Machine Learning Tutorial Outline

Machine Learning is not a difficult course but it is a bit different. And different things require different strategies to be conquered. 

That is why we will devise some strategy to learn machine learning as a whole without getting bored...

so without further ado, lets have a brief overview of what we are going to cover in this Course;

Lecture Topics

1.               Introduction to Machine Learning

2.               Basic categories of Machine Learning

3.               Supervised Learning :

4.               Linear Regression part1 (Uni-variant)

5.               Linear Regression part 2 (Multi-variant)

6.               Polynomial Regression

7.               Logistic Regression

8.               Data fitting problems

9.               Neural Network

10.         Neural Network : Forward and back propagation

11.         Model Selection : Data splits

12.         Decision Tree

13.         Decision Tree : Entropy concepts

14.         Decision Tree : ID3 Algorithm & Ensemble methods

15.         Boosting Algorithm : Ada boosting

16.         Support Vector Machine (SVM)

17.         SVM numerical problem and solution

18.         SVM examples

19.         Bayesian network

20.         Evaluation Metrics; Accuracy, precision, recall, F-score

21.         Unsupervised Learning :

22.         Clustering

23.         Clustering Techniques : K-means

24.         Principal Component Analysis (PCA)

25.         Visual Recognition : Filtering

26.         Recommender Systems

27.         Conclusion

You might be thinking... Whaaaat is this??? 

But don't worry we will cover and understand the above topics step by step in the coming blog posts. Moreover I hope you have looked at Maths for machine learning post, if you haven't then you may look at the maths course, it is very helpful as a refresher...

That's all for today, I hope we will accomplish our goals in an efficient way...

Happy Machine Learning :)