## Posts

### Linear Regression - Performance Checking & Diagnosis

Hello World, This is Saumya, and I am here to help you understand and implement Linear Regression in more detail and will discuss various problems we may encounter while training our model along with some techniques to solve those problems. There won't be any more programming done in this post, although, you can try it out yourself, whatever is discussed in this blog.  So now, first of all, Let's recall what we studied about Linear Regression in our previous blog . So, we first discussed about certain notations regarding to machine learning in general, then the cost function, h θ (x (i) )= θ 0 x 0 +θ 1 x 1 . Further we discussed about training the model using the training set by running the gradient descent algorithm over it. We also discussed about the Cost Function. Now, before we begin, I want to talk about the Cost Function in brief. Cost function, as we defined, is, J(θ)= i=1 m ∑ ( h θ (x (i) )-y (i) ) 2 / (2*m). If we define cost function, we can define it as t
Recent posts

### K-Means Clustering for Image Compression, from scratch.

Hello World, This is Saumya, and I am here to help you understand and implement K-Means Clustering Algorithm from scratch without using any Machine Learning libraries . We will further use this algorithm to compress an image. Here, I will implement this code in Python, but you can implement the algorithm in any other programming language of your choice just by basically developing 4-5 simple functions. So now, first of all, what exactly is Clustering and in particular K-Means? As discussed in my blog on  Machine Learning , Clustering is a type of unsupervised machine learning problem in which, we find clusters of similar data. K-means is the most widely used clustering algorithm. So basically, our task is to find those centers for the clusters around which our data points are associated. These centres of the Clusters are called centroids(K). Note that, these cluster centroids, may or may not belong to our dataset itself. Since our problem is to choose these

### Linear Regression from Scratch

Hello World, This is Saumya, and I am here to help you understand and implement Linear Regression from scratch without any libraries. Here, I will implement this code in Python, but you can implement the algorithm in any other programming language of your choice just by basically developing 4-5 simple functions. So now, first of all, what exactly is Linear Regression? As discussed in my blog on Machine Learning , Linear Regression is used to identify linear relationships between the input features x (i) and the output labels of the training set y (i) and thus form a function F(x (i), θ), which would help in predicting future values. This function, is called hypothesis and is usually denoted by h (x (i) , θ). Note that, x(lowercase) is used to denote a single training example as a whole, where as we use X (i,j) is used to point the j th feature for the i th training example. But confusing?? Let's simplify it!! As shown, to show the whole featu

### Machine Learning

Hello World, This is Saumya, and I am here to help you understand the basics of Machine Learning, what exactly does it mean, what are its types, and how powerful of a tool it can be. We have all been hearing recently about the term "Artificial Intelligence" recently, and how it will shape our future. Well, Machine Learning is nothing but a minor subfield of the vast field of A.I. Some of you might feel they both are basically the same thing, but in reality, they are not. A.I. is basically a cluster of interconnected fields, which makes it difficult for us to sometimes visualize the difference between them all. Now then, what is the difference? By definition, A.I. is basically trying to create a machine that is capable to think the way we humans do and specifically learn from our experiences. On the other hand, M.L. is computer's way of learning from data and henceforth make decision from the information obtained. Again, We can say that ML is basically