What is ML (Machine Learning)?
Machine Learning is a subset of AI (Artificial Intelligence) which provides machines to learn and improve automatically through experience and use of data without being explicitly programmed. Machine Learning is used to make machines smarter so they can learn by themselves and predict more accurate output itself. It is a study of computer algorithms. With the help of these algorithms, we can train our machine to learn by itself through algorithms like SVM, Random forest, etc.
Types of Machine Learning
There are three types of Machine Learning: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
- Supervised Learning: In this learning we give our model labeled datasets to train algorithms to predict output accurately means in this learning first we train our model or machine for lots of input and outputs so that next time we want results so on the basis of the training our machine is able to generate results. The algorithms used in supervised learning include Support Vector Machines (SVM), Random Forests, etc.
- Some Real-life Example of Supervised Learning is: Exit poll result, Weather forecasting, Face recognition, etc
- Unsupervised Learning: In this learning, we give our model Unlabelled datasets and the algorithm is to learn patterns from Unlabelled data and make clusters. Clustering is the primary task of Unsupervised Machine Learning. The algorithms used in Unsupervised learning are K-Means clustering, KNN (K-nearest neighbors), Hierarchical clustering, Anomaly Clustering, and Apriori Algorithm.
- Real-life Example of Unsupervised Learning: The best example of Unsupervised learning comes in our phone gallery. When we click our picture with pets or other people, after some days we can see in our device there is a different folder, made by our device in which pets’ photo is in a different folder and our photos are in a different folder so this is done by the help of Unsupervised learning
- Reinforcement Learning: In this Learning, we have an agent and environment so the agent performs some action on the environment and it gets a reward either it is Positive or Negative it totally depends on the action of the agent so on the basis of the reward it learns.
- Real-life Example of Reinforcement Learning: The best example of Reinforcement learning is, when we play any game first time then we learn by our actions in that game for example when we play Gta first time then we don’t know who is the cop, how we can save ourself from cops but we learn by our action so in the same way the Reinforcement Learning is work.
Advantages of Machine Learning
- Automate Everything so human efforts are less.
- Efficiently handle data with the help of ML.
- Use in a wide range of applications.
- There is no need for human intervention.
- Identifying trends and patterns for improvement is easy with it.
- Continuous improvement happens which makes work easier.
Disadvantages of Machine Learning
- Chances of Error are High.
- Time and Space consuming.
- Data Acquisition.
Machine Learning Real-World Uses
- Medical Diagnosis.
- Health Care.
- Smart Assistant.
- Social Networking.
- Email Intelligence.
- Banking and Personal Finance.
- Google Maps.
- Riding Apps.
- Speech Recognition.
- Computer Vision.
- Customer Service.
- Recommendation Engines.
- Automated Stock Trading.
How ML is Make Human Work Easy
Machine Learning makes human work easy by automating the machines, so some years ago when we needed lots of workers to handle a machine or manage a machine, now we don’t need that many people because ML is automated and works with the help of Computer Algorithms. For Example, some years ago we did not assume that we can see self-driving cars but now it is possible due to ML algorithms as we trained our car by lots of data, so now the cars are capable to drive by themselves so now we don’t need drivers for the car. In the Medical sector, the Health care sector in many sectors things are going to automate with the help of ML so in that way, ML is reduce human work and makes life easier.