What you Will be taught ?
- Perceive and implement a Resolution Tree in Python
- Perceive about Gini and Info Acquire algorithm
- Remedy mathematical numerical associated determination bushes
- Study regression bushes
- Study easy, a number of, polynomial and multivariate regression
- Study Strange Least Squares Algorithms
- Remedy numerical associated to Strange Least Squares algorithm
- Study to create actual world predictions and classification tasks
- Study Gradient Descent
- Study Logistic Regression and hyper parameters
Machine studying is a department of synthetic intelligence (AI) centered on constructing purposes that be taught from information and enhance their accuracy over time with out being programmed to take action.
In information science, an algorithm is a sequence of statistical processing steps. In machine studying, algorithms are ‘skilled’ to search out patterns and options in large quantities of knowledge with a view to make selections and predictions primarily based on new information. The higher the algorithm, the extra correct the choices and predictions will develop into because it processes extra information.
Machine studying has led to some wonderful outcomes, like having the ability to analyze medical photographs and predict ailments on-par with human specialists.
Google’s AlphaGo program was capable of beat a world champion within the technique sport go utilizing deep reinforcement studying.
Machine studying is even getting used to program self driving vehicles, which goes to vary the automotive business ceaselessly. Think about a world with drastically diminished automotive accidents, just by eradicating the component of human error.
Subjects coated on this course:
1. Lecture on Info Acquire and GINI impurity [decision trees]
2. Numerical drawback associated to Resolution Tree might be solved in tutorial periods
3. Implementing Resolution Tree Classifier in workshop session [coding]
4. Regression Timber
5. Implement Resolution Tree Regressor
6. Easy Linear Regression
7. Tutorial on value perform and numerical implementing Strange Least Squares Algorithm
8. A number of Linear Regression
9. Polynomial Linear Regression
10. Implement Easy, A number of, Polynomial Linear Regression [[coding session]]
11. Write code of Multivariate Linear Regression from Scratch
12. Study gradient Descent algorithm
13. Lecture on Logistic Regression [[decision boundary, cost function, gradient descent…..]]
14. Implement Logistic Regression [[coding session]]
- Fundamental mathematical ideas of addition, multiplication and so on
- Realizing python beforehand could be handful