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This article mainly introduces "how to start entry machine learning". In daily operation, I believe many people have doubts about how to start entry machine learning. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful for you to answer the question of "how to start getting started with machine learning"! Next, please follow the editor to study!

Why choose Python?

The two most important program languages in the field of data science and machine learning are Python and RMagol Python, which are simple and easy to learn, have a wide range of applications (not limited to data analysis) and have a smooth learning curve, and are suitable for the first entry program language. Data analysis can be done through pandas, SciPy/NumPy, sckikit-learn, matplotlib and statsmodels, which are suitable for engineering tasks and projects that need to be integrated with network applications. As for R, which is a program language developed by statisticians, it is good at statistical analysis and chart drawing, and is often used in the field of academic research. In general, Python and R are not mutually exclusive, but complementary. Many data engineers and scientists often convert in Python and R, use R for small model verification, statistical analysis and charting, and transfer to Python when writing algorithms to interact with databases and network services. To reduce the cost of learning.

In addition, Python itself is a general language, in addition to data science, it can also be widely used in network development, website construction, game development, web crawlers and other fields, when you need to integrate system products and services, you can serve as an one-stop development language, more importantly, Python can also be used as a glue language very easily and Chand Craft + and other better language integration. In short, Python is a simple, easy-to-learn but powerful program language that is worth investing in, so let's use Python here to introduce it.

For a comparison between Python and R, here are two articles that you can refer to in the data science community: r versus Python, Which is better for data analysis:R or Python?

How to start entry-level machine learning?

In fact, data science is an interdisciplinary subject, and the following knowledge is usually necessary in learning how to use Python for machine learning:

Machine learning algorithm

Python programming language and data Analysis Library

Linear Algebra / Statistics and other related subjects

Domain knowledge of the field of expertise (Domain Knowledge)

In order to master the knowledge of the above three areas (we focus on the core techniques of machine learning and ignore the mastery of domain knowledge in data science for the time being), specifically, we can have the following steps to refer to:

1. Master basic knowledge of Python programming language

Codecademy

DataCamp (you can also learn R)

Learn X in Y Minutes (X = Python)

Learn Python theHard Way

two。 Understand the basics of basic mathematics / statistics and machine learning

Khan College Linear Algebra

Introto Deive Statistics

Introto Inferential Statistics

Andrew Ng Machine Learning course

Andrew Ng machine learning notes

CarnegieMellon University Machine Learning

MachineLearning Foundations (cornerstone of machine learning)

3. Know how to use Python scientific computing libraries and suites

It is recommended to install Anaconda, which supports multiple versions of Python across platforms. It installs suites for data analysis and scientific computing by default, and comes with spyder editor and JupyterNotebook (IPythonNotebook). It provides a web version interface that allows users to develop and maintain Julia, Python or R programs through the browser.

Numpy: scientific Analysis, ScipyLecture Notes Teaching documents

Pandas: data analysis

Matplotlib: can draw a glance

Scikit-learn: machine learning tool

4. Using scikit-learn to learn Python machine learning application

MachineLearning: Python machine learning: using Pytho ­n

5. Using Python to implement Machine Learning algorithm

Perceptron

Decision tree

Linear regression.

K-means clustering

6. Implement advanced machine learning algorithm

SVM

KNN

RandomForests

Reduce the dimension

Verification model

7. Understand the implementation and application of deep learning (DeepLearning) in Python

NTU Applied DeepLearning

Stanford DeepLearning

Deep Learning (Deep Learning) self-study material recommendation

At this point, the study on "how to start getting started with machine learning" is over. I hope to be able to solve everyone's doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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