Introduction to Machine Learning with Scikit-Learn
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Machine learning Algorithm selection faces a unique catch22 situation where you get the data to train but need unseen(new)data to test the algorithm which is available only with production. To avoid this situation and understand the performance of the selected Machine Learning algorithm, we need to generate TEST DATASET from the available DATA Set.
Welcome to the video series on Introduction to Machine Learning with Sciki Python & Learn. This video contains Chapter - 6.3. In this chapter, I've explained the idea of data normalization and why it's important to do the same Though explained in the context of Linear Regression, Data Normalization can be applied to almost all machine learning algorithms. It's a useful concept and helps data engineers to deal with large data sets
This video contains Chapter - 6.2. In this chapter, I’ve explained the detailed maths behind Linear Regression and how Gradient Descent Algorithm works. In this video, we’ll be doing everything in python which is internally taken care by scikit learn. This is only for demonstration purpose. We should use scikit learn even when we know everything about it because it is fast and takes cares of training the model. Hope it helps you to learn about how Linear Regression actually works
Welcome to the video series on Introduction to Machine Learning with Scikit-Learn. This video contains Chapter - 6.1. In this chapter, I've explained our first Machine Learning algorithm called Linear Regression using just five data points for easy understanding In context of this algorithm, I've also explained the unified machine learning algorithm and how generic interface can be used for almost all ML algorithms using scikit-Lea
This video contains Chapter - 5 which introduces us to Hello World DataSet from Scikit Learn called Iris Dataset. In this video, I've explained how the dataset shall look like when we want to use the same with Machine Learning algorithms provided by Scikit Learn. I've used the Iris DataSet provided by Scikit Learn for the same