DataHour: Think like a Data Scientist
DataHour: Think like a Data Scientist
19 Aug 202213:08pm - 19 Aug 202214:08pm
DataHour: Think like a Data Scientist
About the Event
A major challenge that every data scientist faces today is computational power. Companies want professionals who can extract predictions from the raw data cost-effectively. To get the predictions right, the data scientists have to treat the raw data well and extract necessary features for the model of their choice. Choosing the model and empowering it with the necessary optimization is one way to arrive at a more number of truly positive predictions is a very generic thought.
Improving the accuracy of a model does not necessarily imply making the model more robust, and hence we bring back our attention to the features supplied to the model. Advanced Feature Engineering is a crucial thought process to improve the ability of your model to predict well. To think about feature engineering and to effectively perform an analysis, domain knowledge plays a key role. In this DataHour, you would learn practically about the thought process every data scientist should possess to make the machine learning models more robust.
Prerequisites: This DataHour requires minimal exposure to a data science framework and basic terminology in data analytics.
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
Who is this DataHour for?
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
About the Speaker
Participate in discussion
Registration Details
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- Professional Exposure
- Networking Opportunities
- Thought Leadership
- Knowledge Exchange
- Leading-Edge Insights
- Community Contribution
