Machine Learning Feature Engineering Polynomial Regression

Course Lessons

1. 1- #1 What is Machine Learning
2. 2- Machine Learning Supervised Learning
3. 3- Machine Learning Unsupervised Learning
4. 4- Simple Linear Regression Model
5. 5- Machine Learning Linear Regression Cost Function
6. 6- Machine Learning Linear Regression Cost Function for One Parameter
7. 7- Machine Learning Linear Regression Cost Function for Two Parameters
8. 8- Machine Learning Linear Regression Gradient Descent Overview
9. 9- Machine Learning Linear Regression Gradient Descent Mathematically
10. 10- Machine Learning Gradient Descent for Linear Regression
11. 11- Machine Learning Multiple Linear Regression Model
12. 12- Machine Learning Multiple Linear Regression Model Feature Scaling
13. 13- Machine Learning Checking Gradient Descent for Conversions Choosing the learning rate
14. 14- Machine Learning Feature Engineering Polynomial Regression
15. 15- Machine Learning Linear Regression vs. Classification
16. 16- Machine Learning Logistic Regression
17. 17- Machine Learning Decision Boundary
18. 18- Machine Learning Cost Function for Logistic Regression
19. 19- Machine Learning Overfitting and Underfitting
20. 20- Machine Learning How to prevent Overfiting
21. 21- Machine Learning Cost Function with Regularization
22. 22- Machine Learning Gradient Descent with Regularization
23. 23- Machine Learning Introduction to Neural Networks
24. 24- Machine Learning Neural Networks in details
25. 25- Machine Learning Neural Networks Activation Functions
26. 26- Multi-class Classification softmax Regression Multi-Lable Classification
27. 27- Machine Learning Advanced Optimization Adam algorithm
28. 28- Machine Learning Machine Learning Diagnostic Evaluating The Model
29. 29- Machine Learning Model Selection
30. 30- Machine Learning Bias and Variance
31. 31- Machine Learning Regularization with Bias and Variance
32. 32- Machine Learning A Baseline Level of Performance Learning Curves
33. 33- Machine Learning Improving The Learning Algorithm
34. 34- Machine Learning Introduction to Decision Trees
35. 35- Machine Learning Decision Tree Learning Process
36. 36- Machine Learning Decision Tree Measuring Purity Entropy Information Gain
37. 37- Machine Learning Building a Decision Tree Model
38. 38- #38 Machine Learning Decision Tree Using one hot encoding of categorical features
39. 39- #39 Machine Learning Decision Tree Continuous Valued Features
40. 40- #40 Machine Learning Decision Tree Regression Trees
41. 41- #41 Machine Learning Decision Tree Sampling with replacement Random Forest Algo. XGBoost
42. 42- #42 Machine Learning Decision Trees vs Neural Networks
43. 43- #43 Unsupervised Learning Clustering K-means Intuition
44. 44- #44 Unsupervised Learning Anomaly Detection Finding Unusual Events
45. 45- #45 Unsupervised Learning Principle Component Analysis PCA
46. 46- #46 Machine Learning Introduction to The Reinforcement Learning
47. 47- الأساسيات النظرية لتعلم الآلة كاملة في فيديو واحد Machine Learning Complete Course