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Machine Learning

MachineX: A tour to KSAI – Neural Networks

Reading Time: 4 minutes In this blog we would look into how we can use KSAI; A machine learning library purely written in Scala using most of its feature and functional aspects of programming, you can read more about the library at KSAI Wiki, alternatively you can even fork the project from here, KSAI has a rich set of algorithms that address some of the vital problems in classification, Continue Reading

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MachineX: An Introduction to KSAI, a machine learning library

Reading Time: 3 minutes Take a closer look at Linkedin or any media platform for a couple of minutes, you’ll find that the hot topic in the technology section nowadays is Machine Learning and Artificial Intelligence. Why Machine learning and artificial intelligence? Well needless to say it is transforming the world like anything. People are doing good in business by predicting different aspects, doctors are doing good in medical Continue Reading

Code Combat II : The Code Battle For The Vanguard Continues…

Reading Time: 4 minutes “If you can dream it, you can do it. ”  -Walt Disney For some coding is a job. For some, it is an exercise. But for us folks here at Knoldus, it’s a Passion. So in order to bring a twist in the daily work schedule, Knoldus held an overnight Hackathon competition within the organization on 18th May 2018 which presented an opportunity for every Knolder(employees Continue Reading

KnolX: NAIVE BAYES CLASSIFIER

Reading Time: < 1 minute Hi all, Knoldus has organized a 30 min session on 27th April 2018 at 4:00 PM. The topic was NAIVE BAYES CLASSIFIER. Many people have joined and enjoyed the session. I am going to share the slides here. Please let me know if you have any question related to linked slides.

KnolX: Machine Learning with Artificial Neural Networks

Reading Time: < 1 minute Hi all, Knoldus has organized a 30 min session on 8th December 2017 at 4:15 PM. The topic was Machine Learning with Artificial Neural Networks. Many people have joined and enjoyed the session. I am going to share the slides here. Please let me know if you have any question related to linked slides.   Machine Learning with Artificial Neural Networks from Knoldus Inc. Here’s the video of the Continue Reading

MachineX: Total Support Tree for Association Rule Generation

Reading Time: 5 minutes 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

Reading Time: 4 minutes 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

Reading Time: 5 minutes 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?

Reading Time: 3 minutes 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

Reading Time: 2 minutes 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: Choosing Support Vector Machine over other classifiers

Reading Time: 4 minutes When one has to select the best classifier from all the good options, often he would be on the horns of a dilemma. Decision tree/Random Forest, ANN, KNN, Logistic regression etc. are some options that often used for the choice of classification. Every one of it has its pros and cons and when to select the best one the probably the most important thing to Continue Reading

What is Deep Learning??

Reading Time: 4 minutes This term “Deep Learning”, is on fire for past two decades. Every machine learning enthusiast wants to work on it and many big companies are already making an impact on Data Science field by exploring it e.g. Google Brain project from Google or DeepFace from Facebook. The reason is simple, experts say and I quote “for most flavors of the old generations of learning algorithms … performance will Continue Reading

MachineX: One more step towards NAIVE BAYES

Reading Time: 4 minutes I hope we understand the conditional probabilities and Bayes theorem through our previous blog. Now let’s use this understanding to find out more about the naive Bayes classifier. NAIVE BAYES CLASSIFIER Naive Bayes is a simple technique for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set. Naive Bayes Continue Reading

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