figure out the patterns. The system uses the information from both types of data to improve its overall learning capabilities.
• Reinforcement machine learning algorithms: It takes a particular action according to the situation in order to maximize the reward or rather performance by discovering the errors over time. This allows the computer system to automatically determine the ideal behavior in a particular context. It is used in allocating resources to different tasks in computer memory.

Machine learning has various day to day applications:

1. Virtual phone assistants like Siri, Alexa, Google Now use machine learning to analyze your information on the basis of previous interaction with the information.
2. Google maps, traffic predictions and online booking services. All our location and velocity data are stored on a central server.
3. Video surveillance cameras can use machine learning to detect abnormal human behavior like standing still at a place for a long time; an alert is triggered.
4. Social media feed
5. Email spam filtering
6. Product recommendations
7. Search engine queries. Search results are improved on backend analysis of how one responds to the results shown by the search engine.
8. Smarter notifications in newer android versions: android pie learns how one uses a particular app and the duration of use. For instance, there would be only important notifications or no notifications when one is less likely to use the phone, or unnecessary notifications from apps that are not used very often are blocked.

You can join machine learning online course from 360DigiTMG 

to become an expert in machine learning.

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