define machine learning This is a topic that many people are looking for. newyorkcityvoices.org is a channel providing useful information about learning, life, digital marketing and online courses …. it will help you have an overview and solid multi-faceted knowledge . Today, newyorkcityvoices.org would like to introduce to you Machine Learning Basics What Is Machine Learning? Introduction To Machine Learning Simplilearn. Following along are instructions in the video below:
“Know humans learn from their past experiences and machines follow instructions given by humans. But but what if humans can turing the machines to learn from the past data. And what humans can do act much faster well that s called machine learning. But it s a lot more than just learning.
It s also about understanding and reasoning so today we will learn about the basics of machine learning. So that s paul he loves listening to new songs. He either likes them or dislikes. That paul decides this on the basis of the songs tempo johner intensity.
And the gender of voice for simplicity. Let s just use tempo and intensity for now. So here tempo is on the x. Axis ranging from relaxed to fast.
Whereas intensity is on the y axis ranging from light to soling. We see that paul likes the song with fast tempo and soaring intensity. While he dislikes a song with relaxed tempo and light intensity. So now we know paul s choices.
Let s see paul listens to a new song. Let s name it a song a song a has fast tempo and a soaring intensity. So it lies somewhere here looking at the data can you guess. Where the ball will like the song or not correct.
So paul likes the song by looking at paul s past choices. We were able to classify the unknown song very easily right let s say now paul listens to a new song. Let s label..
It as song pete so song b lies somewhere. Here with medium tempo and medium intensity. Neither relaxed nor fast neither light nor. Soaring.
Now can you guess where the paul likes it or not not able to guess with this paul will like it or dislike. It. Other choice is unclear correct we could easily classify song a. But when the choice became complicated as in the case of song p.
Yes. And that s where machine learning comes in let s see how in the same example for song p. If we draw a circle around the song b. We see that there are four words for like whereas one would for dislike if we go for the majority words.
We can say that paul will definitely like the song. That s all this was a basic machine learning algorithm also it s called k. Nearest neighbors. So this is just a small example in one of the many machine learning algorithms quite easy right believe me it is but what happens when the choices become complicated as in the case of song p.
That s when machine learning comes in it learns the data builds the prediction model and when the new data point comes in it can easily project for it. More. The data better. The model higher will be the accuracy.
There are many ways in which the machine learns it could be either supervised learning. Unsupervised learning or reinforcement learning. Let s first quickly..
Understand supervised learning. Suppose your friend gives you 1 million coins of three different currencies. Say one to be one euro and one there huh each coin has different weights for example a coin of one rupee weighs three grams one euro weighs seven grams and one their own weighs four grams your model will predict the currency of the coin here your weight becomes the feature of coins while currency becomes. The label when you feed this data to the machine learning model.
It learns which feature is associated with which slip for example it will learn that if a coin is of three grams. It will be a one rupee coin. Let s give an you going to the machine on the basis of the weight of the new coin your model will predict the currency hence supervised learning uses labels data to train the model. Here.
The machine. Knew. The features of the object and also the labels associated with those features on this note. Let s move to unsupervised learning.
And see the difference. Suppose you have cricket data set of various players with their respective scores and thickets taken when you feed this data set to the machine. The machine identifies the pattern of player performance. So it plops.
This data with the respective achatz on the x axis. While runs on the y axis while looking at the data. You will clearly see that there are two clusters. The one cluster are the players who scored high runs and took less wickets while the other cluster is of the players who scored less runs.
But took many wickets so here we interpret these two clusters as batsman and bowlers. The important point to note. Here is that there were no labels of batsmen boulos..
Hence. The learning with unlabeled data is unsupervised learning. So we saw a supervised learning. Where the data was labeled and the unsupervised learning.
Where the data was unlabeled and then there s reinforcement learning. Which is a reward based learning or we can say that it works on the principle of feedback here let s say you provide the system with an image of a dog and ask it to identify it the system identifies it as a cat so you give a negative feedback to the machine saying that it s a dog s image. The machine will learn from the feedback and finally if it comes across any other image of a dog. It will be able to classify it correctly that is reinforcement learning to generalize machine learning model let s see a flowchart input is given to a machine learning model which then gives the output according to the algorithm applied if it s right we take the output as a final result else we provide feedback to the train model and ask it to predict until it learns i hope you ve understood.
Supervised and unsupervised learning. So let s have a quick quiz. You have to determine whether the given scenarios use the supervised or unsupervised learning simple right so now you want facebook recognizes your friend in a picture from an album of tagged photographs scenario. 2.
Netflix recommends new movies based on someone s past movie choices this in aisle. 3. Analyzing bank data for suspicious transactions and flagging fraud transactions think wisely and comment below your answers moving on don t you sometimes wonder how is machine learning possible in today s era. Well that s because today.
We have humongous data available. Everybody is online either making a transaction or just surfing. The internet and that s generating a huge amount of data every minute and that data my friend is the key to analysis also the memory handling capabilities of computers have largely increased which helps them to process such a huge amount of data at hand. Without any delay.
And yes computers now have great computational powers. So there are a lot of applications of machine learning out there to name a few machine learning is used in healthcare. Where diagnostics are predicted for doctors review..
The sentiment analysis that the tech giants are doing on social media. Is another interesting application machine learning fraud detection in the finance sector and also to predict customer churn in the e commerce sector. While booking the gap. You must have encountered surge pricing.
Often where it says. The farrow field trip. Has been updated continue cooking yes. Please i m getting late for office.
Well. That s an interesting machine learning model. Which is used by global taxi giant uber and others. Where they have differential pricing in real time.
Based on demand. The number of cars available bad weather rush hour etc. So they use the surge pricing model to ensure that those who need a cab can get one also it uses predictive modeling to predict where the demand will be high with the goal that drivers can take care of the demand and surge pricing can be minimized great. Hey siri can you remind me to book a cab at 6.
Pm. Today ok i ll remind you thanks comment below some interesting everyday examples around you where machines are learning. And doing amazing jobs. So that s all for machine learning basics today from my site.
Keep watching the space for more interesting videos until then happy. ” ..
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