From image and speech recognition to predictions and personalized recommendations, machine learning has become pervasive across industries. With intuitive APIs and robust tooling for data science, Python has emerged as a top choice for machine learning applications.
By exploring some Python machine learning fundamentals, we can better grasp this technology’s inner workings to see how data transforms into decisions and insights.
Understanding Machine Learning Models
Whether predicting home prices or detecting credit card fraud, most machine learning pipelines share common high-level components:
- Dataset: Raw input data to process and learn from.
- Features: Numeric representations of raw data fed into models, like home size and location.
- Model: The learning algorithm used to make predictions from features.
- Training: Running many dataset examples through a model to update its parameters.
- Inference: Applying a trained model to make predictions on new unlabeled data.
Together these pieces enable models to learn complex patterns from data. We assess models based on prediction…
