APROACH:
Instead of writing rigid step-by-step rules, developers feed large amounts of data into an algorithm so the computer can find patterns, build a model, and improve its performance over time through experience.
Machine learning trains computers to learn from data and make decisions without being explicitly programmed for every single task.

HOW TO START:
Traditional vs. ML Approach: Traditional engineering uses domain knowledge to write explicit mathematical models and algorithms. Machine learning instead defines a model class and uses training data to automatically fit parameters

What is machine learning in simple words?Machine learning is a way of teaching computers to learn from examples and data instead of making humans write strict step-by-step rules for every task.

Python 'linear regresion'

y = angle0 + angle1.x

import matplotlib.pyplot as plt

import numpy as np

# vectors from table

x =[80,90,100,110]

y =[80,90,100,110]

# add bias teras

x_one =np.c_[np.ones(4),x]

x_ones

array([1.,80],

[1.,90.]

[1.,100..]

[1.,110.])

# equation

angle0 =np.linalg.inv(x_ones.T.dot(x_ones)).dot(x_ones.T).dot(y)

angle0

array([-140.67,1.26])

#drwa plot

plt.scatter(X,y,s=40,c='#06')

# drwa line

x_lim =70,120]

x_lim_ones =np.c_[np.ones(2),x_lim]

y_lim =x_lim_ones.dot(angle0)

plt.plot(x_lim,y_lim,'r-')

#config

plt.axis([80,90,100,110])

plt.xlabel('sp x')

plt.ylabel(y_power')

plt.title('sp vs power)

plt.grid()

# last

power_out =angle[0] + (angle[1] * 179)

power_out