What is Machine Learning
We recognize people analyze from their past studies and machines comply with commands given by humans.
But what if people can teach the machines to study from the past information and do what people can do a lot quicker properly, It is known as Machine Learning.
It’s lots greater than simply learning, it is also approximately knowledge and reasoning.
For Example: You love listening to new songs either like them or dislike them.
You decide this on the basis of the song’s tempo, genre 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 soaring we see that you like the song with fast tempo and soaring intensity while you dislike it.
The song has a relaxed tempo and light intensity so now we know Your choices.
Let’s say you listen to a new song, let’s name it as A song that has fast tempo and a soaring intensity.
So it lies somewhere here looking at the data. Can you guess whether you will like the song or not, correct so you like this song by looking at your past choices.
we were able to classify the unknown song very easily right let’s say now you listen to a new song.
Let’s label it as song B so song B lies somewhere here with medium tempo and medium intensity neither relaxed nor fast nor light nor soaring.
Now can you guess whether you like it or not? Not able to guess whether you will like it or dislike it are the choices unclear, correct.
We could easily classify song A but when the choice became complicated as in the case of song B yes and that’s where machine learning comes in.
How Machine Learning Works
Machine Learning teaches a computer how to perform a task without explicitly programming it to perform said task.
Instead feed data into an algorithm to gradually improve outcomes with experience similar to how organic life learns.
The time period was coined in 1959 with the aid of using Arthur Samuel at IBM.
Who changed into developing artificial intelligence that could play checkers 1/2 of a century later.
And predictive models are embedded in a lot of the products we use each day which carry out essential jobs.
One is to categorize data like is there any other automobile on the street or does this affected person have cancer.
The difference is to make predictions about future results like will the stock move up or which YouTube video do you want to watch next.
Machine Learning Process
The first step in the process is to acquire and clean up lots and lots of data.
The data represents the problem the better the results garbage in garbage out.
The data needs to have some kind of signal to be valuable to the algorithm for making predictions.
And data scientists perform a job called feature engineering to transform raw data into features that better represent the underlying problem.
The next step is to separate the data into a training set and testing set the training data is fed into an algorithm to build a model then.
The testing data is used to validate the accuracy or error of the model.
Machine Learning Models
What makes AI effective in the enterprise? But there’s a science to building the right model.
Many organizations are using AI for a wide range of business applications. But AI is not a one-size-fits-all technology.
Every AI project is customized to solve a specific business problem with machine learning models.
These models which rely on data and algorithms are what address the project’s needs. For many organizations.
Machine learning model development is a new and daunting activity, but some established methodologies help ensure success.
We break down the process of building a machine learning model into seven steps.
After going through this brief about Machine Learning, do you still have questions about it? Let us know in the email at [email protected] or contact us at +91-124 4294496