Machine Learning
ML (Machine Learning) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed. Machine Learning algorithms use historical data to indicate the new output values.
Importance of Machine Learning:
Machine Learning is often categorized by how an algorithm learns to become unsupervised learning, semi-supervised learning, and reinforcement learning. The type of algorithm data scientists chooses to use depends on the type of data they want to predict.
How does Machine Learning work?
Machine Learning algorithms are molded on a training dataset to create a model, As new input data is introduced to the trained ML algorithm, it uses the developed model to make a prediction.
Note: The above illustration discloses a high-level use case scenario.
However, typical machine learning examples may involve many other factors or types of elements, variables, and steps.
Machine learning uses two types of techniques: supervision learning, which trains a model on known inputs and output data to predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data.
4 Types of Machine Learning:
- Supervised Learning.
- Reinforced learning.
- Unsupervised Learning.
- Semi-Supervised Learning.
#1: Supervised Learning:
In this type of machine learning, machines are trained using labeled datasets. Devices use this information to predict output in the future. This whole process is based on supervision and hence, the name. As some inputs are mapped to the output, the labeled data help set a strategic path for machines. Moreover, test datasets are continuously provided after the training to check if the analysis is accurate. The Core objective of super learning techniques is to map the input variables with output variables. It is extensively used in fraud detection, risk assessment, and spam filtering. Here is an example to understand supervised learning: Suppose we have an input dataset of cupcakes. So, first, we will train the machine to understand the images, such as the Sharpe and portion size of the food item, the shape of the dish when served, ingredients, color, accompaniments, etc. After completion of training, we input the picture of a cupcake and ask the machine to identify the object and predict the output. Now, the machine is well-trained, so it will check all the features of the object, such as height, shape, color, toppings, and appearance, and find that it's a cupcake. So, it will put it in the dessert category. This is how the machine identifies various objects in supervised learning.
#2: Reinforced Learning:
In reinforcement learning, there is no concept of labeled data. Machines learn only from experiences. Using a trial and error method, learning works on a feedback-based process, The AI explores the data, notes features, learns from prior experience, and improves its overall performance. The AI agent gets rewarded when the output is accurate and punished when the results are not favorable.
It is categorized into two methods:
1. Positive Reinforcement Learning.
2. Negative Reinforcement Learning.
#3: Unsupervised Learning:
Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets, These algorithms discover hidden patterns or data groupings without the need for human intervention. Its ability to find similarities and differences in information makes it the ideal solution for exploratory data analysis, cross-selling strategies, customer segmentation, and image recognition. The examples are dimension reduction and clustering, Suppose the unsupervised learning algorithm is given an input dataset containing images of different types of cast and dogs.
#4: Semi-Supervised Learning:
Semi-supervised learning is a branch of machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training. Semi-supervised learning falls between unsupervised learning (with no labeled training data) and supervised learning (with only labeled training data). A common example of an application of semi-supervised learning is a text document classifier. This is the type of situation where semi-supervised learning is ideal because it would be nearly impossible to find a large amount of labeled text documents.
Real-World Application of Machine Learning
Machine learning is booming! By 2027, the global market value is expected to be $117.19 billion. With its immense potential to transform businesses across the globe, machine learning is being adopted at a swift pace. Moreover, thousands of new jobs are cropping up and the skills are in high demand.
Here are a few Real-World Applications of Machine Learning:
- Google Maps Traffic Prediction.
- Google Translate.
- Facebook's Automation Alt Text.
- Amazon's Recommendation Engine.
- Tesla's Self-driving Cars.
- Spam Detection in Gmail.
- Amazon Alexa.
- Netflix Movie Recommendation.
Advantages and Disadvantages of Machine Learning:
Advantages of Machine Learning:
Listed are a few points for the advantages of Machine Learning. Let us briefly look at the advantages of Machine Learning.
- It is automatic.
- It is used in various fields.
- It can handle various fields.
- Scope of advancement.
- Can identify trends and patterns.
Disadvantages of Machine Learning:
Listed are a few points for disadvantages of Machine Learning.
- More space is required.
- Data requirement is more.
- Chances of error or fault are more.
- Time-consuming and more resources are required.
- Inaccuracy of interpretation of data.
# Here are some Questions to check how much you understand about Machine Learning, ("Please give your Answers in the comment section with the question number and your name").
Q1. What is the use of Machine Learning?
Q2. What is the use of Artificial intelligence in Machine Learning?
Q3. What is Machine Learning?
Q4. Give any 5 examples of Machine Learning? ("Not those which are given in this blog")
Fruitful knowledge
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