Machine learning is an important part of the field of artificial intelligence, and more and more people are paying attention.

But most people don't know much about machine learning, they are incomplete, and there are even some misunderstandings.

This article is a machine learning science for all, involving all the key knowledge points related to machine learning.

"At the end of the article, there is a PDF of 75 page available for free download.'

A picture to understand machine learning
A picture to understand machine learning

Why understand machine learning?

I am not a computer, nor a researcher. Why do you need to understand machine learning?

Before 20, you might say the same thing: "I am a traditional industry, why should I understand the Internet?" But today, 20, the Internet has changed dramatically in the traditional industry, and this change is still Continue.

Many people today will ask: "I am not in artificial intelligence, why should I understand machine learning?"

I am not engaged in artificial intelligence, why should I understand machine learning?

The artificial intelligence technology represented by machine learning is likely to cause great changes to various industries in the near future!

Start understanding and paying attention to machine learning now, and don't be eliminated by the next wave of technology.

Machine learning knowledge system

Machine learning belongs to the category of artificial intelligence, so we need to briefly understand the key elements of artificial intelligence 3:

  1. data
  2. algorithm
  3. Computing power
Artificial intelligence 3 elements: data, algorithms, computing power

Let's take the example of making a table:

Wood is the data, the basis is the material; the line that makes the table is an algorithm that solves turning wood into a table; the machine in the factory is the computing power. The more powerful the machine, the higher the efficiency of making the table and the faster the speed.

For machine learning, his particularity is mainly reflected in algorithms and data. Different algorithms require different data. Here is a "machine learning panorama":

Machine learning science

Key knowledge points of machine learning

First of all, we have a holistic concept of artificial intelligence and machine learning. It is recommended to read the following 2 articles:

'What is "2019 Update" for artificial intelligence? (The essence of AI + development history + limitations)"

'I understand machine learning in one article! (3 learning methods + 7 practical steps + 15 common algorithms)"

Secondly, machine learning has 3 important learning methods, which can be learned in detail through the following 3 articles:

'What is supervised learning? How to understand classification and regression?"

'What is unsupervised learning? Concept, usage scenarios and algorithms"

'What do you understand in a text is reinforcement learning? (basic concept + application scenario + mainstream algorithm)"

Finally, there are many algorithms for machine learning. Here is a list of 12 mainstream algorithms.Click on the link below to view details):

  1. Linear regression
  2. Logistic regression
  3. Linear discriminant analysis
  4. Decision tree
  5. Naive Bayes
  6. K proximity algorithm
  7. Learning vector quantization
  8. Support Vector Machines
  9. Random forest
  10. Adaboost
  11. Limit Boltzmann machine
  12. K-means clustering

All of the above has been compiled into the PDF of the 75 page, "The Machine Learning Science for Everyone", click the button below to download.

Download "Machining Science for Everyone"
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