ML-Introduction_MachineLearningTech.pptx
- 1. What is Machine Learning? Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
- 3. Types of ML Algorithm ML Model Supervised Classification Regression Unsupervised Clustering Association Reinforcement “Predicting future is not magic, It’s ARTIFICIAL INTELLIGENCE” — Dave Waters
- 4. Supervised Learning Supervised learning, as the name indicates, has the presence of a supervisor as a teacher. Basically supervised learning is when we teach or train the machine using data that is well labelled. Which means some data is already tagged with the correct answer. After that, the machine is provided with a new set of examples(data) so that the supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labelled data.
- 5. Unsupervised Learning Unsupervised learning is the training of a machine using information that is neither classified nor labelled and allowing the algorithm to act on that information without guidance. Here the task of the machine is to group unsorted information according to similarities, patterns, and differences without any prior training of data.
- 6. Classification Problem Classification is the process of grouping things according to the similar features they share. Predicted value is Categorical Binary Classification Spam Email Will Customer buy Life Insurance Multi Class Classification Which party a person is going to vote? Which type of flower is it?
- 7. Regression Problem A regression problem is when the output variable is a real or continuous value, such as “salary” or “weight”. Example Home Prices Weather Stock Price Predicted Value is Continuous
- 8. Clustering and Association(Unsupervised Learning) Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behaviour. Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.
- 10. Reinforcement Learning Reinforcement Learning is a feedback-based Machine learning technique in which an agent learns to behave in an environment by performing the actions and seeing the results of actions. For each good action, the agent gets positive feedback, and for each bad action, the agent gets negative feedback or penalty.