Useful websites

Python List Methods

Don't overwhelm to learn Git,🙌

Git is only this much👇😇

1.Core:
• git init
• git clone
• git add
• git commit
• git status
• git diff
• git checkout
• git reset
• git log
• git show
• git tag
• git push
• git pull

2.Branching:
• git branch
• git checkout -b
• git merge
• git rebase
• git branch --set-upstream-to
• git branch --unset-upstream
• git cherry-pick

3.Merging:
• git merge
• git rebase

4.Stashing:
• git stash
• git stash pop
• git stash list
• git stash apply
• git stash drop

5.Remotes:
• git remote
• git remote add
• git remote remove
• git fetch
• git pull
• git push
• git clone --mirror

6.Configuration:
• git config
• git global config
• git reset config

7. Plumbing:
• git cat-file
• git checkout-index
• git commit-tree
• git diff-tree
• git for-each-ref
• git hash-object
• git ls-files
• git ls-remote
• git merge-tree
• git read-tree
• git rev-parse
• git show-branch
• git show-ref
• git symbolic-ref
• git tag --list
• git update-ref

8.Porcelain:
• git blame
• git bisect
• git checkout
• git commit
• git diff
• git fetch
• git grep
• git log
• git merge
• git push
• git rebase
• git reset
• git show
• git tag

9.Alias:
• git config --global alias.<alias> <command>

10.Hook:
• git config --local core.hooksPath <path>

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Python vs C++ vs Java

Important AI Terms Explained

Some useful PYTHON libraries for data science

NumPy stands for Numerical Python. The most powerful feature of NumPy is n-dimensional array. This library also contains basic linear algebra functions, Fourier transforms,  advanced random number capabilities and tools for integration with other low level languages like Fortran, C and C++

SciPy stands for Scientific Python. SciPy is built on NumPy. It is one of the most useful library for variety of high level science and engineering modules like discrete Fourier transform, Linear Algebra, Optimization and Sparse matrices.

Matplotlib for plotting vast variety of graphs, starting from histograms to line plots to heat plots.. You can use Pylab feature in ipython notebook (ipython notebook –pylab = inline) to use these plotting features inline. If you ignore the inline option, then pylab converts ipython environment to an environment, very similar to Matlab. You can also use Latex commands to add math to your plot.

Pandas for structured data operations and manipulations. It is extensively used for data munging and preparation. Pandas were added relatively recently to Python and have been instrumental in boosting Python’s usage in data scientist community.

Scikit Learn for machine learning. Built on NumPy, SciPy and matplotlib, this library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction.

Statsmodels for statistical modeling. Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.

Seaborn for statistical data visualization. Seaborn is a library for making attractive and informative statistical graphics in Python. It is based on matplotlib. Seaborn aims to make visualization a central part of exploring and understanding data.

Bokeh for creating interactive plots, dashboards and data applications on modern web-browsers. It empowers the user to generate elegant and concise graphics in the style of D3.js. Moreover, it has the capability of high-performance interactivity over very large or streaming datasets.

Blaze for extending the capability of Numpy and Pandas to distributed and streaming datasets. It can be used to access data from a multitude of sources including Bcolz, MongoDB, SQLAlchemy, Apache Spark, PyTables, etc. Together with Bokeh, Blaze can act as a very powerful tool for creating effective visualizations and dashboards on huge chunks of data.

Scrapy for web crawling. It is a very useful framework for getting specific patterns of data. It has the capability to start at a website home url and then dig through web-pages within the website to gather information.

SymPy for symbolic computation. It has wide-ranging capabilities from basic symbolic arithmetic to calculus, algebra, discrete mathematics and quantum physics. Another useful feature is the capability of formatting the result of the computations as LaTeX code.

Requests for accessing the web. It works similar to the the standard python library urllib2 but is much easier to code. You will find subtle differences with urllib2 but for beginners, Requests might be more convenient.

Additional libraries, you might need:

os for Operating system and file operations

networkx and igraph for graph based data manipulations

regular expressions for finding patterns in text data

BeautifulSoup for scrapping web. It is inferior to Scrapy as it will extract information from just a single webpage in a run.

Java for Everything: ☕

Java + Spring = Enterprise Applications

Java + Hibernate = Object-Relational Mapping

Java + Android = Mobile App Development

Java + Swing = Desktop GUI Applications

Java + JavaFX = Modern GUI Applications

Java + JUnit = Unit Testing

Java + Maven = Project Management

Java + Jenkins = Continuous Integration

Java + Apache Kafka = Stream Processing

Java + Apache Hadoop = Big Data Processing

Java + Microservices = Scalable Services

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All the best 👍👍

In 1994, people told me programming was for nerds and that I should become a doctor or a lawyer instead.

10 years later, they told me that someone from India would take my job for $5/hour.

Then, no code was going to doom my career.

In 2021, Codex, then Copilot, then ChatGPT, then Devin, then OpenAI o1...

People keep yelling that "Programming is Dead," and yet the demand for good Software Engineers has never been higher.

Stop listening to midwit people. Learn to build good software, and you'll be okay. (Credits: unknown)

Python Roadmap

Master C programming in 30 days with free resources

Week 1: Basics
1. Days 1-3: Learn the basics of C syntax, data types, and variables.
2. Days 4-7: Study control structures like loops (for, while) and conditional statements (if, switch).

Week 2: Functions and Arrays
3. Days 8-10: Understand functions, how to create them, and pass parameters.
4. Days 11-14: Dive into arrays and how to manipulate them.

Week 3: Pointers and Memory Management
5. Days 15-17: Learn about pointers and their role in C programming.
6. Days 18-21: Study memory management, dynamic memory allocation, and deallocation (malloc, free).

Week 4: File Handling and Advanced Topics
7. Days 22-24: Explore file handling and I/O operations in C.
8. Days 25-28: Learn about more advanced topics like structures, unions, and advanced data structures.
9. Days 29-30: Practice and review what you've learned. Work on small projects to apply your knowledge.

Throughout the 30 days, make sure to:
- Code every day to reinforce your learning.
- Use online resources, tutorials, and textbooks.
- Join C programming communities and forums for help and discussions.
- Solve coding challenges and exercises to test your skills (e.g., HackerRank, LeetCode).
- Document your progress and make notes.

Free Resources to learn C Programming
👇👇

Introduction to C Programming

CS50 Course by Harvard

Master the basics of C Programming

C Programming Project

Let Us C Free Book

Free Interactive C Tutorial

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Software Engineer: C++ C# Java, Python, JavaScript

Web Dev: HTML, CSS, JavaScript, NodeJS

Game Dev: Unity, Unreal, Java

App Dev: Flutter, Objective C, Java, Swift, Kotlin, React

Cyber Security: Python, Linux, Networking

AI & Data Science - Julia, Haskell

𝗛𝗼𝘄 𝘁𝗼 𝗰𝗼𝗱𝗲 𝘄𝗶𝘁𝗵 𝗚𝗶𝘁𝗛𝘂𝗯 𝗖𝗼𝗽𝗶𝗹𝗼𝘁?

A recent study by GitHub and Microsoft discovered that AI now authors 46% of new code. They also found that overall developer productivity surged by 55%, leading to more efficient coding processes. When we talk about AI-powered coding, we mainly talk about GitHub Copilot.

But 𝗵𝗼𝘄 𝗚𝗶𝘁𝗛𝘂𝗯 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝘄𝗼𝗿𝗸𝘀?

The process goes in the following steps:

𝟭. 𝗦𝗲𝗰𝘂𝗿𝗲 𝗽𝗿𝗼𝗺𝗽𝘁 𝘁𝗿𝗮𝗻𝘀𝗺𝗶𝘀𝘀𝗶𝗼𝗻: Your prompts are securely sent to Copilot, ensuring data privacy.

𝟮. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴: Copilot analyzes the code around your cursor, the file type, and other open files to offer relevant suggestions.

𝟯. 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 𝗳𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴: It filters out personal data and inappropriate content, focusing solely on generating helpful code.

𝟰. 𝗖𝗼𝗱𝗲 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: Based on the intent identified in your prompts, Copilot crafts code suggestions that align with your coding style and project standards.

𝟱. 𝗨𝘀𝗲𝗿 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻: Here, we can decide whether to use, tweak, or reject Copilot's suggestions.

𝟲. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽: Copilot learns from your interactions, improving its suggestions. Every time you tweak or reject its ideas, he knows from it. It employs techniques like zero-shot (asking without examples), one-shot (asking with an example), and few-shot learning (providing multiple examples) to adapt to our instructions, whether you provide examples or not.

𝟳. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗵𝗶𝘀𝘁𝗼𝗿𝘆 𝗿𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻: It remembers past prompts and interactions, making future suggestions more accurate.