1. Introduction
Data analytics is the process of examining data to uncover insights and make informed decisions. Machine learning is a subset of artificial intelligence that empowers data analytics by automating predictions based on data patterns.
2. What is Machine Learning?
Machine learning is a technology that enables computers to learn and make predictions or decisions without being explicitly programmed. It's divided into three main types: supervised learning, unsupervised learning, and reinforcement learning.
3. Data Collection and Preprocessing
Quality data is essential for machine learning. Data preprocessing includes cleaning and preparing the data for analysis.
4. Supervised Learning
Supervised learning is used for tasks like classification and regression. It involves using labeled data to train a model.
5. Model Evaluation
To understand how well your model performs, you can use evaluation metrics like accuracy, which measures how often the model is correct.
Conclusion
Machine learning is a powerful tool that can enhance your data analytics efforts. By understanding its basics and applying it to your data, you can make more accurate predictions and gain valuable insights.
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