Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books

Deleting in next few minutes

Free Resources only for Indian Users 👇👇 https://chat.whatsapp.com/KDNWxWYJgC50nC18TsJSBh

Europe is Elon Musk’s next target — and he is already making moves Having successfully accomplished getting his candidate into the White House, Elon Musk has set his sights on Europe. In a series of posts on his platform X in recent weeks, the billionaire Trump supporter, took shots at Germany and the United Kingdom, criticizing the respective governments, questioning their laws and their economic viability, reports Bloomberg. During the US presidential election, Great Britain and Germany openly sided with the Democrats. Now Elon Musk is mocking the two countries, criticizing their ruling political elites. The consistent failures of the German and British governments is becoming apparent to an increasing number of political analysts. They insist that it was mismanagement that caused the large-scale crises in these once-great countries. #Musk #Germany #Britishgovernments 🇪🇺 Keep up with the latest Star Union News  🖥

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 Join @free4unow_backup for more free courses ENJOY LEARNING 👍👍

𝟱 𝗕𝗲𝘀𝘁 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗗𝗼 𝗜𝗻 𝟮𝟬𝟮𝟱😍  Kickstart 2025 with these 5 free courses that can elevate your skills and open doors to new opportunities! The best part? They’re absolutely free! Invest in yourself and make 2025 your most productive year yet. 𝗟𝗶𝗻𝗸 👇:-    https://bit.ly/49uYAG1   Enroll For FREE & Get Certified 🎓

Python Roadmap

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)

𝗧𝗼𝗽 𝗠𝗡𝗖𝘀 𝗞𝗣𝗠𝗚 , 𝗦&𝗣 𝗚𝗹𝗼𝗯𝗮𝗹 & 𝗣𝘄𝗰 𝗵𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀😍 Openings :- 50+ Office Location :- Hyderabad/Bangalore Expected Salary:- 6 To 15LPA KPMG:- https://bit.ly/4ja8QIo S&P Global :- https://bit.ly/4acOWbp Pwc :- https://bit.ly/40qapub Apply before the link expires

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 Best Programming Resources: https://topmate.io/coding/886839 All the best 👍👍

𝐌𝐚𝐬𝐭𝐞𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐚𝐧𝐝 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐓𝐡𝐢𝐬 𝐅𝐫𝐞𝐞 𝐂𝐨𝐮𝐫𝐬𝐞😍 This free course will help you build a solid foundation in Excel for analyzing data and creating impactful visuals. 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4j4TsN8 ✅️Don’t Wait — Start Learning Today!

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.

❗️ WITH LISA YOU WILL START EARNING MONEY Lisa will leave a link with free entry to a channel that draws money every day. Each subscriber gets between $100 and $5,000. 👉🏻CLICK HERE TO JOIN THE CHANNEL 👈🏻 👉🏻CLICK HERE TO JOIN THE CHANNEL!👈🏻 👉🏻CLICK HERE TO JOIN THE CHANNEL 👈🏻 🚨FREE FOR THE FIRST 500 SUBSCRIBERS ONLY!

Important AI Terms Explained

𝐀𝐦𝐚𝐳𝐨𝐧 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 😍 Learn AI for free with Amazon's incredible courses! These courses are perfect to upskill in AI and kickstart your journey in this revolutionary field. 𝐋𝐢𝐧𝐤 👇:- https://bit.ly/3CUBpZw Don’t miss out—enroll today and unlock new career opportunities! 💻📈

List Comprehension in Python

𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐅𝐑𝐄𝐄 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬!🚀💻 Supercharge your career with 5 FREE Microsoft certification courses designed to boost your data analytics skills! 𝐄𝐧𝐫𝐨𝐥𝐥 𝐅𝐨𝐫 𝐅𝐑𝐄𝐄👇 :- https://bit.ly/3Vlixcq - Earn certifications to showcase your skills Don’t wait—start your journey to success today! ✨

Python vs C++ vs Java

𝐅𝐑𝐄𝐄 𝐎𝐧𝐥𝐢𝐧𝐞 𝐌𝐚𝐬𝐭𝐞𝐫𝐜𝐥𝐚𝐬𝐬 𝐎𝐧 𝐀𝐈/𝐌𝐋😍 Kickstart a rewarding Artificial Intelligence & Machine Learning career Roadmap to Become a successful AI & ML engineer!   Eligibility :- Students ,Freshers & Working Professionals  𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐅𝐨𝐫 𝐅𝐑𝐄𝐄 👇:-  https://bit.ly/3DC6MbF (Limited Slots ..HurryUp🏃‍♂️ )  𝐃𝐚𝐭𝐞 & 𝐓𝐢𝐦𝐞:-  Jan 09, 2025, at 7 PM

Python Roadmap for 2025: Complete Guide 1. Python Fundamentals 1.1 Variables, constants, and comments. 1.2 Data types: int, float, str, bool, complex. 1.3 Input and output (input(), print(), formatted strings). 1.4 Python syntax: Indentation and code structure. 2. Operators 2.1 Arithmetic: +, -, *, /, %, //, **. 2.2 Comparison: ==, !=, <, >, <=, >=. 2.3 Logical: and, or, not. 2.4 Bitwise: &, |, ^, ~, <<, >>. 2.5 Identity: is, is not. 2.6 Membership: in, not in. 3. Control Flow 3.1 Conditional statements: if, elif, else. 3.2 Loops: for, while. 3.3 Loop control: break, continue, pass. 4. Data Structures 4.1 Lists: Indexing, slicing, methods (append(), pop(), sort(), etc.). 4.2 Tuples: Immutability, packing/unpacking. 4.3 Dictionaries: Key-value pairs, methods (get(), items(), etc.). 4.4 Sets: Unique elements, set operations (union, intersection). 4.5 Strings: Immutability, methods (split(), strip(), replace()). 5. Functions 5.1 Defining functions with def. 5.2 Arguments: Positional, keyword, default, *args, **kwargs. 5.3 Anonymous functions (lambda). 5.4 Recursion. 6. Modules and Packages 6.1 Importing: import, from ... import. 6.2 Standard libraries: math, os, sys, random, datetime, time. 6.3 Installing external libraries with pip. 7. File Handling 7.1 Open and close files (open(), close()). 7.2 Read and write (read(), write(), readlines()). 7.3 Using context managers (with open(...)). 8. Object-Oriented Programming (OOP) 8.1 Classes and objects. 8.2 Methods and attributes. 8.3 Constructor (init). 8.4 Inheritance, polymorphism, encapsulation. 8.5 Special methods (str, repr, etc.). 9. Error and Exception Handling 9.1 try, except, else, finally. 9.2 Raising exceptions (raise). 9.3 Custom exceptions. 10. Comprehensions 10.1 List comprehensions. 10.2 Dictionary comprehensions. 10.3 Set comprehensions. 11. Iterators and Generators 11.1 Creating iterators using iter() and next(). 11.2 Generators with yield. 11.3 Generator expressions. 12. Decorators and Closures 12.1 Functions as first-class citizens. 12.2 Nested functions. 12.3 Closures. 12.4 Creating and applying decorators. 13. Advanced Topics 13.1 Context managers (with statement). 13.2 Multithreading and multiprocessing. 13.3 Asynchronous programming with async and await. 13.4 Python's Global Interpreter Lock (GIL). 14. Python Internals 14.1 Mutable vs immutable objects. 14.2 Memory management and garbage collection. 14.3 Python's name == "main" mechanism. 15. Libraries and Frameworks 15.1 Data Science: NumPy, Pandas, Matplotlib, Seaborn. 15.2 Web Development: Flask, Django, FastAPI. 15.3 Testing: unittest, pytest. 15.4 APIs: requests, http.client. 15.5 Automation: selenium, os. 15.6 Machine Learning: scikit-learn, TensorFlow, PyTorch. 16. Tools and Best Practices 16.1 Debugging: pdb, breakpoints. 16.2 Code style: PEP 8 guidelines. 16.3 Virtual environments: venv. 16.4 Version control: Git + GitHub. 📕 Python Interview 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: https://topmate.io/analyst/907371 📘 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 : https://topmate.io/coding/914624 📙 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D Join What's app channel for jobs updates: https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226