What is inside the machine learning? What you need to learn to get started?

Today if we talk about artificial intelligence, The first thing come in our mind is machine learning. So what is actually machine learning is? What will you need to know before you start learning? How much mathematical knowledge you should have? Which programming language you need to learn? Today, In this article we are going to cover all the topics related to Machine learning.

Many of you are still think that AI and machine learning are the same things. But they are not completely same. AI is whole as a parent itself and machine learning is a subpart of machine learning. Deep learning is also another subpart in which many types of algorithms and mathematics stuff goes on.

So, what is Artificial Intelligence? If we do some task with intelligence it is called human intelligence. If we add human intelligence into a machine, then it is called Artificial Intelligence. To do that we need to train our machine to perform a certain task. Learning that task by doing it thousands of time, and learning from the past data is called machine learning.

We understood what is machine learning and AI. So, now which programming language is suitable for it or you need to learn?. There are many programming languages you can go with that, like Python,C++, JS, Java, Lisp, R, GO, etc. But Python is more popular for going with AI because it has the simplest syntax, easy to learn, availability of high quality libraries which helps to code easier. Java is also a great choice, It can be lengthy but it is very powerful and easy for debugging. Java is widely used in Bigdata.

Now the question is how much Mathematics should I know before to get started with it? You need to know about Linear algebra, Probability, and distribution, Statistics, Vector Calculus, Matrix Decomposition, etc.

Mainly there are three types in machine learning,
1) Supervised Learning
2) Unsupervised Learning
3) Reinforcement Learning

1) Supervised Learning:- Supervised learning is the machine learning task of getting function from the labeled data. For example, We have 10 images and we have to find out is this a car or not so, It is called supervised learning. This kind of learning needs initial data. If you have that initial data then this type of learning is used. There are many algorithms are used in this learning like Logistic Regression, Decision Tree, Gradient- Boosted Tree, Multilayer Perceptron, One-vs-rest, Naive Bayes, Kernel Approximation, Support Vector Machine, Random Forest.

2) Unsupervised Learning:- Unsupervised Learning is the machine learning vice a versa of supervised learning. Here you don't have any prior labeled data. Machine learning from itself by guessing probabilities and density of the input data. One of the applications of this learning is anomaly detection, Clustering, Association mining, etc. There are algorithms used in this learning are K-Means, hierarchical, Gaussian Mixture, Neural Networks, Hidden Markov Model.

3) Reinforcement Learning:- Reinforcement learning is another technique of machine learning where it focuses on decision making. Where Machine learns from its trial and error. For example, There is a robot who is playing chess. So, It has to know all the functions of the game properly and after getting know it needs to know how to apply and when. After playing thousands of time it learns how to play that is called as Reinforcement Learning. There are many algorithms used in this learning are Simple Linear Regression Model,  Lasso Regression, Logistic Regression, Support Vector Machine, etc.

So, there are the main three types of machine learning. in this article, we covered so many confusions about machine learning and AI, how to start, What to know everything. So, If you found useful then make sure to share it with your friends. Leave a comment below if you have any question. We will back with a new article soon so stay tuned with us. Peace out.

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