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Running TensorFlow Lite (TFLite) on a Raspberry Pi is an excellent way to deploy efficient, edge-based machine learning models for tasks like object detection, image classification, or audio recognition
Machine learning trains computers to learn from data and make decisions without being explicitly programmed for every single task.
SECTOR:
Manufacturing, Process Control, OEM Systems, Factory Automation, Testing Equipment, and Measurement Instrumentation
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.
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/18Wy4SgDS88V5wD7Dx46vVdCIVSCEA7pB
Fahrenheit =Celsius *1.8 +32
...Main Types of Machine Learning
Supervised learning: The computer trains on labeled data (such as emails marked specifically as "spam" or "not spam") to learn how to classify future inputs correctly.
Unsupervised learning: The computer analyzes unlabeled data to find hidden patterns, groupings, or structures on its own (such as customer segmentation).
Reinforcement learning: The computer learns a task through trial and error, receiving rewards for correct actions and penalties for mistakes (commonly used in robotics and self-driving cars). [1, 2]
Semi-supervised learning: Uses a small amount of labeled data combined with a larger pool of unlabeled data to improve accuracy when labeling is expensive or difficult.
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.
How It Works
Traditional programming: Humans write a specific recipe of rules, and the computer follows them to get an answer.
Machine learning: Humans feed the computer lots of data and examples. The computer figures out the rules and patterns all by itself.
Prediction: Once the computer learns these patterns, it uses them to make smart guesses or decisions about new, unseen data
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
BOOK:
https://www.nrigroupindia.com/e-book/Introduction%20to%20Machine%20Learning%20with%20Python%20(%20PDFDrive.com%20)-min.pdf
https://64studio.com/MagPi67.pdf
https://mlsysbook.ai/vol1/assets/downloads/Machine-Learning-Systems-Vol1.pdf
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
Month 2 — Machine Learning + AI
Month 2 — Machine Learning + AI
Week 5 — Machine learning fundamentals
Week 5 — Machine learning fundamentals
Now learn:
What is machine learning?
Training data
Test data
Features
Labels
Regression
Classification
Accuracy
A good free course is freeCodeCamp's Machine Learning with Python and Scikit-Learn. It covers regression, classification, decision trees, random forests, gradient boosting and practical projects. (YouTube)
Project 5: Predict Equipment Failure
Give the model:
temperature
vibration
current
operating_hours
and have it predict:
NORMAL
or
LIKELY FAILURE
This is where your previous engineering experience becomes particularly useful.
Week 6 — Scikit-learn
Week 6 — Scikit-learn
Learn:
Linear regression
Logistic regression
Decision trees
Random forests
Train/test split
Model evaluation
Project 6: Predict a Measurement
For example:
Given temperature, pressure and current, predict whether an industrial machine is operating normally.
Don't worry about achieving perfect accuracy. Learn the complete process:
DATA
↓
CLEAN
↓
TRAIN
↓
TEST
↓
EVALUATE
↓
PREDICT
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