Coding Free Books | Python | AI

Important topics of Object Oriented Programming System 1. Classes and Objects: -> Basics of defining classes and creating objects. -> Class members: attributes (properties) and methods (functions). 2. Inheritance: -> Creating a new class by inheriting properties and methods from an existing class. -> Superclasses (base classes) and subclasses (derived classes). 3. Polymorphism: -> Ability to take multiple forms. -> Method overriding and method overloading. 4. Encapsulation: -> Hiding the internal details of a class and providing a controlled interface. -> Access modifiers: public, private, protected. 5. Abstraction: -> Simplifying complex reality by modeling classes based on real-world entities. -> Abstract classes and interfaces. 6. Constructors and Destructors: -> Special methods for initializing and cleaning up objects. -> Constructor overloading. 7. Method Access and Modifiers: -> Public, private, protected, and package-private access modifiers. -> Static methods and variables. A few advanced topics :- Composition and Aggregation: Combining objects to create more complex structures. Has-a and Is-a relationships. Object Relationships: Association, aggregation, and composition. One-to-one, one-to-many, and many-to-many relationships. Interfaces: Defining contracts that classes must adhere to. Multiple interface implementation. Polymorphic Behavior: Achieving flexibility through polymorphism. Method overriding and dynamic method binding. Inheritance vs. Composition: Comparing and choosing between inheritance and object composition. Design Patterns: Common solutions to recurring design problems. Examples: Singleton, Factory, Observer, etc. Exception Handling: Handling errors and exceptions gracefully in OOP. Try-catch blocks. Object Serialization: Converting objects into a format suitable for storage or transmission. Reading and writing objects to/from files. Garbage Collection: Automatic memory management to reclaim unused memory. Mark and sweep, reference counting, and generations. UML (Unified Modeling Language): A visual language for modeling software systems. Class diagrams, sequence diagrams, and use cases. Method Overriding vs. Method Overloading: Understanding the differences between these two concepts. Abstract Classes vs. Interfaces: Comparing and contrasting abstract classes and interfaces in OOP. Encapsulation Benefits: Discussing the advantages of encapsulation, such as data protection and code organization. P.S - These are just the name of topics which you should be aware of. You can get enough articles on every topic just on a Google search.

𝗧𝗖𝗦 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍 Want to kickstart your career in Data Analytics but don’t know where to begin?👨‍💻 TCS has your back with a completely FREE course designed just for beginners✅ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4jNMoEg Just pure, job-ready learning📍

Data science is a multidisciplinary field that combines techniques from statistics, computer science, and domain-specific knowledge to extract insights and knowledge from data. Here are some essential concepts in data science: 1. Data Collection: The process of gathering data from various sources, such as databases, files, sensors, and APIs. 2. Data Cleaning: The process of identifying and correcting errors, missing values, and inconsistencies in the data. 3. Data Exploration: The process of summarizing and visualizing the data to understand its characteristics and relationships. 4. Data Preprocessing: The process of transforming and preparing the data for analysis, including feature selection, normalization, and encoding. 5. Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data. 6. Statistical Analysis: The use of statistical methods to analyze and interpret data, including hypothesis testing, regression analysis, and clustering. 7. Data Visualization: The graphical representation of data to communicate insights and findings effectively. 8. Model Evaluation: The process of assessing the performance of a predictive model using metrics such as accuracy, precision, recall, and F1 score. 9. Feature Engineering: The process of creating new features or transforming existing features to improve the performance of machine learning models. 10. Big Data: The term used to describe large and complex datasets that require specialized tools and techniques for analysis. These concepts are foundational to the practice of data science and are essential for extracting valuable insights from data. Join for more: https://t.me/datasciencefun ENJOY LEARNING 👍👍

𝗥𝗲𝗮𝗱𝘆 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗶𝗻 𝗝𝘂𝘀𝘁 𝟯 𝗠𝗼𝗻𝘁𝗵𝘀?😍 📍Feeling lost on where to start your data analytics journey? We’ve got you. This 3-month plan will take you from total beginner to job-ready, even if you’re from a non-tech background. 🎯 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3Ev1cJ9 Save this. Share this. Start today✅️

Types of API ✅

𝟱 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗵𝗮𝘁’𝗹𝗹 𝗠𝗮𝗸𝗲 𝗦𝗤𝗟 𝗙𝗶𝗻𝗮𝗹𝗹𝘆 𝗖𝗹𝗶𝗰𝗸.😍 SQL seems tough, right? 😩 These 5 FREE SQL resources will take you from beginner to advanced without boring theory dumps or confusion.📊 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/3GtntaC Master it with ease. 💡

𝗡𝗼 𝗗𝗲𝗴𝗿𝗲𝗲? 𝗡𝗼 𝗣𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘀𝗲 𝟰 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗖𝗮𝗻 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗝𝗼𝗯😍 Dreaming of a career in data but don’t have a degree? You don’t need one. What you do need are the right skills🔗 These 4 free/affordable certifications can get you there. 💻✨ 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/4ioaJ2p Let’s get you certified and hired!✅️

𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 𝗮𝘁 𝗚𝗼𝗼𝗴𝗹𝗲? 𝗧𝗵𝗲𝘀𝗲 𝟰 𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗪𝗶𝗹𝗹 𝗛𝗲𝗹𝗽 𝗬𝗼𝘂 𝗚𝗲𝘁 𝗧𝗵𝗲𝗿𝗲😍 Dreaming of working at Google but not sure where to even begin?📍 Start with these FREE insider resources—from building a resume that stands out to mastering the Google interview process. 🎯 𝐋𝐢𝐧𝐤👇:- https://pdlink.in/441GCKF Because if someone else can do it, so can you. Why not you? Why not now?✅️

How to be a Prompt Engineer 101 The shortest and most comprehensive guide 1. start with an explanation Make a description and character situation at the beginning of the Prompt Error example: Please help me read the following code: {your input here} Correct example: Now let's play the role, you are a senior information security engineer, I will give you a piece of code, please help me read the code and point out where there may be security vulnerable. Text: """ {your input here} """ 2. Prompt to describe the situation In the prompt, it is necessary to describe the context, result, length, format and style as much as possible Error example: Write a short story for kids Correct example: Write a funny soccer story for kids that teaches the kid that persistence is the key for success in the style of Rowling. 3. gives output in the format If you are doing data analysis, please give the input template of the format Error example: Extract house pricing data from the following text. Text: """ {your text containing pricing data} """ Correct example: Extract house pricing data from the following text. Desired format: """ House 1 | $1,000,000 | 100 sqm House 2 | $500,000 | 90 sqm ... (and so on) """ Text: """ {your text containing pricing data} """ 4. Add some example questions and answers Sometimes adding some question and answer examples can make GPT more intelligent Correct example: Extract brand names from the texts below. Text 1: Finxter and YouTube are tech companies. Google is too. Brand names 2: Finxter, YouTube, Google ### Text 2: If you like tech, you'll love Finxter! Brand names 2: Finxter ### Text 3: {your text here} Brand names 3: The question and answer example is also a standard template example in fine-tune 5. Simplify the sentence and clarify the purpose Keep your words as short as possible and don't say useless content Error example: ChatGPT, write a sales page for my company selling sand in the desert, please write only a few sentences, nothing long and complex Correct example: Write a 5-sentence sales page, sell sand in the desert. 6. Good at using introductory words Error example: Write a Python function that plots my net worth over 10 years for different inputs on the initial investment and a given ROI Correct example: # Python function that plots net worth over 10 # years for different inputs on the initial # investment and a given ROI import matplotlib def plot_net_worth(initial, roi):

🛡️ Mafia OTP Bot – The Ultimate OTP Bypass Solution ✅ Try Before Buy – 2 FREE CALLS to test before you subscribe 🔐 Premium Features:  • ✅ Accept or Reject OTP in-call   • 🏦 Works with Banks, 📧 Emails, 🔐 Wallets, 🔑 2FA   • 🎙️ Dual Voice Options (Male / Female)   • 🤖 Smart Human Detection  • 🎧 Call Recording Enabled   • 🌍 Supports All Languages   • ⚡ Ultra Fast – No need to restart the call   • 💸 Earn 33% Commission for every referral 🤖 Bot Link: CLICK HERE TO START BOT

Ad 👇👇

𝗣𝗼𝘄𝗲𝗿𝗕𝗜 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗿𝗼𝗺 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁😍 ✅ Beginner-friendly ✅ Straight from Microsoft ✅ And yes… a badge for that resume flex Perfect for beginners, job seekers, & Working Professionals 𝐋𝐢𝐧𝐤 👇:- https://pdlink.in/4iq8QlM Enroll for FREE & Get Certified 🎓

𝗪𝗲𝗯 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍 Want to master web development? These free certification courses will help you build real-world full-stack skills: ✅ Web Design 🎨 ✅ JavaScript ⚡  ✅ Front-End Libraries 📚 ✅ Back-End & APIs 🌐  ✅ Databases 💾  💡 Start learning today and build your career for FREE! 🚀 𝐋𝐢𝐧𝐤 👇:- https://pdlink.in/4bqbQwB Enroll for FREE & Get Certified 🎓

𝗪𝗼𝗿𝗸 𝗙𝗿𝗼𝗺 𝗔𝗻𝘆𝘄𝗵𝗲𝗿𝗲 | 𝗥𝗲𝗺𝗼𝘁𝗲 𝗝𝗼𝗯𝘀 😍 Top 5 Platforms to Find High-Paying Remote Tech Jobs Whether you’re a coder, data analyst, content strategist, or UI designer… your remote dream job is a click away. ✨ 𝐋𝐢𝐧𝐤 👇:- https://pdlink.in/3XZYqCf Get Your Dream Remote Job 🎓