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10 Machine Learning Methods that Every Data Scientist Should Know

Machine learning is a hot topic in research and industry, with new methodologies developed all the time. The speed and complexity of the field makes keeping up with new techniques difficult even for experts — and potentially overwhelming for beginners.

To demystify machine learning and to offer a learning path for those who are new to the core concepts, let’s look at ten different methods, including simple descriptions, visualizations, and examples for each one.

A machine learning algorithm, also called model, is a mathematical expression that represents data in the context of a ­­­problem, often a business problem. The aim is to go from data to insight. For example, if an online retailer wants to anticipate sales for the next quarter, they might use a machine learning algorithm that predicts those sales based on past sales and other relevant data. Similarly, a windmill manufacturer might visually monitor important equipment and feed the video data through algorithms trained to identify dangerous cracks.

The ten methods described offer an overview — and a foundation you can build on as you hone your machine learning knowledge and skill:

  1. Regression
  2. Classification
  3. Clustering
  4. Dimensionality Reduction
  5. Ensemble Methods
  6. Neural Nets and Deep Learning
  7. Transfer Learning
  8. Reinforcement Learning
  9. Natural Language Processing
  10. Word Embeddings

Read the full article, with detailed description for each method, here

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