Hands-On Machine Learning with Scikit-Learn and TensorFlow

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A model trained on more realistic data will naturally perform better in real-world applications.Aurélien Géron

A machine learning project is not a one-shot process of training and deploying a model; it’s a continuous process of learning and evolution.Aurélien Géron

Never underestimate the power of a good selection of features in your model.Aurélien Géron

A common pitfall in machine learning is to test a model on the training data and assume that its performance will generalize to new instances.Aurélien Géron

Avoid overfitting by keeping the model’s complexity in check. Simpler models often perform better when tested on unseen data.Aurélien Géron

In Deep Learning, initialization and activation functions play a crucial role in defining the performance of the model.Aurélien Géron

Machine Learning is about making machines get better at a task by learning from data, instead of having to explicitly code rules.Aurélien Géron

It’s practically not possible to test an ML model on all possible instances. Therefore, we should split the dataset into training and testing sets.Aurélien Géron

Fine-tuning a model involves adjusting the hyperparameters of an already trained model so that it performs better on the task at hand.Aurélien Géron

Solving real-world problems with machine learning involves more than just training models. It involves the whole data pipeline and making decisions based on dataAurélien Géron