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Finance | Tech | Data science | Stories

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    Mutual funds

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

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    Clearing & settlement process in stock markets

    Clearing & settlement process in stock markets

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    Machine learning

    All about Machine learning | Applications of Machine learning techniques

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    Deep learning

    Deep Learning

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    Feature encoding

    Feature encoding

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    Feature engineering

    Feature engineering

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    Feature scaling

    Feature scaling

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    Outlier handling

    Outlier handling

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

    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.

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    Feature Selection | core concepts in machine learning

    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.

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