What is machine learning used for?
Machine Learning is an area of study where we want machines to think like humans and act like humans. This journey of going from a machine to a human or making a machine think like a human it at least needs two things One is it needs some kind of Thinking humans can think.
Second, humans get experience, they mature, they learn from their surroundings, learn from school, learn from their parents, and so on. Somehow in the machine, we need to at least put two things that are to make the machine think and the second one is we need to see the machine gets matured by learning.
The thinking power in a machine can be brought in by some kind of Algorithm. We need to create the thinking power of the machine by putting some algorithms. Depending on the situations we can have different kinds of Algorithms.
MLis an incredibly complex topic. Machine learning is used for data analysis, prediction, automation, speech recognition, robotics, etc.
Definition of machine learning with examples?
There’s no universal definition, however, at a high level, I’d outline machine learning because of the semi-automated extraction information|of information} from data.
Let’s break that down into three parts
- ML continuously starts with knowledge, and your goal is to extract data or insight from that knowledge. You have a matter you’re attempting to answer, and you theorise that your question can be answerable victimisation the information.
- Machine-learning involves some quantity of automation. Rather than making an attempt to assemble your insights from the information manually, you’re applying some method or rule to the information employing a laptop in order that the pc will help to provide the insight.
- Machine-learning is not a fully automated process.machine-learning needs you to form several sensible selections for the method to achieve success.
Types of machine learning?
There are three types of algorithms in ML
What is machine learning algorithms?
The first issue that ML wants is Associate in Nursing algorithmic program. Then this Algorithm will provide training data and it will train them. The experience part is brought in by providing some kind of training data to the algorithm. One is we have algorithm thinking and the experience or the maturing power we can do it by providing it training data. Computers only understand numbers so when we provide numbers it gets trained very easily. But if we have text, audio, video, and images we need to convert those kinds of formats and things into
numbers. Those numbers are termed as Vector.
A vector is a collection of numbers. We have an Algorithm. We are training that Algorithm and by doing so we are having an experienced algorithm. This experience algorithm is termed as a Model. A Model is an algorithm with some kind of experience it has got from training data.
What is model in machine learning?
The Model is that the most vital a part of the ML project. To start with a Machine Learning project the first thing that should come to our mind is what kind of Model we are expecting at the end of the day. The model means a trained algorithm. When we training a machine by using some kind of training data we need to provide that training data a format of features and labels.
What is feature in machine learning?
Features are input. Features are the important characteristics That are extracted from a text and describe an object, thing, entity, or anything we want the machines to learn. We can say Red, Round, and Sweet will tell the machine to think it is an Apple. When you see a Cone Shape, Yellow and Grained machines think it is a Maize. When you see Yellow, Sweet, and Juicy the machine thinks it is a Mango. Features are inputs.
What is label in machine learning?
Labels are output. Features are inputs and labels are things that we want the machine to think about those features. Whenever we are providing training data we need to provide in terms of Features and Labels. Features are a very very important part of Machine Learning. We need to own a mechanism by that we are able to extract options from the coaching information. Somebody provides training data in text format, audio format, or in video format, we need to somehow extract features from that text, and then later on we need to label features and that will become as training input to the Machine.
What is Bag of Words in machine learning?
There are a lot of time tested mechanisms that ar on the market to extract the options. The Simplest mechanism which is available is BOW – Bag of Words. Bag of Words is a concept or a process by which we can extract features from a free text. Bag of Words is a simplifying representation of words for a big textual document.
How to learn machine learning.
Why machine learning is important.
ML makes more sense in industries with many customers with a big number of transactions and with a large amount of available data. Just to name a few retail financial services all kinds of subscription businesses like telecommunications and energy but also b2b if you think of spot markets in b2b.
if you think of machinery with all the available data coming from the sensors which currently provide data in the machines. check why certain actions for instance a price increase or decrease are recommended and then analyze whether that in your experience makes sense or not. We call that a face validity check.
If it is a purely black-box model with no information on the accuracy of that model be very cautious and then finally. 80 to 90 percent of the project time is used for data engineering and only 10 to 20 percent for the true modeling of that. Finally, always combine data scientists and true industry experts.
Machine learning makes our pricing much more accurate and precise and therefore also more profitable and that is true for b2c but is also true for b2b for instance in our b2b pier pricing models.
We have in meanwhile integrated machine learning algorithms that permanently optimize the recommended price which is given to salespeople so every single transaction influences the newly recommended price.
Machine learning also makes the pricing much more differentiated than before – one-size-fits-all is history. Finally, it enables us to change prices much faster than before. The keyword here is dynamic pricing. That works perfectly in online or in the e-tailing business but not everywhere: be cautious.
Let’s take as an example Apple has all the data you can think of to come up with the perfect dynamic pricing still what they are doing and what they’re using is one of the most boring pricing systems you can think of: different sizes, different colors and that’s it.
Why? Because they are convinced that dynamic pricing would maybe not destroy but would hurt the overall brand value. So dynamic pricing is a great approach that will lead to significant profit increases but not everywhere.
Machine learning libraries in python
Industries using machine learning?
- Government agencies using machine learning.
- Oil industries using machine learning.
- Gas industries using machine learning.
- Healthcare industries using machine learning.
- Transportation industries using machine learning.
- Financial services using machine learning.
- Automation industries using machine learning.
- Retail industries using machine learning.
- Big e-commerce companies using machine learning
- Many big companies use machine learning for data analysis and predictions. Companies like, google, yahoo, Baidu, Pinterest, Twitter, Instagram, etc.
Can we detect fake news in any language using machine learning?
Yes, We Can create an algorithm for detecting fake news and we can easily detect fake news using machine learning.
How to detect an object is moving using machine learning or deep learning?
You can detect an moving object using open CV Library in Machine learning or Deep Learning.
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