Let’s understanding the way of making ML like making a food

What I want to introduce is the way of making model that was called Machine Learning(ML).

There are many site to learn how to use ML. For programmer that process is easy to understand. However, man who do not know those system is difficult to understand. Therefore, in this time I want to explain this by a series of flows like making foods.

In some sites, they explain the definition of ML is not clear for begineer. I think it is so confused for you.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from data without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn by themselves.

There are many example that ML was used in our dairy life so, before proceed our main story, I want you search in google ‘ML example’ . If you available.

Index

1, collecting some foods you need 1, collecting some data you need

2, cutting those foods to cook 2, preprocessing data

3, adjusting fire to cook 3, tuning learning rate

4, tasting and evaluating the taste 4, looking some graphs to evaluate the result

5,conclusion

In here, I want to use unique format to divide situation. In the below, First, I will write situation in cooking and in the next index, I want to introduce ML steps. Please care about that.

1, collecting some foods you need 1, collecting some data you need

0, deciding what you want to eat 0, deciding what do you want to predict Today, we want to make curry. First we have to collect vegetable and beef. 1, collecting some foods you need First we have to collect vegetable and beef.

We want to predict the prices like house or other somethings. 1, collecting some data you need. In the ML , we have to collect house’s data like size, money and the place.(In detail, we can use SQL that is programming of language focus on collecting data )

2, cutting those foods to cook 2, preprocessing data

Next is cutting those vegetable that make you easier to cook when you use fire or eat foods. we want to cut those be good size that we can eat.

Data is also have to be arranged by cutting some bad dates. For example those data are biased then we have arrange data.(There are NA in the data so we have to put out this number and fill some good number.)

Id	YearBuilt	LotArea	LotFrontage
1	2003	8450	65
2	1976	9600	80
3	2001	11250	68
4	1915	9550	60
5	2000	14260	84
6	1993	14115	85
7	2004	10084	75
8	1973	10382	NA
9	1931	6120	51
10	1939	7420	50
11	1965	11200	70
12	2005	11924	85
13	1962	12968	NA
14	2006	10652	91
15	1960	10920	NA

3, adjusting fire to cook 3, tuning learning rate

Next is using spices to decide the taste of curry. You know, we do not know what material is in the spices but we know this make curry better taste. In the ML is also happened the thing. After firing, the curry will be yummy taste.

ML also have spices to improve the taste that is a neural network. we just only have to input clear data. After training This tools will output good prediction.(we can use libraries like pytorch or keras. Those tools is like good pot in cooking. If you use high price tools to cook then your curry will be better than usual. ) Tuning Learning rate is important when we use neural network. It’s like adjusting fire when you cook. In cooking we have to adjust good fire for foods not be burned. If you fail that process your bread will like be below

4, tasting and evaluating the taste 4, looking some graphs to evaluate the result

When you finish making curry, you have to taste that is good or not. This step is very important for cooker. They deserve good taste.

ML also have the step to check the taste. We have to check the output is overlapping or not and other many things. There are many factor that improve the model or not , so we have to quote those fact from graph and some output.