Machine learning Note one: early acquaintance

　　This article is the author through the "Machine learning Practice," the Book of Learning, the following made his own study notes. The writing is clumsy and correct!

Machine Learning (machines learning, ML) is a multidisciplinary interdisciplinary subject involving probability theory, statistics, approximation theory, convex analysis, algorithmic complexity theory and many other disciplines. Specialized in computer simulation or realization of human learning behavior, in order to acquire new knowledge or skills, reorganize the existing knowledge structure to continuously improve their performance. Machine learning has been used in a wide range of applications, such as data mining, computer vision, natural language processing, biometric identification, search engines, medical diagnostics, detection of credit card fraud, stock market analysis, DNA sequencing, speech and handwriting recognition, strategy games, and robotic applications.

Machine learning is divided into two main types: one is supervised learning, the other is unsupervised learning. Supervised learning is the analysis and training on the basis of the known data samples, and the classification data model is used to predict the numerical data. Unsupervised learning is the clustering of data. Therefore, the main task of machine learning is classification.

What issues do we need to consider when applying machine learning algorithms to actual projects?

1, how to choose the appropriate algorithm?

To select the appropriate algorithm, first of all, ask two questions: (1) The purpose of using machine learning algorithm, (2) Analyze what data is collected.

2. What are the steps to develop a machine learning application?

(1) Collect data

(2) Prepare input data

(3) Analysis of input data

(4) Training algorithm

(5) Test algorithm

(6) using the algorithm

Summary of machine learning related learning materials:

1. "Machine Learning Practice" Pdf+python source code + related learning materials Web site;

1. The course "machine learning", by Andrew Ng Daniel of Stanford University, NetEase public course;

2. Rachel-zhang's study notes on Ng's machine learning class;

3. Jerrylead's study notes on Ng's machine learning class;

4. Tornadomeet's machine learning summary, which also includes the content of deep learning;

5.11 Open source machine learning projects;

　　

Machine learning Note one: early acquaintance