Author: Akshansh Jain

MachineX: Total Support Tree for Association Rule Generation

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

MachineX: Frequent Itemset generation with the FP-Growth algorithm

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

MachineX: Understanding FP-Tree construction

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

MachineX: Why no one uses apriori algorithm for association rule learning?

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

MachineX: Two parts of Association Rule Learning

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

MachineX: Improve accuracy of your ML models before even writing them

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

MachineX: Layman guide to Association Rule Learning

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

Introduction to AWS DynamoDB

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

Getting started with TensorFlow: Writing your first program

In my previous blog , we saw what Tensorflow is and some of it’s terminologies. In this blog, we are going to go ahead and implement a very basic program in TensorFlow using Python to see it in action. To import TensorFlow library, use import tensorflow as tf The computation in TensorFlow consists of two stages – Building the computational graph Running the computational graph Continue Reading

Getting started with TensorFlow: A Brief Introduction

TensorFlow is an open source software library, provided by Google, mainly for deep learning, machine learning and numerical computation using data flow graphs. Looking at their website, the first definition they have written for TensorFlow goes something like this – TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges Continue Reading

PJAX: Loading your website faster!

We all hate waiting for websites to load before we can start using or surfing it. Internet has a come a long way with great speeds to decrease this time, but this has just made the user more impatient. After a lot of research through the decade, it has been found out that on an average a user would not wait for more than 4 Continue Reading

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