FAQ ML Part 4

Machine Learning Interview Questions and Answers | part 4

In this video post I have explained about the below ML FAQ:
1. What is Gini
2. What is Gini Index
3. How to calculate Gini and Gini Index
4. How Gini Index helps to decide the Parent and Decision nodes in Decision Tree

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7 Fun Comic Illustrations That Best Describe Machine Learning

When are you looking for the best way to express yourself without speaking, which technique comes to your mind? Yes, that’s right, it is an illustration. The illustration is one of the best ways to express a feeling or describe something. This technique has been started by our ancestors in the primitive age. It is a medium of explaining things easily so that anyone can understand. There are various types of comics you will find on the book store and online. Even you can read these tremendous African graphic novels. However, along with making comic stories and anime, there are other benefits of it. In technology, the comic is one of the top tools to teach and explain digital innovations and devices. In this article, you will find 7 fun comic illustrations that best describe machine learning.

A Step by Step Guide to Implement Decision Tree using Python | Machine Learning

In this we will learn from scratch how to implement decision tree using python. We will solve one classification problem and build the model from scratch. Following are the points we will be covering in this post:
Exploratory Data Analysis – EDA
Data Visualization
Data Pre-processing
Data Spliting- Stratified Sampling
Oversampling – SMOTE
Model Training
Fine Tuning
Hyper parameter Tuning

Machine Learning Interview questions and answers part 2 | ML Faq

This post is part 2 in the series of frequently asked Machine Learning Interview Questions and Answers. Machine Learning Frequently asked Interview Questions and Answers Part 2

1. What is Feature Scaling and why and where it is needed?
2. Normalization vs Standardization
3. What is the bias-variance trade-off?
4. Define the Overfitting problem and why it occurs?
5. What are the methods to avoid Overfitting in ML?

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