Conventional Python course

I've seen a lot Python Tutorials and books , Most of them say that Python：

First from Python Development history of , Introduce Python The basic grammatical rules of ,Python Of list, dict, tuple And so on , Then I'll talk about string processing and regular expressions , Introduction documents, etc IO operation , Introduce exception handling again , It's just one chapter at a time .

Many of them are boring theories , The more you look, the more tired you are , The more tired I am, the less I want to see it .

that , Is there a better way ？

This Python special column

Because I've had that “ self-taught ” Python The confused period of , So I know a good systematic study plan and teacher's explanation , It's the key to save more youth of our programmers .

So I extracted more than five years of work experience , And studying in American universities AI The professional postdoctoral teacher wrote this together 60 God's column .

When other teachers introduce knowledge points, they will say “ What is this thing ”, But I don't want to . I think “ Why is this thing like this ” perhaps “ What's the advantage of adapting to what's needed in what scene ”, On the contrary, it will make you have a deeper understanding of knowledge itself .

In line with Interesting and interesting , Dry and pure , Practical first Principles , Five features of the column ：

First of all , Case teaching . It's boring to learn pure theoretical knowledge , But combined with a small case , In this way , It's better to learn .

second , Try to be fun . illustrated , Add interesting examples 、 Interesting little projects , More fun to learn .

Third , Self system . Like a detective film , Step-by-step , To spread out one by one Python Technology stack .

Fourth , Dissect some Python Common interview questions . Explain theoretical knowledge , Combined with cases , At the same time, relevant interview questions , Get through the theoretical knowledge thoroughly .

The fifth , Project practice . Not only will there be a real combat environment deployment plan , And the actual project ：Python GUI Development projects ,Kaggle Data analysis project , Machine learning project .

Column catalog

In order to let you in self-study can be based on their own learning basis tailor , I will be the whole Python Content By day , Not only can you lighten your daily study burden , And it can test the learning effect more effectively .

Python The basic chapter

Day 1：Python Two characteristics and four basic grammars

Day 2：Python Summary of four data types

Day 3：list and tuple Basic operation 、 Detailed operation of deep and shallow copy and slice, etc 5 Summary of aspects ( With graphic image explanation )

Day 4：list and tuplel Of 13 Classic use cases

Day 5：dict and set Basic operation 、 Dictionary view, etc 6 Detailed explanation and summary of various aspects with graphic explanation

Day 6：dict and set Of 15 Classic use cases

Day 7： Mathematical operations 、 Logical operation and base conversion related 16 Built in functions

Day 8：16 Type functions and 10 Built in function large scale points related to class objects

Day 9：Python String and regular introduction summary

Day 10：Python File operations 11 Summary of cases

Day 11：Python Time module uses logical big board point

Python Construction of actual combat environment

Day 12：Python Summary of four common development environments

Day 13：Python Installation package common problems and solutions , Through two practical cases

Day 14： Five minutes to get started 7 individual Web、 Reptiles 、 Packaging tools Pyinstaller Equal package introduction and introduction case summary

Day 15： Five minutes to get started 8 Data analysis 、 Machine learning and deep learning package and framework and introduction case summary

Day 16：Pyinstaller Packaging process details

Python Advanced

Day 17：Python List generation efficient 12 A case

Day 18：Python Equality comparison between objects is,in,id,== And so on

Day 19：yield Keyword and generator usage four aspects summary and three examples ,nonlocal Key words and global Keyword Usage Summary

Day 20： Higher order function 、 iterator 、 Decorator, etc 20 Built in function big board point

Day 21：Python Apply three regular cases and recommend a regular verification tool

Day 22：Python Multithreading use logic easy to understand summary

Day 23：Python Summary of efficient memory saving methods ( Further improve yield usage )

Day 24：Python The most underrated Library collections Use summary

Day 25：Python Five types of arguments to a function ,inspect Module view parameter type and parameter assignment rule summary

Day 26：Python Functional programming summary , Including closures ,nonlocal Summary of the use of keywords

Day 27：Python The essence of decorator , Combined with three cases of decorators

Day 28：Python common 12 A collection of pits

Python Data analysis

Day 29：NumP Getting started using logic efficiently , Master these five functions

Day 30：NumPy Advanced and efficient use logic , Master these five functions

Day 31：NumPy Interpretation and application of broadcasting mechanism rules of

Day 32：Pandas Five kinds of questions about reading and writing documents and 38 Summary of parameters

Day 33：Pandas Stronger bracket operation ,iterrows, itertuples and merge Comparative analysis of processing speed , Peculiar set_index,reset_index,reindex operation

Day 34：Pandas The pivoting function 4 Summary of the use of large functions

Day 35：Pandas There are two ways to split data , Convert to dummy variable (dummy) Two methods of , A summary of four different ways to connect two tables

Day 36： Develop common exception summary ：Unhashable Type, Reading files is the most common 4 Exceptions ,SettingWithCopyWarning

Data analysis practice

Day 37： The drawing is amazing Pyecharts A quick and detailed summary of the methods on hand , from Charts and Options Start with two modules

Day 38：Matplotlib Drawing principle summary , Three methods of drawing multiple graphs are summarized ,12 A complete code analysis and animation method summary of the commonly used graph

Day 39： be based on Kaggle Movie Review datasets Pandas Data analysis practice - Data preprocessing stage

Day 40： be based on Kaggle Movie Review datasets Pandas Data analysis practice - Dig out comedy Top50 The list

Day 41：PyQt Make GUI actual combat ： Learn to use by making small and beautiful calculators PyQt

Basic algorithm

Day 42： About the entry algorithm 、 Machine learning and deep learning are some of my thoughts

Day 43： Eight sorting algorithm principle summary and Python Complete code implementation

Day 44： Dynamic programming algorithm and case summary

Day 45： Interviews often test Leetcode Algorithm analysis and summary

Machine learning algorithm

Day 46： Necessary statistical knowledge ： probability , expect , variance , Standard deviation , covariance , The correlation coefficient ,t test ,F test , Chi square test

Day 47： Basic mathematics knowledge for machine learning ： The most commonly used derivation formula , Matrix eigenvalue decomposition, etc

Day 48： Concepts that machine learning has to know ： sample space , Eigenvector , dimension , Generalization ability , Inductive preference, etc

Day 49： Machine learning 9 A common probability distribution

Day 50：OLS Linear regression practice part 1 ： A detailed introduction to the regression principle of machine learning , Including assumptions and principles , Gradient descent for weight

Day 51：OLS Linear regression practice 2 ： The implementation of linear regression algorithm without packet transfer by handwriting

Day 52： Analysis and compilation of Bayesian classification cases

Day 53： Practical application of Bayesian algorithm ： Implement word spell corrector

Day 54： Analysis and solution summary of clustering principle of Gaussian mixture model

Day 55： Cluster model practice ： Multi dimension data clustering case without packet transfer

Day 56： Machine learning commonly used clustering algorithm includes the principle and use considerations

Day 57： Machine learning dimension reduction algorithm PCA Principle derivation and example analysis

Day 58：Kaggle Machine learning classification task case practice

Experience sharing

Day 59： Doctor of famous universities in the United States 、AI Experts Alicia How to learn mathematics 、 machine learning 、 Summary of data analysis

Day 60： Column summary and my past 5 Years of algorithm experience sharing

Suits the crowd

1. Python Language lover
2. Python Advanced language
3. Python Data analysis enthusiasts
4. Programmers want to get started with algorithms
5. Introduction to machine learning algorithm
6. Advanced machine learning algorithm
7. Python And AI enthusiasts

Author's brief introduction

author 1：zglg,5 Years working experience in algorithm development , Senior algorithm engineer of famous Internet company , Created Python Case study GitHub One month in the library star Quantity from 0 To 1700+, By AI Quantum report of authoritative media .

author 2：Alicia, Ph.D. in mathematics from a famous American school , HP Senior Data Analyst , Currently studying in the top universities in the United States AI Professional postdoctoral , Rich experience in work and scientific research .

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