Machine learning is changing the way the world thinks and operates. Let’s face it, when the words “Machine Learning Algorithm” present themselves in a room full of peers, everyone takes a deep breath. Some are confused by the thought of math; others are thinking, “Oh no, which algorithm do we use for this problem?”
Is Machine Learning AI?
Machine Learning is a subset of artificial intelligence (AI) that uses data and analytics to predict, detect, and learn outcomes using different algorithms, data, and processes.
Machine learning lets us use a wide variety of collected data to automatically derive the pattern hidden in the data. The patterns are then used to form the model, which is applied to new data to provide a more well-versed and adaptive prediction.
What Do I Need to Know?
You need to differentiate between a boy and a girl? If you want to do this quickly, use a decision tree or Naïve Bayes. Or for a more accurate prediction, use Neural Networks and also Gradient Boosting Trees!
Before any model can be chosen, we always need to understand the data, goal, and metrics.
- How is data collected?
- What does each column represent?
- Assumption, distribution, and relationship
- Predict, describe, and explore
What is a Machine Learning Model?
A model in Machine Learning (ML) is the trained algorithm used to identify patterns in the data.
What the Main Types of Machine Learning Algorithms?
- Reinforcement Learning is best worked on an action-reward principle. An agent learns to reach its goal by iteratively calculating the reward of its actions.
- With Supervised Learning, algorithms model the relationship between features (independent variables) and a label (target) given a set of observations. The model is then used to predict the label of new observations using the features.
- With Unsupervised Learning, algorithms work to try and find the structure in unlabeled data.
When choosing an algorithm, always think about accuracy, training time, and ease of use!
For further information, reach out to Martin Townend.