Basics of Machine Learning

 Let's begin our journey with  MACHINE LEARNING...

WHAT IS MACHINE LEARNING?

Machine learning is a branch of artificial intelligence (AI) and computer science, it gives devices the ability to learn from their experiences and improve themself without doing any coding. 

From Where did it evolve?

image reference: https://miro.medium.com/max/686/1*CYWu0xizXIzY1FF6PZOclg.png

WHAT IS ARTIFICIAL INTELLIGENCE AND HOW IT IS DIFFERENT FROM MACHINE LEARNING?

The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

                                                                      or

Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

THE MAJOR DIFFERENCES ARE:

1)Artificial intelligence is a technology that enables a machine to simulate human behavior. 

Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly. 

2)The goal of AI is to make a smart computer system like humans to solve complex problems whereas 

 

The goal of ML is to allow machines to learn from data so that they can give accurate output.

What is DEEP LEARNING and how it is different from ml?

Deep Learning is just a type of Machine Learning, inspired by the structure of the human brain Deep learning algorithms attempt to draw similar conclusions as humans would by continually analyzing data with a given logical structure. To achieve this, deep learning uses a multi-layered structure of algorithms called neural networks.

image reference:https://miro.medium.com/max/1838/1*KHs1Chs6TCJDTIIQVyIJxg.png

THE MAJOR DIFFERENCES ARE:

1)Deep learning is a type of machine learning, which is a subset of artificial intelligence. 

2)Machine learning is about computers being able to think and act with less human intervention whereas Deep learning is about computers learning to think using structures modeled on the human brain.

3)Machine learning requires less computing power; deep learning typically needs less ongoing human intervention. Deep learning can analyze images, videos, and unstructured data in ways machine learning cannot easily do.

image reference: https://lawtomated.com/wp-content/uploads/2019/04/MLvsDL.png

What is DATA SCIENCE and how it is different from ml?

Machine learning requires less computing power; deep learning typically needs less ongoing human intervention. Deep learning can analyze images, videos, and unstructured data in ways machine learning cannot easily do.

Those who practice data science are called data scientists, and they combine a range of skills to analyze data collected from the web, smartphones, customers, sensors, and other sources to derive actionable insights.

Data science encompasses preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis. 

Analytic applications and data scientists can then review the results to uncover patterns and enable business leaders to draw informed insights.

THE MAJOR DIFFERENCES ARE

At its core, data science is a field of study that aims to use a scientific approach to extract meaning and insights from data.      

Machine learning, on the other hand, refers to a group of techniques used by data scientists that allow computers to learn from data. These techniques produce results that perform well without programming explicit rules.

Data Science is a field of deep study of data that includes extracting useful insights from the data and processing that information using different tools, statistical models, and Machine learning algorithms.    

 Machine Learning allows computers to learn from past experiences on their own, it uses statistical methods to improve performance and predict the output without being explicitly programmed.

APPLICATIONS OF MACHINE LEARNING:

SPAM MAIL DETECTION :

image reference: https://miro.medium.com/max/875/0*ZzcKU607S3OWpn5I.jpg

YouTube recommendation System:

image reference: https://miro.medium.com/max/1838/1*fcsodWL98sYqyUhQtJlNPA.png

Fraudulent Transactions Detection :

image reference: https://vitalflux.com/credit-card-fraud-detection-machine-learning/

Flight fare prediction:

image reference: https://www.usnews.com/dims4/USNEWS/ed7b154/2147483647/crop/2000x1334%2B0%2B1/resize/970x647/quality/85/?url=http%3A%2F%2Fmedia.beam.usnews.com%2Fb7%2Ff4%2F847d373a4218bcbb9383ff501eaa%2F191004-flightapp-stock.jpg

Identifying potential customers:

image reference: https://data-flair.training/blogs/r-data-science-project-customer-segmentation/

Breast Cancer Detection:

image reference: https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41598-020-80570-0/MediaObjects/41598_2020_80570_Fig1_HTML.png

TYPES OF MACHINE LEARNING:

Supervised Machine 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)

 The goal is to approximate the mapping function so well that when you have new input data (x) you can predict the output variables (Y) for that data.

image reference: https://static.javatpoint.com/tutorial/machine-learning/images/supervised-machine-learning.png

Examples of Supervised Machine learning:

REGRESSION:

                         Linear Regression :

                                Simple Linear Regression

                                Multi Linear Regression

                                Polynomial Regression

CLASSIFICATION:

                             Logistic Regression

                             Support Vector Machine (SVM)

                             K-nearest Neighbour

                             Random forest

                             Decision Tree

Unsupervised Machine Learning:

                                       

Unlike supervised machine learning, unsupervised machine learning methods cannot be directly applied to regression or a classification problem because you have no idea what the values for the output data might be, making it impossible for you to train the algorithm the way you normally would. Unsupervised learning can instead be used to discover the underlying structure of the data.

image reference: https://techvidvan.com/tutorials/wp-content/uploads/sites/2/2020/07/Unsupervised-Learning-in-ML-1.jpg

Examples of Unsupervised Machine learning:

                                                               Clustering Technique:

                                                                                  K-Means 

                                                                                  Hierarchical 

NOTE:

There are many other algorithms that are not mentioned.