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Machine Learning Basics

Machine Learning Basics

Machine Learning (ML)

  • A subset of artificial intelligence that focusses on creating computer systems that can learn and improve from experience.
  • powered by algorithms that incorporate intelligence into machines.
  • ML provides statistical tools to analyze, visualize, and make predictions from data.

ML Applications

  • Online shopping
  • Netflix movie suggestions
  • Spam mail warning
  • Self-driving cars

 

ML Data types:

Numerical data

(Measurable data)

 categorical data

(characteristics: Nominal, ordinal)

 Time series data

(Number sequence)

 Text data

(Words or paragraphs)

 

Machine Learning Types

  • Supervised Machine Learning

It is machine learning model which learns from labelled data.

Steps in Supervised machine learning

  • Unsupervised Machine Learning

A type of machine learning where there are no labeled outputs.

Algorithms learn the patterns in the data group similar data items

  • Reinforcement Learning:

It is a branch of Machine Learning where an agent learns to perform actions in an environment to maximize a cumulative reward. In    the context of autonomous car driving, the car is the agent, the road and its surroundings form the environment, and the reward might be related to safe and efficient navigation, obeying traffic rules, and reaching the desired destination.

Machine Learning types and examples:

S/No ML Type Meaning & Example
1 Unsupervised Machine Learning Extracting trends from data

Understand relationships within datasets
2 Supervised Machine Learning ·       Labelled data to train algorithms to predict outcomes or classify data.

·       Extracting rules from Data.
3 Reinforcement Learning Solving Tasks by trial and error.

·       Autonomous car driving

·       Learn to play a game like chess.

Decision->Feedback->Learning

 

 

Questions

Which type of Machine Learning algorithms extract trends from data?

  • Unsupervised Machine Learning 
  • Reinforcement Learning
  • Supervised Machine Learning
  • Natural Language Processing

Explanation

Correct Option: Unsupervised Machine Learning

The Unsupervised Machine Learning algorithms extract trends from data. In contrast, Supervised Machine Learning (option c) involves using labelled data to train algorithms to predict outcomes or classify data, and Reinforcement Learning (option b) focuses on training agents to make sequences of decisions through trial and error to maximize rewards. Natural Language Processing (option d) is a field within machine learning that deals with processing and understanding human language. Although NLP can be used to extract trends and insights from text data, it’s not a type of Machine Learning algorithm in the same sense as the other options.

 

Which type of Machine Learning is used in autonomous car driving?

  • Supervised Learning
  • Reinforcement Learning 
  • Unsupervised Learning
  • Natural Language Processing

Correct Option: Reinforcement Learning

Reinforcement Learning (RL) is a branch of Machine Learning where an agent learns to perform actions in an environment to maximize a cumulative reward. In the context of autonomous car driving, the car is the agent, the road and its surroundings form the environment, and the reward might be related to safe and efficient navigation, obeying traffic rules, and reaching the desired destination.

Which application does NOT require a Machine Learning solution?

  • Password Validation(*)
  • Detecting spam emails
  • Customer Segmentation
  • Stock Price Predictions

Correct Option: Password Validation

Password Validation (option a) typically does not require a Machine Learning solution. Password validation is a straightforward process that involves checking whether a user-entered password matches the stored password on a server. This can be achieved through standard cryptographic techniques and rules without the need for Machine Learning.

 

Which type of Machine Learning algorithm learns from outcomes to make decisions?

  • Reinforcement Learning 
  • Unsupervised Learning
  • Natural Language Processing
  • Supervised Learning

Correct Option: Reinforcement learning

Reinforcement Learning (option c) is a type of Machine Learning algorithm that learns from outcomes to make decisions. In Reinforcement Learning, an agent interacts with an environment and takes actions to maximize cumulative rewards.

 

What type of clustering algorithm is used to cluster the data points into nonoverlapping clusters?

  • Weight based
  • Partition based 
  • Distribution based
  • Density based

Correct Option: Partition based

Partition-based clustering algorithms (option c) are used to divide data points into nonoverlapping clusters, where each data point belongs to exactly one cluster. The most well-known example of a partition-based clustering algorithm is K-Means. K-Means iteratively assigns data points to the nearest cluster center and then recomputes cluster centers until convergence, resulting in distinct, nonoverlapping clusters.

 

What type of Machine Learning algorithm is used when we want to predict the resale price on a residential property?

  • Binary Classification
  • Multiclass Classification
  • Anomaly Detection
  • Regression 

Correct Option: Regression

Regression (option a) is the type of Machine Learning algorithm used when we want to predict continuous numerical values such as the resale price of a residential property. In regression tasks, the goal is to learn a mapping between input features (for example, square footage, number of bedrooms, and location) and a continuous target variable (for example, price). Linear Regression is most employed for this purpose.

 

Which type of function is used in Logistic Regression to predict a loan defaulter?

  • Identity function
  • Step function
  • Sigmoidal function
  • Gaussian function

Correct Option: Sigmoidal function to predict the probability of binary outcome

Logistic Regression is a binary classification algorithm commonly used in Machine Learning to predict binary outcomes, such as whether a loan will be defaulted or not. The key idea behind Logistic Regression is to model the probability of an event occurring as a function of input features. The output of this model is transformed using the sigmoidal (also known as logistic) function. The sigmoidal function, often represented as the sigmoid function, has an S-shaped curve that maps any input value to an output value between 0 and 1.

Source: http://oracle.com

See also

Artificial Intelligence Basics