Welcome to Beginning with ML, a blog on machine learning for beginners. Most of these posts are small, so you can learn in bite-sized pieces. Below are the posts published so far. Some of these posts are also accompanied by scanned notes provided by Dr. Snehanshu Saha, Professor of Computer Science and Head-APPCAIR at BITS Pilani, Goa Campus.
1 – Introduction and Prerequisites
3.1 – The Newton-Raphson Method
3.2 – Multi-class Classification
3.3 – So what is maximum likelihood estimation, anyway?
3.6 – Completing Softmax Regression: Implementation and Regularization
3.7 – Putting it all together: MNIST digit recognition
3.8 – Where do the cost functions come from?
4 – Finding a good learning rate
5 – Evaluating Classification Models
6 – Locally Weighted Linear Regression
6.1 – Implementing Locally Weighted Regression
7 – Evaluating Regression Models
8 – Gaussian Discriminant Analysis
8.1 – Implementing Gaussian Discriminant Analysis – Iris data classification
8.2 – The Naive Bayes algorithm
8.3 – The Naive Bayes Multinomial Event Model
9 – Excursus: Principal Components Analysis
10.1 – Implementing the ID3 algorithm
10.2 – Improving the ID3 algorithm
11.2 – Posing the dual problem
11.3 – Implementing a linear 2-class SVM
11.5 – Regularization and Soft Margin SVM
12 – Introduction to Cluster Analysis
12.3 – Hierarchical Clustering
12.4 – Gaussian Mixture Models
12.7 – Finding the number of clusters
13 – Excursus: k-Nearest Neighbors
15.3 – Disadvantages of neural networks
Lecture videos by Dr. Saha are also available on YouTube:
Beginning with ML 