AI Fundamentals

From linear regression to deep neural networks: the math and logic behind machine learning — covering gradient descent, backpropagation, and industrial AI

How algorithms predict machine failures before they happen: from vibration and thermal data to machine learning models

From sensor to database — how to collect clean, structured data from machines to feed AI models

How smart cameras detect defects, read barcodes, and monitor production lines — CNN, YOLO, and industrial vision

How to build a live digital model of a machine or production line — simulation, optimization, and failure prediction

How algorithms detect abnormal values in sensor data — from simple statistics to Autoencoders

Analyzing time-varying sensor data — trends, seasonality, ARIMA, and LSTM models for industrial predictions

Decision trees, SVM, and random forests — how machines automatically classify good parts from defective ones

Deep neural networks CNN, RNN, and Transformer — practical applications in visual inspection, maintenance, and control

Agent, environment, and reward — how systems learn to optimize industrial processes through trial and error

How machines understand maintenance reports and fault logs — information extraction and automatic fault classification

Data bias, transparency, and accountability — ethical challenges when deploying AI in real factories

Installing Python, Jupyter, NumPy, and Pandas — setting up a sensor and machine data analysis environment step by step

Reading CSV files and databases — handling missing and outlier values and visualizing data with charts

Mean, median, standard deviation, and normal distribution — understanding machine behavior through numbers

How a model learns the relationship between temperature and vibration — simple and multiple regression with scikit-learn

Decision trees, random forests, and SVM — building a model that automatically classifies product quality from sensor data

K-Means and DBSCAN — discovering different behavior groups in your machines without predefined labels

Isolation Forest and Autoencoder — building a system that automatically detects abnormal machine behavior before failure

Seasonal decomposition, trends, and forecasting — predicting when a motor needs maintenance from historical vibration data

Accuracy, recall, confusion matrix, and cross-validation — avoiding overfitting and ensuring real performance on the factory floor

Exporting models, building APIs, and continuous monitoring — moving your model from experiment to a 24/7 production system