What do you know about machine learning?
Does it attract you? You want to know more about it?
I prepared this article for you just to explain what’s machine learning.Here’s so basic information, the first thing is that machine learning a great part of Artificial Intelligence and it generally is to simplify the structure of data and integrate it into models that can be easily used by people add to that technology user today has benefitted from machine learning.
Face ID technology allows social media platforms to help users tag and share photos of friends. Now, that you met the machine leaning let’s move to the methods.First is the Supervised Learning, The purpose of this method is for the algorithm to be able to “learn” by comparing its actual output with the “taught” outputs to find errors, and modify the model accordingly. Supervised learning therefore uses patterns to predict label values on additional unlabeled data. Second Unsupervised Learning; data are unlabeled, so the learning algorithm is left to find commonalities among its input data. As unlabeled data are more abundant than labeled data, machine learning methods that facilitate unsupervised learning are particularly valuable, so The goal of unsupervised learning may be as straightforward as discovering hidden patterns within a dataset which allows the computation machine to automatically discover the representations that are needed to classify raw data. Moving on to the Approaches, we basically have two.
The first one is The k-nearest neighbor algorithm is a pattern recognition model that can be used for classification as well as regression. Often abbreviated as k-NN, the kink-nearest neighbor is a positive integer, which is typically small. In either classification or regression, the input will consist of the k closest training examples within a space. the second one is Decision Tree Learning for general use, decision trees are employed to visually represent decisions and show or inform decision making. When working with machine learning and data mining, decision trees are used as a predictive model. These models map observations about data to conclusions about the data’s target value. The goal of decision tree learning is to create a model that will predict the value of a target based on input variables. last but not least is the Deep Learning. Deep learning attempts to imitate how the human brain can process light and sound stimuli into vision and hearing. A deep learning architecture is inspired by biological neural networks and consists of multiple layers in an artificial neural network made up of hardware and GPUs. Deep learning uses a cascade of nonlinear processing unit layers in order to extract or transform features (or representations) of the data. The output of one layer serves as the input of the successive layer. In deep learning, algorithms can be either supervised and serve to classify data, or unsupervised and perform pattern analysis. It’s a lot of information I know but we are almost done so about the programming languages, the most popular one is python, it may be due to the increased development of deep learning frameworks available for this language recently.
Then we have Java, which is widely used in enterprise programming, and is generally used by front-end desktop application developers who are also working on machine learning at the enterprise level. You may be surprised by it, but we are going back to the basics C++ is the language of choice for machine learning and artificial intelligence in game or robot applications. You may ask how come we use C++ in Machine Learning, well Some machine learning libraries you can use with C++ include the scalable ml-pack, Dlib offering wide-ranging machine learning algorithms, and the modular and open-source Shark. Let’s not forget about Machine Learning Algorithms (“The decision tree” Random Forests, ” “Gradient Boosting” / “XG Boost” “Genetic Algorithms”), these three basically looks lite trees, it’s like you got into the woods “Support Vector Machines” The “K closest neighbors” The “Logistic Regression”, the past algorithms have a lot of victors, if you decide to imagine how will your code look like and finally we have the “Clustering”. That was a lot to take but you asked for it, but hey, don’t forget the difference between AI, ML, and DL.Don’t forget to send me your feedbacks.
