Blog: Machine Learning Introduction
Machine Learning is a method of teaching machines or computers to make a prediction based on data sets and experiences.
In briefly, Machine learning is subset of artificial intelligence that automates analytical model building by using an algorithm.
It is like a system to ask questions and answers.
1. Application of ML
- Search Engine Result
- Number Plate Recognition
- Voice Recognition
- Dream Reader
2. How to ML Works
It has some phases that you should want to know.
- Phase 1 (Learning)
2. Phase 2 (Prediction)
3. ML Work-flow
4. Type of ML
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
5. Follow these Steps :-
1. Identify what are the variables :
Firstly, We want identify what are variables that we suppose to use this model. After find variables, if we have input and output dataset, we want to divide 2 parts according to their types.
Two type of Variables :-
- Independent Variables:- This variable cannot change values via any effort. We use these variables as X-axis. e.g. Humidity, Pressure, Soil Status, ID Number and etc.
- Dependent Variables:- It depends on others, as well as we can tell these as predicted values that we hope to predict. Thus, We use these variables as Y-axis. e.g.: Salary, Demand, Rate and etc.
2. Identify Which Variables are Numerical or not
3. Identify Which Algorithm Type that you want to follow :
- Supervised Learning :-
- If we use this type, we should have past data sets to learn and make future predictions.
- Input variable(X) and Output variable(Y) and you use and algorithm to the learn mapping function from input to the output.
- Unsupervised Learning :- If you don’t have past data sets, we can use this type. Then we can analyzing and grouping data.
- Reinforcement Learning :- According to past action and feedback, This model can learn. Output data depends on the state of the current input data and Next input data depends on the output of the previous input. like chess game.
4. Identify Which Algorithm that you want to follow :
Supervised Learning :-
- It can divide 2 parts.
- if you get Numeric data as predicted value, you can use this type.
- e.g. : prediction of housing Price, Temperature, like that.
- It has 2 Parts.
Linear Regression :
Simple Linear Regression :
- If you have one variable as X you can use this.
Multiple Linear Regression :
- If you have multiple variable as X you can use this.
Polynomial Regression :
- non-linearly separable data
Logistic Regression :
- outcome (dependent variable) has only a limited number of possible values.
- outcome is categorical in nature.
- For instance,
- 1st/2nd/3rd/4th, etc.
- Separate data in to distinct classes.
- e.g. : Color, shape, categorized data.
- It has 2 type :
- Decision Tree
- Support Vector Machine
Unsupervised Learning :-
- Analyzing a grouping data which does not include pre-labeled class attributed.
- Algorithms :
- Hierarchical Clustering
- Discover probability of co-occurrence of item in a collection
- Algorithms :
- FP- Growth
- e.g. :
- 2 customers are buy foods, predict which foods are 3rd customer will buy next.
Reinforcement Learning :
- It is learning by interacting with a space or and a environment.
- It select its actions on basis of its past experiences and also by new choices.
5. Limitation of Machine Learning :
- failed to solve bellow those:
- Crucial problem of AI
- Natural Language processing
- Image Recognition
- not useful while working with high dimensional data(large no. of inputs and outputs)