Machine Learning Made Easy

In the past decade, there has been a surge in interest in machine learning. Almost every day, we can see discussions about machine learning in a variety of computer science courses, industry conferences, the Wall Street Journal, and more.

Developing new machine learning algorithms and describing how they work and why work is a science is often not necessary when developing a learning system.

Open source machine learning tools also allow you to migrate learning, which means you can solve machine learning problems by applying other aspects of knowledge.

Machine learning is a combination of art and science. No machine learning algorithm can solve all the problems. There are several factors that can influence your decision to choose a machine learning algorithm.

Recently, Airbnb machine learning infrastructure has been improved, making the cost of deploying new machine learning models into production environments much lower. For example, our ML Infra team built a common feature library that allows users to apply more high-quality, filtered, reusable features to their models.

Some tasks are more complicated to code directly. We can't handle all the nuances and simple coding. Therefore, machine learning is necessary. Instead, we provide a large amount of data to machine learning algorithms, allowing the algorithm to continuously explore the data and build models to solve the problem.

Anaconda is the first choice for beginner Python and entry machine learning. It is a Python distribution for scientific computing that provides package management and environment management capabilities to easily handle multi-version python coexistence, switching, and various third-party package installation issues.

The concept of machine learning was first born in science fiction, and its new features were quickly discovered and applied, but with the inevitable limitations.

The past spring Festival has allowed programmers to have a rare holiday break, but artificial intelligence in the holiday has been improving, we saw the Facebook AI director Yann LeCun, the Hong Kong University of Science and Technology, Director of the Department of Computer and Engineering Yangqiang and other artificial intelligence Daniel's cool thinking about the upsurge of artificial intelligence, Google has also seen the development of artificial intelligence gaming systems that transcend human levels in specific conditions. 　　Here's a look at the new Year's inspiration from Daniel's artificial intelligence. Yann LECUN:IBM True North is "the straw race science" ...

Algorithms in Machine Learning (1) - Random Forest and GBDT Based on Decision Tree Model Combination. Decision Tree This algorithm has many good features, such as training time complexity is low, the prediction process is relatively fast, the model is easy to display (easy to get the decision tree made of pictures) and so on. But at the same time, the single decision tree has some bad points, such as easy over-fitting, although there are some ways, such as pruning can reduce this situation, but not enough. Model combinations (say Boosting, Bagging, etc.) are related to decision trees ...