@ -24,6 +24,8 @@ In a professional setting, clustering can be used to determine things like marke
Alternately, you could use it for grouping search results - by shopping links, images, or reviews, for example. Clustering is useful when you have a large dataset that you want to reduce and on which you want to perform more granular analysis, so the technique can be used to learn about data before other models are constructed.
✅ Once your data is organized in clusters, you assign it a cluster Id, and this technique can be useful when preserving a dataset's privacy; you can instead refer to a data point by its cluster id, rather than by more revealing identifiable data. Can you think of other reasons why you'd refer to a cluster Id rather than other elements of the cluster to identify it?
Deepen your understanding of Clustering techniques in this [Learn module](https://docs.microsoft.com/en-us/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-15963-cxa)
## Getting started with clustering
[Scikit-Learn offers a large array](https://scikit-learn.org/stable/modules/clustering.html) of methods to perform clustering. The type you choose will depend on your use case. According to the documentation, each method has various benefits. Here is a simplified table of the methods supported by Scikit-Learn and their appropriate use cases:
In this lesson, we will walk through the major milestones of the history of Machine Learning and AI.
The history of Artificial Intelligence as a field is intertwined with the history of Machine Learning, as the algorithms that underpin ML fed into the development of AI. It is useful to remember that, while AI as a field of inquiry began to crystallize in the 1950s, important [algorithmical, statistical, mathematical and technical discoveries](https://wikipedia.org/wiki/Timeline_of_machine_learning) predated and overlapped this era.
## Notable Discoveries
- 1763, 1812 [Bayes Theorem](https://wikipedia.org/wiki/Bayes%27_theorem) and its predecessors. This theorem and its applications underlie inference, describing the probability of an event occuring based on prior knowledge.
- 1805 [Least Square Theory](https://wikipedia.org/wiki/Least_squares) by French mathematician Adrien-Marie Legendre. This theory, which you will learn about in our Regression unit, helps in data fitting.
- 1913 [Markov Chains](https://wikipedia.org/wiki/Markov_chain) named after Russian mathematician Andrey Markov is used to describe a sequence of possible events based on a previous state.
- 1957 [Perceptron](https://wikipedia.org/wiki/Perceptron) is a type of linear classifier invented by American psychologist Frank Rosenblatt that underlies advances in deep learning.
- 1967 [Nearest Neighbor](https://wikipedia.org/wiki/Nearest_neighbor) is an algorithm originally designed to map routes. In an ML context it is used to detect patterns.
- 1970 [Backpropagation](https://wikipedia.org/wiki/Backpropagation) is used to train [feedforward neural networks](https://wikipedia.org/wiki/Feedforward_neural_network)
- 1982 [Recurrent Neural Network](https://wikipedia.org/wiki/Recurrent_neural_network) are artificial neural networks derived from feedforward neural networks that create temporal graphs.
## 1950: Machines that Think
Alan Turing
@ -63,7 +75,7 @@ better able to frame question
## Now
AI started as a single area, now there are may parts and they cross-collaborate
AI started as a single area, now there are many parts and they cross-collaborate
[](https://www.youtube.com/watch?v=mTtDfKgLm54 "The history of Deep Learning")
> Yann LeCun discusses the history of Deep Learning in this lecture
Jen Looper
4 years ago