The way we use the internet and our mobile phones are continuously improving because of machine learning. Machine learning and Artificial intelligence (MLAI) are sometimes used interchangeably however, there is a big difference between these two. It can be said to be a part of artificial intelligence.
The primary aim of machine learning is to make machines take decisions by themselves. To do that machines need learning capabilities, reasoning capabilities, and abstract thinking. ML is completely focused on writing computer programs that can learn from previous experiences. ML is more associated with statistics and data analytics than it is to the artificial intelligence.
We don’t realize but machine learning plays a very important role in the way we use the internet. “Google search” is probably the best example of ML. Every time you type something in the Google search bar and get the exact results is because of ML algorithm running in the background. Machine learning is like the “unsung hero” behind all the latest development in technology.
Giant online retailers like Amazon, Alibaba and many more use machine learning to recommend “items” to the buyers based on their previous buying history. Netflix uses the machine learning algorithm to suggest movies and shows to subscribers based on what they watched earlier. These are some common examples where ML has kind of become the backbone of business and corporations are so dependent on it that they can’t imagine a single day without ML.
Know the 3 types of Machine Learning:-
1. Supervised Machine Learning:
Supervised learning is the type of learning where to train the machine with the help of well-organized data. It means that the data is already labeled with the correct outcome. For example, you can train the machine to recognize the alphabets, colors and anything at all. Machines can learn more about the subject if the data related to it adequate. Once the machine is trained it is given the new set of data and the algorithm then uses the past experience to give the results.
2. Unsupervised Machine Learning:
Unsupervised is where the machine is trained with the help of non-labeled or unorganized data. The algorithm is never told what the data represent. For example, the machines are shown the alphabets but not told which sign represent which alphabet. In Unsupervised learning, machines try to recognize patterns on its own when a lot of raw data is thrown at it. The learning algorithms are such that they enable the machine to learn on its own.
3. Reinforcement Machine Learning:
Reinforcement learning is more like unsupervised learning in terms of the data used to train machine is non-labeled but the outcome of the question asked by machine is graded and with the continuous process of validating the outcome the machine can define a correct outcome.
There are a lot of different algorithms that can be used in ML-like, nearest neighbor, decision tree, and random forest to name a few. We will talk about these algorithms in details in future articles.
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