Machine Learning / Introduction to Machine Learning
Machine Learning
It is a branch of artificial intelligence focuses on the development of computer programs that can access data and make use it by machine to learn for themselves.
Best decisions in the future based on the past data we provide. Machine Learning objective is to make the computers learn automatically without human assistance.
Machine Learning uses two things. They were
1. Observations .
2. Data (past data).
Machine learning methods
1. Supervised Learning
For a model we use Independent Variables to predicted Target/Outcome/Dependent variable using a function that maps inputs to desired outputs. Training process for a model continues until a desired accuracy is achieved on a training data. Supervised Learning Examples: Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.
Set of predictors (Independent variables)---->Predicts ---> target/outcome/Dependent variable.
Inputs -------------------------------------->set of variables (generate a function) Maps------------> Outputs.
Train the Model-------------------------->To achieve desired level of accuracy.
2. Unsupervised Learning
It is used for clustering population in different groups. Examples are Apriori algorithm, K-means.
3. Reinforcement Learning
Here Machine is trained continuously using trial and error method in a real time environment and it past experience used to identify correct knowledge to take business decisions.
Machine Learning Algorithms List
1. Linear Regression.
2. Logistic Regression.
3. Decision Tree.
4. SVM.
5. Naive Bayes.
6. kNN.
7. K-Means.
8. Random Forest.
9. Dimensionality Reduction Algorithms.
10. Gradient Boosting algorithms.
a. GBM b. XGBoost c. LightGBM d. CatBoost
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