A tuple is an assortment of items which requested and permanent. Tuples are successions, very much like records. The contrasts among tuples and records are, the tuples can't be changed not normal for records and tuples use enclosures, though records utilize square sections. Making a tuple is pretty much as straightforward as putting diverse comma-isolated qualities. Alternatively you can put these comma-isolated qualities between enclosures moreover. For instance − tup1 = ('material science', 'science', 1997, 2000); tup2 = (1, 2, 3, 4, 5 ); tup3 = "a", "b", "c", "d"; The void tuple is composed as two enclosures containing nothing − tup1 = (); To compose a tuple containing a solitary worth you need to incorporate a comma, despite the fact that there is just one worth − tup1 = (50,); Like string files, tuple records start at 0, and they can be cut, linked, etc. Getting to Values in Tuples To get to values in tuple, u...
We know humans learn from past experiences and machines follow instructions given by humans, but what if humans can train the machines to learn from the past data and do what humans can do act much faster, that’s called Machine Learning.
For example, 1 method is the classification method. It can put data into various groups. The same classification method used to concede handwritten numbers and also be used to classify emails into spam and not-spam. It is the same method but it’s fed different training data and hence it comes up with different classification logic.
Figure: Classification
There are three types of Machine Learning Algorithms:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised Learning:
Here we have a teacher who gives us instructions i.e training data, which means here in supervised learning we have inputs also and outputs also and through that given data also known as labeled data we prepare a model and there we put our new I puts and check whether we are getting desired output or no and if we get the same output as per training data, the data given was very accurate and refined and the algorithm is properly learned and classified, and the algorithm used here is Naive Bayes Algorithm.
Unsupervised Learning:
Unsupervised learning is something where we only have some inputs, and from the available and known inputs, we make clusters or groups according to
similar inputs. Here we do not have outputs, so we have to generate them by using K-Mean Algorithm. Maximum learning is done through unsupervised learning.
Reinforcement Learning:
Now here, this learning is based on reward and policy. For eg. We have an agent who performs some action in the environment and in return, the agent gets some reward/penalty based on the action performed may be positive or negative. So according to the change in environment, the agent makes the policy, and based on the policy he performs his actions in a different manner. So here from the rewards and penalties, learning is done.
Some of the exciting examples of Machine Learning are:
1. Virtual Personal Assistants
Siri, Alexa, Google Now are some of the well-known examples of virtual personal assistants. As the name suggests, they assist in finding specific information, when asked over voice. All you need to do is activate them and ask “What is my schedule” or maybe “Read the messages” or maybe set any alarm and accordingly u get answered.
Virtual Assistants are integrated into a variety of platforms. For example:
- Smart Speakers: Amazon Echo and Google Home
- Smartphones: Samsung Bixby on Samsung S8
- Mobile Apps: Google Allo
2. Predictions while Commuting
Traffic Predictions: We use our GPS locations randomly while traveling, so that detects our current location as well as the location where we want to reach and guide us accordingly. Also, we are guided by the traffic ahead and various routes we can go through.
Online Transportation Networks: While we book an ola or cab, the price is estimated automatically. In this entire cycle, Machine Learning plays a very important role.
3. Videos Surveillance
When an alone person is appointed to monitor various video cameras, that becomes very difficult. So, nowadays the system of video surveillance is powered by artificial intelligence so the crime is detected before they are occurred, hence gives us the alert.
4. Product Recommendations
When you shop some product online, and after some days you receive emails with similar shopping suggestions. You also get some notifications through the same website from where you have shopped the product earlier, they suggest products according to your taste and preference.
5. Online Fraud Detection
Machine learning is proving its potential to make cyberspace a secure place and tracking monetary frauds online is one of its examples. For example, Paypal is using ML for protection against money laundering.
I hope you enjoyed reading this article and finally, you came to know about Machine Learning, Types of machine learning, and Real-life applications of machine learning.
For more such blogs/courses on data science, machine learning, artificial intelligence, and emerging new technologies do visit us at InsideAIML.
Thanks for reading…
Happy Learning…
Comments
Post a Comment