In this blog, we are going to look at what the Builder Pattern is and how does it help us construct objects of classes easily. But before understanding that, we need to understand why did we come up with the Builder Pattern in the first place. So, let’s discuss the problem statement due to which the Builder Pattern came into existence.
In this blog, we are going to explore and learn about K-Fold Cross Validation. K-Fold Cross Validation is a statistical method to evaluate a Machine Learning model’s performance. So, to understand what K-Fold Cross Validation is, we first need to understand what evaluating a model means, and why do we need to do that.
This blog is primarily aimed towards getting DGraph up and running on your local machine, create some schema in DGraph(alter), add/delete data from DGraph(mutation) and get the data from it(query). We are going to keep the blog simple, exploring each of the mentioned operation in detail in later blogs.
In many of my previous blogs, I have posted about Association Rule Learning, what it’s about and how it is performed. In this blog, we are going to use Association Rule Learning to actually see it in action, and for this purpose, we are going to use KSAI, a machine learning library purely written in Scala. So, let’s begin. Adding KSAI to your project You Continue Reading
In our previous blogs on Association Rule Learning, we have seen the FP-Tree and the FP-Growth algorithm. We also generated the frequent itemsets using FP-Growth. But a problem arises when we try to mine the association rules out of these frequent itemsets. Generally, the number of frequent itemsets is massive and to run an algorithm on them becomes very memory inefficient. So, to store these Continue Reading
In our previous blog, MachineX: Understanding FP-Tree construction, we discussed the FP-Tree and its construction. In this blog, we will be discussing the FP-Growth algorithm, which uses FP-Tree to extract frequent itemsets in the given dataset. FP-growth is an algorithm that generates frequent itemsets from an FP-tree by exploring the tree in a bottom-up fashion. We will be picking up the example we used in Continue Reading
In my previous blog, MachineX: Why no one uses apriori algorithm for association rule learning?, we discussed one of the first algorithms in association rule learning, apriori algorithm. Although even after being so simple and clear, it has some weaknesses as discussed in the above-mentioned blog. A significant improvement over the apriori algorithm is FP-Growth algorithm. To understand how FP-Growth algorithm helps in finding frequent Continue Reading
In my previous blog, MachineX: Two parts of Association Rule Learning, we discussed that there are two parts in performing association rule learning, namely, frequent itemset generation and rule generation. In this blog, we are going to talk about one of the algorithms for frequent itemset generation, viz., Apriori algorithm. The Apriori Principle Apriori algorithm uses the support measure to eliminate the itemsets with low Continue Reading
In our previous blog, MachineX: Layman guide to Association Rule Learning, we discussed what Association rule learning is all about. And as you can already tell, with a large dataset, which almost every market has, finding association rules isn’t very easy. For these, purposes, we introduced measures of interestingness, which were support, confidence and lift. Support tells us how frequent an itemset is in a given dataset and confidence Continue Reading
Every machine learning practitioner will agree with me when I say that one of the most important part of machine learning is preparing data for machine learning. It certainly requires some experience to properly and effectively prepare data for machine learning. Although data preparation is in itself a really big topic, today we will only be looking at a part of its process, that is Continue Reading
Association rule learning is one of the most common techniques in data mining and as well as machine learning. The most common use, which I’m sure you all must be aware of, is the recommendation systems used by various e-shops like Amazon, Flipkart, etc. Association rule learning is a technique to uncover the relationship between various items, elements, or more generally, various variables in a Continue Reading
DynamoDB is a database service provided by Amazon. Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. You can use it to store and retrieve any amount of data without worrying about hardware provisioning, setup & configuration, replication, software patching, or cluster scaling. It mostly takes care of these things itself while also providing options to configure these Continue Reading