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Machine learning trains computers to learn from data and make decisions without being explicitly programmed for every single task.
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.
...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://scispace.com/pdf/a-brief-introduction-to-machine-learning-for-engineers-19ffkee4gx.pdf
https://www.nrigroupindia.com/e-book/Introduction%20to%20Machine%20Learning%20with%20Python%20(%20PDFDrive.com%20)-min.pd
https://64studio.com/MagPi67.pdf
https://mlsysbook.ai/vol1/assets/downloads/Machine-Learning-Systems-Vol1.pdf
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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