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 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.
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).
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).
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.. 🔏