Model Training

Model training is the core process in machine learning where an algorithm learns patterns from data to make predictions or decisions. It involves feeding training data into a model, adjusting its parameters through optimization techniques like gradient descent, and iteratively improving performance based on a loss function. This process transforms raw data into a functional predictive model that can generalize to new, unseen data.

Also known as: ML Training, Model Learning, Algorithm Training, Neural Network Training, Supervised Learning

Why learn Model Training?

Developers should learn model training when building machine learning systems for tasks like image recognition, natural language processing, or recommendation engines. It's essential for creating models that can automate decision-making, classify data, or predict outcomes in fields such as healthcare, finance, and autonomous systems. Mastery of training techniques ensures models are accurate, efficient, and robust against overfitting or bias.

Learning Resources

• course: Machine Learning Crash Course by Google
• docs: Scikit-learn Documentation on Model Training
• course: Deep Learning Specialization on Coursera
• tutorial: PyTorch Tutorials on Training Neural Networks
• book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Book