Artificial intelligence and Machine Learning.pptx

introduction to ai and machine learining

1. AI & Machine Learning

2. AI Artificial intelligence, or AI, is technology that enables computers and machines to simulate human intelligence and problem-solving capabilities.

3. AI Documentry

4. Machine Learning A sub field of Computer science and AI Learn make decision prediction from data Machine learning is a field of computer science that uses statistical technique to give computer systems the ability to learn with data, without being explicitly programmed.

5. Life cycle Get Data Clean, prepare & Manipulate Data Train Model Test Data Improve

6. Type of Data Structure Unstructured Semi structure

7. Structure Data structured data is the type of data that is well-organized and accurately formatted. This data exists in a format of relational databases ( RDBMSs ), meaning the information is stored in tables with rows and columns that are connected.

8. Unstructured Data Social Media Text Block Chat Tag Audio Video

9. Multi Structure E-commerce Amazon shopping Daraz Shopping Point of Sale Currency Data

10. Types of ML

11. Supervised ML

12. Types of Supervised ML Algorithms Regression Classification

13. Regression Regression  is a statistical approach used to analyze the relationship between a dependent variable (target variable) and one or more independent variables (predictor variables ).

14. Continue Simple Linear Regression Multiple Linear Regression Polynomial Regression

15. Classification Classification is a supervised machine learning method where the model tries to predict the correct label of a given input data. In classification, the model is fully trained using the training data, and then it is evaluated on test data before being used to perform prediction on new unseen data

16. Continue Binary Multi

17. Continue Random Forest Decision Tree Logistic Regression SVM

18. Unsupervised

19. Types of Unsupervised

20. Continue K-means clustering. KNN (k-nearest neighbors) Hierarchal clustering. Anomaly detection. Neural Networks. Principle Component Analysis.