Let us start building a decision tree!
In this lecture we start building a classification tree from scratch. In the process, we understand the concept of leaf impurity and the need for a metric to quantify leaf impurity.
0:00 Introduction: Tree terminology
1:54 Training data
3:42 Why not logistic regression
9:42 Our goal
11:20 How to decide the root node?
17:50 The concept of impurity
20:17 Need for an impurity metric
Our other playlists related to machine learning:
(1) ML Teach by Doing (37 videos): • Machine Learning: Teach by Doing
(2) Building Neural Networks from Scratch (35 videos):
• Building Neural Networks from Scratch
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Vizuara philosophy:
As we learn AI/ML/DL the material, we will share thoughts on what is actually useful in industry and what has become irrelevant. We will also share a lot of information on which subject contains open areas of research. Interested students can also start their research journey there.
Students who are confused or stuck in their ML journey, maybe courses and offline videos are not inspiring enough. What might inspire you is if you see someone else learning and implementing machine learning from scratch.
No cost. No hidden charges. Pure old school teaching and learning.
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🎓 Dr. Sreedath Panat (MIT PhD, IIT Madras department gold medalist)
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🎓 Abhijeet Singh (Software Developer at Vizuara, GSOC 24, SOB 23)
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🎓 Sourav Jana (Software Developer at Vizuara)
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