Don’t worry if you have no idea of what nest JS is, let’s discuss what it’s about and look more into the components used along with its features offered!
NestJS is a Node.js framework that’s intended for use with TypeScript to build efficient and scalable server-side applications.
Before getting into the climax, let’s firstly break down what both of these are and then jump deeper.
If you love frontend development, you should have some experience with React JS, this is because it’s one of the greatest frontend libraries out there. …
This is a supervised learning algorithm, if you are confused about what supervised learning is, I highly recommend checking my blog post on Quick Breakdown for Supervised Machine learning before jumping into this.
As I mentioned, this is a supervised learning algorithm and can be used for both classification problems…
Today I came across an interesting problem; assuming that you know the basics of git, we normally work with several feature branches and merge to the main or aka master branch once we are done with the features,
but consider this scenario where you have the main branch and two…
This is a supervised learning algorithm and is the most powerful algorithm under the supervised learning category, you can say. This algorithm can be used for both classification and regression.
This is a tree structure classifier where the internal nodes represent the features of a dataset, branches represent the decision…
KNN is a type of supervised machine learning algorithm and is also called as K Nearest Neighbor, which can be used for both classifications and regression predictive problems.
These are the following two properties that will define KNN well:
Deep learning is based on the branch of machine learning, which is a subset of artificial intelligence. Since neural networks imitate the human brain and so deep learning will do. In deep learning, nothing is programmed explicitly. Basically, it is a machine learning class that makes use of numerous nonlinear…
The machine is given an algorithm to analyze all possible moves at each stage of the game. The machine may select one of the moves at random. If the move is right, the machine is rewarded, otherwise, it may be penalized.
As time progresses, the machine will start to differentiate from what's right and wrong moves after several iterations would learn to solve the game puzzle with better accuracy.
In unsupervised learning, we do not specify a target variable to the machine, rather we ask the machine “what can you tell me about X”?
To be more specific, we may ask questions such as, given a huge dataset X, “what are the 5 best groups we can make out of X” or “what features occur together with the most frequently in X?”
Under unsupervised learning we have a number of types out of those the main 2 are as follows:
Here are few examples for unsupervised learning, face detection and object detection uses unsupervised learning.