Machine learning is a science of trial and error. Having a lot of data is almost more important than having a good algorithm. No machine learning model can be the most effective for all problems.

Here’s how I’ll get started with machine learning in Python.

Artificial intelligence, machine learning, and deep learning

### Learn basic Python syntax

First, I found an introductory tutorial on the Python website and went over the basic syntax of Python quickly. I believe this is not a problem for people with a little programming background.

As a practical matter, I then implemented a command-line translation script in Python. This is the beginning of Python.

Here is the setup process for the Python environment on Mac. In this article, I show you how to deal with both the system’s native and self-installed versions of Python.

###Python machine learning library

Python has many libraries that deal with machine learning, such as Theano, TensorFlow, PyTorch, SciKit-Learn, etc. I chose SciKit-Learn (skLearn) as a springboard for getting started because it encapsulates and abstracts machine learning and allows beginners to get out of their math nightmares and practice machine learning. In addition to this, you will need to learn the following Python libraries for data processing or scientific computation.

  • Numpy: provides powerful n-dimensional arrays and related operations library, see numpy quick Start notes.

  • Pandas: A library that provides similar relational or tag data structures. See pandas Quick Start Notes.

Scipy: a library that integrates many mathematical functions, please refer to the official documentation.

  • matplotlib: a tool to draw data into images, you can refer toMatplotlib Quick Start Notes.

### From 0 to 1, an introductory guide to machine learning

Machine learning isn’t that deep, it’s fun (1)

Machine learning isn’t that deep, it’s fun (2)

Machine learning isn’t that deep, it’s fun (3)

Machine learning isn’t that deep, it’s fun (4)

##### From 0 to 1, the introduction to machine learning is as follows:

Sklearn can provide the implementation of machine learning algorithms in a black-box manner, which is beneficial for beginners. However, it is obviously not enough to just stay here. If we do not master certain basic knowledge and principles, we cannot conduct modeling and selection of display problems. Therefore, after learning the SkLearn algorithm, you must consult relevant documents to understand the knowledge and principle behind the algorithm.

What is machine learning? What exactly is the machine learning?

What does the training data look like and what is the model that the machine learned

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