Machine Learning A-Z Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science is a good course and personally for me with the theoretical background in Machine learning, it was fun to implement the projects and play around. It was amazing to experience. I really liked the Machine Learning A-Z™: Hands-On Python & R In Data Science course. The way the trainers explained the concepts were too good. The only thing which I thought was missing was more of real-world datasets and application in the course. Overall it was a great experience. The course will really help the beginners to gain knowledge.

machine learning a-z

Overall a great course! As always, from the Super DataScience team! The intuition tutorials are very helpful conceptually. Both Python and R tutorials are easy to follow and generally well explained. The Machine Learning A-Z™ is quite good to understand ML in a simplistic sense. I will definitely recommend this course to persons who want to develop their skills in ML.

Curriculum For This Course

1. Welcome to the course!
2. -------------------- Part 1: Data Preprocessing --------------------
3. ------------------ Part 2: Regression --------------------
4. 1 Lecture
5. Simple Linear Regression
6. Multiple Linear Regression
7. Polynomial Regression
8. Support Vector Regression (SVR)
9. Decision Tree Regression
10. Random Forest Regression
11. Evaluating Regression Models Performance
12. -------------------- Part 3: Classification --------------------
13. Logistic Regression
14. K-Nearest Neighbors (K-NN)
15. Support Vector Machine (SVM)
16. Kernel SVM
17. Naive Bayes
18. Decision Tree Classification
19. Random Forest Classification
20. Evaluating Classification Models Performance
21. -------------------- Part 4: Clustering --------------------
22. K-Means Clustering
23. Hierarchical Clustering
24. -------------------- Part 5: Association Rule Learning --------------------
25. Apriori
26. Eclat
27. -------------------- Part 6: Reinforcement Learning --------------------
28. Upper Confidence Bound (UCB)
29. Thompson Sampling
30. -------------------- Part 7: Natural Language Processing --------------------
31. -------------------- Part 8: Deep Learning --------------------
32. Artificial Neural Networks
33. Convolutional Neural Networks
34. -------------------- Part 9: Dimensionality Reduction --------------------
35. Principal Component Analysis (PCA)
36. Linear Discriminant Analysis (LDA)
37. Kernel PCA
38. -------------------- Part 10: Model Selection & Boosting --------------------
39. Model Selection
40. XGBoost
41. Bonus Lectures

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