# Types Of Machine Learning [](https://hackmd.io/SH93QC4RQdKfqmyzOM0Vbg) [Link to live slides](https://hackmd.io/@xlQ9WIAmSZiqBWhri7jjKw/Hkxn2mN85#/) by Nasheya Louviere ---  --- # Supervised Learning 👩‍🏫 ---  --- ### Supervised learning explained Supervised Learning involves training models using labelled datasets so that they can learn about each type of data. After the training is completed, the model is given test data to identify and predict the output. --- ### How does supervised learning work? Say you're giving a crate of assorted candy and you're given chocolate bars and strawberry hard candies. In the supervised machine learning approach, your first step will be to acquaint the machine with all the different candies one by one in this way: * If the object is like rectangular and brown it will be labled as - Chocolate Bar * If the object is red in colour and round it will be labled as - Strawberry Candy --- # Unsupervised Learning 👶🏼 --- ## How does Unsupervised Learning work?  --- Unsupervised Learning(unsupervised machine learning) uses machine learning algorithms to analyze and cluster unlabeled datasets in which hidden patterns/data groupings are discovered without the need for human manipulation. --- ### When is it used? Face recognition is a task in which system consequently distinguishes human appearances in the database. The database is put away in the system which entails face images. Whenever another image is received, it is contrasted and the database of face images as of now put away in the system. ---  --- ### What is clustering?  --- Clustering is the act of grouping unlabled data based on their contrasts/comparisons. Algorithms can be clustered can be catergorized a few ways - [ ] Exclusive - [ ] Overlapping - [ ] Hierarchical - [ ] Probabilistic --- # Reinforcement 🧠 ---  --- Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like situation. The computer employs trial and error to come up with a solution to the problem. To get the machine to do what the programmer wants, the artificial intelligence gets either rewards or penalties for the actions it performs. Its goal is to maximize the total reward. --- ### When is it used? Self-driving cars: Reinforcement learning is used in self-driving cars for various purposes such as the following.  - [ ] Controller optimisation - [ ] Scenario-based learning policies for highways - [ ] Motion planning including lane changing, parking etc - [ ] Dynamic pathing: Reinforcement learning can be used for dynamically planning the most efficient path in a grid of potential paths. --- # The End👋