"Machine learning is a sort of artificial intelligence that enables computers to learn from data and enhance their performance over time without being explicitly programmed."
What is Machine Learning?
Machine learning is the process of making a machine learn things from data and make its own decisions for future data. So that it can predict what's going to happen in the future.
There are four types of machine learning -:
Supervised Learning
Unsupervised Learning
Semi-Supervised Learning
Reinforcement Learning
Types of Machine Learning -:
Supervised Machine Learning -:
It is a machine-learning model where there will be labels for the data. That means there is a target feature in the dataset. On this dataset, the prediction will be done.
Target Feature -: Feature means a column in the data frame. The target feature is the column that the machine learning model trained to predict.
Prediction -: Prediction is the purpose of the machine learning models.
It will learn the pattern based on data and make the predictions.
Unsupervised Machine Learning -:
It is a machine-learning model where there will be no labels on the data. That means there is no target variable in the data frame. So using similarities it will learn the pattern of the dataframe.
Unsupervised learning is not about prediction it's about grouping similar data. So that it gets categorized easily like making clusters using similar data.
Cluster -: Grouping the unlabelled data is known as clustering and the group is called a cluster.
Example -:
One of the best real-time examples of this model is the recommendation system which will recommend movies, products etc using unlabelled data.
Semi-Supervised Machine Learning -:
It is a hybrid machine learning model where the dataset contains both labelled and unlabelled data which means it is a mix of both supervised and unsupervised learning. First, it will use the data and make the unsupervised model from it so that it will get the grouping of data and from these insights, it will make a supervised model and get the predictions from it.
Hybrid Model -: Hybrid model means a mix of supervised and unsupervised learning models.
Due to this, the model will enhance its performance. Most of the real-time data has less labelled data. So the model will do better performance.
Example -:
One of the best real-time examples for this model is medical diagnosis,
we will have the data where known diseases are labelled data which are less in quantity(less known data) and unknown diseases which are unlabelled data that are more in quantity(more known data). This model can help doctors to diagnose the disease accurately.
Reinforcement Learning -:
A type of machine learning called reinforcement learning enables an agent to discover how to act in a given environment in order to maximise its expected reward.
It is a type of machine learning model, the agent will learn from the environment by making trial-and-error decisions. There is no labelled data and unlabelled data in the dataset to make predictions or to find the patterns.
Agent -: It is an entity which makes decisions and learns to survive in the environment.
Environment -: It is a context where the agent interacts and learns something.
It is a trial-error model. It uses the trial-error learning process.
Example -:
One of the best real-time examples of this model is self-driving cars like Tesla cars. These cars will learn how to drive safely on the roads using reinforcement learning.
Relationship between Machine Learning, Artificial Intelligence, Deep Learning and Data Science:
Artificial Intelligence -:
It is a process where a machine can do a lot of things without the help of humans. It will learn things on its own without the interaction of the humans. An example of artificial intelligence is the Netflix movie recommendation system which will recommend movies on its own based on the data without the help of humans. It contains both machine learning and deep learning in it. You can take virtual assistance as an example of AI.
Machine Learning -:
It is a subset of Artificial Intelligence, It contains a set of tools which are used for analyzing, visualizing, predicting and forecasting the data.The examples of ML are fraud detection, spam clustering etc.
Deep Learning -:
It is a subset of Machine Learning, It is like an artificial human brain where it can make its own decision to do something. For doing this deep learning we use a multi-layered neural network. The real-time examples of DL are face regonization and generating photos etc.
Data Science -:
It is a mix of everything. It contains some parts of AI some parts of ML and Some parts of DL. Where we will predict the data and visualize and analyze it.Data science is used to develop new business prospects, estimate customer turnover, and improve manufacturing processes.
In conclusion, data science is a discipline that employs machine learning and deep learning methods to draw conclusions from data. Machine learning and deep learning are subsets of artificial intelligence.
"Machine learning is an effective technique that may be utilised to address many different types of issues. There is an abundance of resources online and in libraries if you're interested in learning more about machine learning. If you want to learn from my blogs then wait and watch much more to come.
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