Explanation
Machine learning has produced amazing results, such as being able to analyze medical images and predict diseases on a par with human experts. Google’s AlphaGo was able to beat a world champion in the strategy game using deep reinforcement learning. Machine learning is even being used to teach self-driving cars, which will revolutionize the automotive industry. Imagine a world with far fewer car accidents, simply by removing the element of human error.
Google has famously announced that they are now “machine learning first”, and companies like NVIDIA and Amazon have followed suit, and this is what will drive innovation in the coming years. Machine learning has permeated all kinds of products, and is used in many industries, such as finance, online advertising, pharmaceuticals, and robotics. It’s a widely applicable tool that will benefit you no matter what industry you’re in, and it will also open up more career opportunities when you get good at it.
What will you learn?
- Understand and remove biased variables
- Understand the bootstrap process and its application in wallet
- Understand why a wallet improves classification and retrieval performance
- Understand and implement Random Forests
- Understand and implement AdaBoost
Who is this course for?
- Understand the types of models that win machine learning competitions (Netflix Price, Kaggle)
- Students studying machine learning
- Professionals who want to apply data science and machine learning to their work
- Entrepreneurs who want to use data science and machine learning to improve their business
- Computer science students who want to learn more about data science and machine learning
- For those who know some basic machine learning models but want to know how today’s most powerful models (Random Forest, AdaBoost, and other hybrid methods) are built
A Guide to Hybrid Machine Learning in Python: Random Forest, AdaBoost
Abstract of Hybrid Machine Learning in Python: Random Forest, AdaBoost 2023-1
Requirements
- Calculus (separation)
- Learn Numpy, Matplotlib, Sci-Kit
- K-Nearest Neighbors, Decision Trees
- Probability and Statistics (undergraduate level)
- Linear regression, logistic regression
Pictures
Sample Clip
Installation Guide
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Subtitle : English
Quality: 720p
Download Links
Password file: free download software
file size
1.08 GB