Machine Learning and Algorithms

Samarth Kumar
4 min readNov 7, 2020

According to Expertsystem.com, Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

Source: Nand Kishor

Machine Learning can be divided broadly divided into three types:

  1. Supervised Learning: It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process.
  2. Unsupervised Learning: It is called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data.
  3. Reinforcement Learning: Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment.

In the graphic above we can see the three broad categories and various algorithms which use the particular type of learning. Through the graphic, we can also infer that Machine Learning has numerous applications and is certainly a way ahead towards the future.

Source: Kamil Krzyk

Glassdoor, a leading job review company, suggested that Data science, Deep Learning and AI will be the most in-demand professions in 2020. It is in demand because of its usability and prospects for the future.

Supervised Learning

The majority of practical machine learning uses supervised learning.

Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.

Y = f(X)

Source: Anukrati Mehta

Supervised learning problems can be further grouped into regression and classification problems.

  • Classification: A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”.
  • Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”.

Some common types of problems built on top of classification and regression include recommendation and time series prediction respectively.

Some popular examples of supervised machine learning algorithms are:

  • Linear regression for regression problems.
  • Random forest for classification and regression problems.
  • Support vector machines for classification problems.

Unsupervised Learning

Unsupervised learning is where you only have input data (X) and no corresponding output variables.

The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.

Source: CHI Software

Unsupervised learning problems can be further grouped into clustering and association problems.

  • Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.
  • Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.

Some popular examples of unsupervised learning algorithms are:

  • k-means for clustering problems.
  • Apriori algorithm for association rule learning problems.

Reinforcement Learning

In reinforcement learning, an artificial intelligence faces a game-like situation. The computer employs trial and error to come up with a solution to the problem. To get the machine to do what the programmer wants, the artificial intelligence gets either rewards or penalties for the actions it performs. Its goal is to maximize the total reward.
Although the designer sets the reward policy–that is, the rules of the game, he gives the model no hints or suggestions for how to solve the game. It’s up to the model to figure out how to perform the task to maximize the reward, starting from totally random trials and finishing with sophisticated tactics and superhuman skills.

Source: Apoorva Gokhale

Some popular examples of reinforced learning algorithms are:

  • Discord Bots use this algorithm to learn and maintain a user’s behavior.
  • The AI in games use this to develop in-game responses to the user.

“I am telling you, the world’s first trillionaires are going to come from somebody who masters AI and all its derivatives,and applies it in ways we never thought of.” ~Mark Cuban

Sources:

  • MachineLearningMastery.com
  • DeepSense.ai

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