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Data Science: Supervised Machine Learning in Python

#artificialintelligence

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.


Data Science: Supervised Machine Learning in Python

@machinelearnbot

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.


Data Science: Supervised Machine Learning in Python

#artificialintelligence

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.


Data Science: Supervised Machine Learning in Python

@machinelearnbot

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.


Machine Learning in Python - PyImageSearch

#artificialintelligence

Struggling to get started with machine learning using Python? In this step-by-step, hands-on tutorial you will learn how to perform machine learning using Python on numerical data and image data. By the time you are finished reading this post, you will be able to get your start in machine learning. To launch your machine learning in Python education, just keep reading! Inside this tutorial, you will learn how to perform machine learning in Python on numerical data and image data. Using this technique you will be able to get your start with machine learning and Python! Along the way, you'll discover popular machine learning algorithms that you can use in your own projects as well, including: This hands-on experience will give you the knowledge (and confidence) you need to apply machine learning in Python to your own projects. Before we can get started with this tutorial you first need to make sure your system is configured for machine learning. Today's code requires the following libraries: In order to help you gain experience performing machine learning in Python, we'll be working with two separate datasets. The first one, the Iris dataset, is the machine learning practitioner's equivalent of "Hello, World!" (likely one of the first pieces of software you wrote when learning how to program). The second dataset, 3-scenes, is an example image dataset I put together -- this dataset will help you gain experience working with image data, and most importantly, learn what techniques work best for numerical/categorical datasets vs. image datasets.