Machine Learning For Beginners From Zero Level
Machine Learning is the term which we have been Hearing Now everywhere. When I was like complete Noob having No Idea what ML is I used to Think does ML means that Machine is learning? But Then how can Machine learn By themselves? Such silly questions came to My Mind when I started with. We Have heard people talking about machine learning but only have a fuzzy idea of what that means.
So this is a kind of Guide for them who are willing to start there Journey with ML.
“Most of the knowledge in the world in the future is going to be extracted by machines and will reside in machines” — Yann LeCun, Director of AI Research, Facebook
So Starting With the First question what is ML?
Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves. and later improve from experience without being explicitly programmed. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
So How Does Process of Learning Begins?
The process of learning begins with the feeding of data, Giving instruction, in order to look for patterns/Observation in data and later make better decisions in the future on new examples/Data that we provide.
Machine “learns” really by using old/Past data to get information about what’s the most likely that will happen. If the old data looks a lot like the new data, then the things you can say about the old data will probably be relevant to the new data. It’s like looking back to look forward.
Application of Machine Learning Algorithm in Real Scenarios?
- Retail :
Machine learning algorithms are probably behind some of online retailers Companies such as Amazon use this technology to offer a highly-personalized service: Online recommendations. Machine learning allows retailers to offer you personalized recommendations based on your previous purchases or activity.
2. Voice recognition systems such as Google Assistant and Cortana use machine learning to imitate human interaction.
3 Google Maps analyze the speed of Traffic through anonymous location data from smartphones. This enables Google to reduce travel time by suggesting Faster Routes.
4. Google Search engine offers recommendations based on previous user search
5. Spam Detection: Given email in an inbox, identify those email messages that are spam and those that are not. Having a model of this problem would allow a program to leave non-spam emails in the inbox and move spam emails to a spam folder.
Types of ML Algorithm?
Machine Learning Algorithm Basically can be Divided into three
- Supervised Learning(Task Driven)
- Unsupervised Learning(Data-Driven)
- Reinforcement Learning(Learning From Environment)
Suppose we have Data set having columns X, Y where X is input variable and Y Is output variable
Supervised learning is where we have input variables (x) and an output variable (Y) and use an algorithm to learn the mapping function from the input to the output.
Y = f(X)
The goal is to approximate the mapping function so well that when you have new input data (x) that we can predict the output variables (Y) for that data.
It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. We know the correct answers, the algorithm iteratively makes predictions on the training data and is corrected by the teacher. Learning stops when the algorithm achieves an acceptable level of performance.
Supervised learning Example:
• suppose you had a basket and it is filled with some fresh fruits your task is to arrange the same type fruits at one place.
• suppose the fruits are apple, banana, cherry, grape.
• so you already know from your previous work that, the shape of each and every fruit so it is easy to arrange the same type of fruits at one place. here your previous work is called as train data in data mining.
• so you already learn the things from your train data, This is because of you have a response variable which says you that if some fruit has so and so featured it is grape, like that for each and every fruit.
• This type of data you will get from the train data.
• This type of learning is called supervised learning.
- So you already learn things so you can do your job confidently.
unsupervised learning unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own to discover and present the interesting structure in the data.
suppose you had a basket and it is filled with some fresh fruits your task is to arrange the same type fruits at one place.
• This time you don’t know anything about those fruits, you are first time seeing these fruits so how will you arrange the same type of fruits.
• What you will do first you take on fruit and you will select any physical characteristics of that particular fruit. suppose you taken colour.
• Then you will arrange them base on the colour, then the groups will be something like this.
• RED COLOR GROUP: apples & cherry fruits.
• GREEN COLOR GROUP: bananas & grapes.
- so now you will take another physical character as size, so now the groups will be something like this.
- here you didn’t know to learn anything before means no train data and no response variable.
- This type of learning is known as unsupervised learning.
Suppose we have Data set having columns X, Y where X is input variable and Y Is output variable
Supervised learning is where we have input variables (x) and an output variable (Y) and use an algorithm to learn the mapping function from the input to the output.
Y = f(X)
The goal is to approximate the mapping function so well that when you have new input data (x) that we can predict the output variables (Y) for that data.
It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. We know the correct answers, the algorithm iteratively makes predictions on the training data and is corrected by the teacher. Learning stops when the algorithm achieves an acceptable level of performance.
Supervised learning Example:
• suppose you had a basket and it is filled with some fresh fruits your task is to arrange the same type fruits at one place.
• suppose the fruits are apple, banana, cherry, grape.
• so you already know from your previous work that, the shape of each and every fruit so it is easy to arrange the same type of fruits at one place. here your previous work is called as train data in data mining.
• so you already learn the things from your train data, This is because of you have a response variable which says you that if some fruit has so and so featured it is grape, like that for each and every fruit.
• This type of data you will get from the train data.
• This type of learning is called supervised learning.
- So you already learn things so you can do your job confidently.
unsupervised learning unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own to discover and present the interesting structure in the data.
suppose you had a basket and it is filled with some fresh fruits your task is to arrange the same type fruits at one place.
• This time you don’t know anything about those fruits, you are first time seeing these fruits so how will you arrange the same type of fruits.
• What you will do first you take on fruit and you will select any physical characteristics of that particular fruit. suppose you taken colour.
• Then you will arrange them base on the colour, then the groups will be something like this.
• RED COLOR GROUP: apples & cherry fruits.
• GREEN COLOR GROUP: bananas & grapes.
- so now you will take another physical character as size, so now the groups will be something like this.
- here you didn’t know to learn anything before means no train data and no response variable.
- This type of learning is known as unsupervised learning.
Reinforcement learning is one type of Machine learning. In a single sentence, in this learning process, a machine learns using trial and error method. Here basically, we give the machine 2 instructions.
1. Try all possible ways.
2. From your experience avoid errors and increase the success rate.
Suppose, we have a robot. There is a fire in front of it. The robot can do 2 things. Whether it can directly jump into the fire or run away from it.
At first, it will try both ways. Jump into the fire and fail. Then again it will run away and survive. The robot will remember it. Next time when it sees the fire again, it will run away. This is the basic concept of reinforcement learning.
Difference between Supervised Learning, Unsupervised Learning?
Supervised Learning: When teaching an agent something you provide with an answer sheet where the agent can see the answer after its own calculation if the answer is right it will do the same thing for That Particular range of Input. Example Teacher Teaching to Students.
Unsupervised Learning: There is no answer sheet and the agent is all his own. It will try to curb itself. Self-Study No teacher available.
That’s all for this Blog Hope you learn the basic of Machine learning and its Type.
Keep Reading Keep Learning!!!