Machine Learning: Key aspects of Machine Learning

Machine Learning is a subset of Data Science that focuses on developing algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed.

It is concerned with building systems that can automatically learn from data and improve their performance over time. Machine Learning algorithms aim to recognize patterns, make predictions, or gain insights from data by iteratively learning from examples or experiences.

Key aspects of Machine Learning

Training models with labeled examples to make predictions or classifications on new, unseen data.

Supervised learning:

Identifying patterns or structures in unlabeled data without specific output variables.

Unsupervised learning:

Teaching agents to learn from interactions with an environment to maximize rewards or outcomes.

Reinforcement learning:

Selecting or transforming relevant features from raw data to improve model performance.

Feature engineering:

Assessing the performance of models and fine-tuning them to improve accuracy or efficiency.

Model evaluation and optimization:

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