MACHINE LEARNING : The AGE of AI
MACHINE LEARNING IS THE ABILITY OF MACHINES TO LEARN AND FORM PATTERNS FOR SOLVING REAL WORLD PROBLEMS THEMSELVES WITHOUT EXPLICIT CODE.
Machine learning booms from the advent in technology and automation leading to increased traffic on web pages. This calls for the management and supervision of large quantities of data sets and the objective is achieved through machine learning.
We come across different machine learning models in our daily lives. Some of the basic examples comprise of the recommendations of content and videos we obtain in search-engines such as Google and YouTube that are by and large based on our search history.
Gmail incorporates numerous machine learning algorithms to identify spam emails in order to save the user from tons of unnecessary data.
Machine Learning touches various segments of the industry and basic sciences. Most of the Natural Language Processing (NLP) involves machine learning. In recent times when the world is ravaged by the COVID-19 pandemic machine learning models are required to maintain electronic medical records to study and analyze the novel corona virus better.
BASIC WORKING OF A MACHINE MODEL
A computer program learns to perform a task(T) gathering experience (E) and has a performance measure(P). This performance measure (P) applied on test tasks(T) improve with experience(E)
Example: Let’s take the example of chess.
T (Task) — To win a match against the opponent.
E (Experience) — The number of matches played
P (Performance Measure) — Ability to identify winning moves
The machine learning model for chess is fed with some basic instructions or the rules of the game. It is then allowed to play against large a number of human opponents. In this way, the model can learn about the different techniques and winning moves used by the opponent. The more experience it gains, the more efficient its performance measure becomes and this increases its probability to win further matches. The advantage that the machine learning model has over humans is that it doesn’t get tired and can play as many matches as possible. In this way, it overcomes the limitation of a human brain.
DEMAND OF MACHINE LEARNING
Machine Learning is currently the hottest domain in the technical world. Big tech giants like Apple, Facebook, Google and Amazon are investing heavily in this domain.
There are two reasons for the extreme demand for Machine Learning Engineers. First, the world is encountering situations that are uncertain and unpredictable like that posed by the current lock down due to COVID 19, where there has been an enormous demand of machine learning used to maintain records of positive patients, recovered cases, etc in different demographics.
Second, there is a huge scarcity of engineers who know how to apply machine learning models efficiently. Even in the Silicon Valley, some data scientists and engineers who have been working on models for more than six months but are unable to obtain the desired results due to improper and inefficient application of machine learning models.
Today, in order to stand out, one not only has to learn the basics but also develop a deep specialization.
BROAD CLASSIFICATION OF MACHINE LEARNING
In supervised learning, machines are provided with data sets and there is somewhat a clear picture of what the desired results should look like. There is a direct connection between input and output.
Supervised learning problems are classified into two categories:
Classification Problems: Here, we get discrete values as output for the given input.
Regression Problems: Here, the output is generated in a continuous form for the given input. When plotted on the graph, the output appears as a continuous function.
Unsupervised learning helps us to approach real world problems where there is not a direct relationship between the input and output. In other words, we don’t have any idea of what the actual picture of the solution would look like.
Here, the structure of data is derived irrespective of the effect of variables involved.
Unsupervised learning involves clustering of datasets into similar groups as shown in the figure below.
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