## 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.
- Regression
- Classification

**Regression :**

- 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,
- yes/no,
- true/false,
- red/green/blue,
- 1st/2nd/3rd/4th, etc.

**Classification :**

- Separate data in to distinct classes.
- e.g. : Color, shape, categorized data.
- It has 2 type :
- Decision Tree
- Support Vector Machine

**Unsupervised Learning :-**

**Clustering :**

- Analyzing a grouping data which does not include pre-labeled class attributed.
- Algorithms :
- K-means
- Hierarchical Clustering

**Association :**

- Discover probability of co-occurrence of item in a collection
- Algorithms :
- Apriori
- 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)

*Source: Artificial Intelligence on Medium*