Random Forest, Adaboost & Decision Trees in Machine Learning
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 22 lectures (2 hour, 58 mins) | Size: 1.82 GB
Learn Machine Learning on Random Forest, Adaboost, Decision Trees

What you'll learn

Knowing how to write a Python code for Random Forests.
Implementing AdaBoost using Python.
Having a solid knowledge about decision trees and how to extend it further with Random Forests.
Understanding the Machine Learning main problems and how to solve them.
Understanding the differences between Bagging and Boosting.
Reviewing the basic terminology for any machine learning algorithm.

Requirements

Python basics
NumPy, Matplotlib, Sci-Kit Learn
Basic Probability and Statistics

Description

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning.

Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Google famously announced that they are now "machine learning first", and companies like NVIDIA and Amazon have followed suit, and this is what's going to drive innovation in the coming years.