Embedded Files
SECTOR:
Manufacturing, Process Control, OEM Systems, Factory Automation, Testing Equipment, and Measurement Instrumentation
Google Colab (short for Collaboratory) is a free, cloud-based Jupyter Notebook environment hosted by Google. It allows anyone to write and execute arbitrary Python code through a web browser, making it highly popular for machine learning, data analysis, and education
Popular Online Python Options
Google Colab: A cloud-based notebook ideal for data science, machine learning, and sharing code blocks.
https://colab.research.google.com/
https://colab.research.google.com/drive/18Wy4SgDS88V5wD7Dx46vVdCIVSCEA7pBREFERENCES
https://www.youtube.com/watch?v=hmVh2ddVCK4
https://www.youtube.com/watch?v=29ZQ3TDGgRQ
BOOKS:
https://python-course.eu/books/bernd_klein_python_and_machine_learning_a4.pdf
https://github.com/CodeWithHarry/The-Ultimate-Python
Course/blob/main/The%20Ultimate%20Python%20Handbook.pdf
If you are using Google Colab
If you are using Google Colab
The easiest way is to upload the CSV.
Run this in a new cell:
from google.colab import files
uploaded = files.upload()
A file-selection window will appear. Select:
table1_python.csv
Then run:
df = pd.read_csv("table1_python.csv")
df
If you are using Google Colab
If you are using Google Colab
The easiest way is to upload the CSV.
Run this in a new cell:
from google.colab import files
uploaded = files.upload()
A file-selection window will appear. Select:
table1_python.csv
Then run:
df = pd.read_csv("table1_python.csv")
df
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
from google.colab import files
uploaded = files.upload()
df =pd.read_csv("table1_python.csv")
df
reg =linear_model.LinearRegression()
reg.fit(df[['area']],df.price)
reg =linear_model.LinearRegression()
reg.fit(df[['x_sp']],df.y_value)
reg.predict([[75]])
SAMPLE#7
import matplotlib.pyplot as plt
import numpy as np
# vectors from table
x =[80,90,100,110]
y =[80,90,100,110]
# drwa line
x_lim =[70,120]
x_lim_ones =np.c_[np.ones(2),x_lim]
y_lim =x_lim_ones.dot(0.99)
plt.plot(x_lim,y_lim,'r-')
plt.scatter(x,y)
EXAMPLE #1
python test
python test
print ("Hello Hmillan 2026 Sept 10")
REFERENCE:
EXAMPLE#6
import matplotlib.pyplot as plt
import numpy as np
# vectors from table
x =[80,90,100,110]
y =[80,90,100,110]
#drwa plot
plt.scatter(x,y)
EXAMPLE #2
import numpy as np
import matplotlib.pylab as plt
import matplotlib
#data for plot
t = np.arange(0.0,2.0,0.01)
s = 1 + np.sin(2*np.pi *t)
fig, ax =plt.subplots()
ax.plot(t,s)
print ("Hello Hmillan 2026 Sept 10")
ax.set(xlabel ='time (s)', ylabel ='volage (mv)',
title ='About as simple as it gets, folks')
ax.grid()
EXAMPLE #3
from google.colab import ai
response = ai.generate_text("What is the capital of France?")
)
EXAMPLE #4
TP = 42
TN = 32
FP = 8
FN = 18
Accuracy = (TP + TN)/(TP + TN + FP + FN)
print(Accuracy)
EXAMPLE #5
from matplotlib import pyplot as plt
import numpy as np
# Generate 100 random data points along 3 dimensions
x, y, scale = np.random.randn(3, 100)
fig, ax = plt.subplots()
# Map each onto a scatterplot we'll create with Matplotlib
ax.scatter(x=x, y=y, c=scale, s=np.abs(scale)*500)
ax.set(title="Some random data, created with JupyterLab!")
plt.show()
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