Machine learning has not been easy to pick up and there has been lots of detours that I took all in supplementing the depth of knowledge that I thought I’d need.

### What new skills have you learned?

#### Machine learning.

Machine Learning is a field of computer science that uses statistical techniques to give computer systems the ability to learn . . . without being explicitly programmed. - Wikipedia

For more than a week, I looked at the introduction and uses of ML and lots of correlated disciplines within the field and it can get easily overwhelming.

Now that I’ve explored the data a bit, working on advancement and furthering research on this is crucial at informing further what data is all about.

#### Linear Regression.

What I have picked up under Linear Regression.

Linear regression or Ordinary Least Squares (OLS) is the simplest and most classic linear method for regression.

- Linear models are models that make a prediction using a linear function of the input features.
- Linear models for regression can be characterized as regression models for which the prediction is a line for a single feature, a plane when using two features, or a hyperplane in higher dimensions (that is when having more features).

###### Concepts

```
Finding the most correlated feature with Training and Testing Data
Model Training & fitting data.
Predicting Test Data
```

Having been able to fit a model, the next step is to evaluate its performance by predicting off the test values!

Evaluate the model performance by calculating the `residual sum of squares`

and the `explained variance score`

(R^2).

Calculate;

```
Mean Absolute Error
Mean Squared Error
Root Mean Squared Error
```

In essence finding a correlation between elements is about interpreting relationships between coefficients.

Link to notebook with exercise on Linear_Regression

So that was the Fifth week.. 🔏