Machine Learning Made Easy
Hey folks,
You heard of the term ‘machine learning’ before and of course, someone told you that machine learning and AI will take overall jobs. but wait, actually what it is. let’s walk through ML most simply than ever before and what it is not?
Overview:
What is Machine Learning?
Types
Supervised ML
Unsupervised ML
Introduction to Regression
Introduction to Classification
Brief about Clustering
Brief about Association
What is ML?
According to Tom Mitchell definition of machine learning
‘A computer program is said to learn from experience E concerning some class of tasks T and performance measure P, if its performance at the task in T, as measured by P, improves with experience E.’
Example:
playing checkers.
E=The experience of playing many games of checker
T=The task of playing checker
P=The probability that the program will win the next game.
Types:
The machine-learning problem can be classified as supervised learning and unsupervised learning
Supervised learning:
In supervised learning, you train the machine by using labeled data. In short, some data is already knowing what their correct output should look like and there is a relationship between input data and output data.
Supervised learning problems are categorized into two categories regression and classification.
Regression:
The regression technique predicts a single output value using training data.
Example: You can use regression to predict the house price from training data. The input variables will be locality, size of a house, etc.
Classification:
Classification means to group the output inside a class. If the algorithm tries to label input into two distinct classes, it is called binary classification. Selecting between more than two classes is referred to as multiclass classification.
Example: Determining whether or not someone will be a defaulter of the loan.
Unsupervised learning:
This deals with unlabeled data.Unsupervised learning algorithms that allow us to perform more complex processing tasks and it can be more unpredictable compared with others.
Unsupervised learning problems categorised into clustering and association problems.
Unsupervised learning allows us to approach problems with little or no idea what our results should look like. we can derive structure from data where we don’t necessarily know the effect of the variables.
Association:
association rules allow you to establish association amongst data objects inside large databases. this unsupervised technique is about discovering the exciting relationship between variables in large databases
Example:
People who buy a phone most likely to buy the phone case.
Here is the list of most commonly used machine learning algorithms:
Linear Regression
Logistic Regression
Decision Tree
SVM
Naive Bayes
kNN
K-Means
Random Forest
Dimensionality Reduction Algorithms
Gradient Boosting algorithms
GBM
XGBoost
LightGBM
CatBoost
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