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Latest episodes from KJ_Pods

Mutual funds

Play Episode Listen Later Mar 20, 2021 9:47


What are mutual funds | How does it work | Pros & cons MF investing in mutual funds | All about of mutual funds

pros mf mutual funds
Clearing & settlement process in stock markets

Play Episode Listen Later Mar 20, 2021 7:49


Clearing & settlement process in stock markets

clearing settlement stock markets
Machine learning

Play Episode Listen Later Feb 12, 2021 9:45


All about Machine learning | Applications of Machine learning techniques

applications machine learning
Deep learning

Play Episode Listen Later Feb 12, 2021 1:14


Deep Learning

deep learning
Feature encoding

Play Episode Listen Later Feb 12, 2021 0:38


Feature encoding

feature encoding
Feature engineering

Play Episode Listen Later Feb 12, 2021 1:16


Feature engineering

feature feature engineering
Feature scaling

Play Episode Listen Later Feb 12, 2021 0:27


Feature scaling

scaling feature
Outlier handling

Play Episode Listen Later Feb 12, 2021 1:28


Outlier handling

outliers
Unsupervised learning (UL) is a type of algorithm that learns patterns from untagged data.

Play Episode Listen Later Feb 10, 2021 1:26


Unsupervised learning (UL) is a type of algorithm that learns patterns from untagged data. The hope is that through mimicry, the machine is forced to build a compact internal representation of its world. In contrast to Supervised Learning (SL) where data is tagged by a human, eg. as "car" or "fish" etc, UL exhibits self-organization that captures patterns as neuronal predelections or probability densities. The other levels in the supervision spectrum are Reinforcement Learning where the machine is given only a numerical performance score as its guidance, and Semi-supervised learning where a smaller portion of the data is tagged. Two broad methods in UL are Neural Networks and Probabilistic Methods.

data patterns algorithms semi learns ul neural networks unsupervised reinforcement learning unsupervised learning
Feature Selection | core concepts in machine learning

Play Episode Listen Later Feb 9, 2021 0:49


Feature Selection is one of the core concepts in machine learning which hugely impacts the performance of your model. The data features that you use to train your machine learning models have a huge influence on the performance you can achieve. Irrelevant or partially relevant features can negatively impact model performance. Feature selection and Data cleaning should be the first and most important step of your model designing.

data feature machine learning selection irrelevant core concepts

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