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Python from scratch by University of Waterloo

0. Introduction
1. First steps
2. Built-in functions
3. Storing and using information
4. Creating functions
5. Booleans
6. Branching
7. Building better programs
8. Iteration using while
9. Storing elements in a sequence
10. Iteration using for
11. Bundling information into objects
12. Structuring data
13. Recursion

Link: https://open.cs.uwaterloo.ca/python-from-scratch/

Python Resources: https://t.me/pythonresourcestp

Google, Harvard, and even OpenAI are offering FREE Generative AI courses (no payment required) 🎓

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Basics of AI, neural networks, and deep learning
https://microsoft.github.io/AI-For-Beginners/

3. Introduction to AI with Python (Harvard)
7-week course exploring AI concepts and algorithms
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Hi guys,

I got this query from many people asking if there is any demand for web development, data science, machine learning, cybersecurity or similar fields in the future. Many people who are new to these fields are wondering if AI would replace their jobs or if these fields will still be relevant.

The short answer is yes, there is still a significant demand for these skills, and they are expected to remain relevant for the foreseeable future. Here's a breakdown of each field

1. Web Development With the continuous growth of the internet and the increasing number of online businesses, web development remains a vital skill. The demand for dynamic and responsive websites, as well as web applications, ensures that web developers will always have opportunities.

2. Data Science As companies accumulate more data, the need for skilled data scientists to analyze and interpret this data is growing. Data-driven decision-making is becoming essential for businesses, making data science a highly sought-after field.

3. Machine Learning Machine learning is a subset of AI that involves teaching computers to learn from data. Its applications range from recommendation systems to predictive analytics and autonomous systems. The field is rapidly expanding and is expected to create numerous job opportunities.

4. Cybersecurity With the increasing number of cyber threats and attacks, cybersecurity has become a top priority for organizations. Professionals in this field are crucial for protecting sensitive information and ensuring the security of digital infrastructure.

While AI is indeed advancing and automating many tasks, it is also creating new opportunities and fields of study. AI will likely augment rather than replace professionals in these areas, enabling them to work more efficiently and effectively. Adapting to new technologies and continuously upskilling will be key to staying relevant in the evolving job market.

In conclusion, take an overview of each field and see if that interests you. Pick up a field which you can do for years which will make you an expert in long run. Experts are highly valued & irreplaceable in any field. AI might automate simple tasks, but it can't replace the depth of experience and expertise you bring.

Give your best, leave the rest ✅

A-Z SQL Overview

Link: https://dev.to/henryclapton/a-z-sql-overview-2a7e

Complete Roadmap to become a web developer in two months:

*Week 1-2: Basics of Web Development*
1. HTML & CSS: Learn the fundamentals of building web pages with HTML for structure and CSS for styling.
2. Responsive Design: Understand how to make your websites responsive to different screen sizes using media queries.
3. Basic JavaScript: Start with basic JavaScript concepts like variables, data types, and operators.

*Week 3-4: Intermediate Web Development*
1. DOM Manipulation: Learn how to manipulate the Document Object Model (DOM) with JavaScript to dynamically change website content.
2. Intermediate JavaScript: Dive deeper into JavaScript with concepts like functions, arrays, objects, and control flow.
3. Version Control: Learn Git and GitHub for version control and collaboration.

*Week 5-6: Frontend Development*
1. Frontend Frameworks: Learn a frontend framework like React, Vue.js, or Angular. Focus on one and understand its fundamentals.
2. Package Managers: Learn how to use npm or yarn to manage dependencies for your projects.
3. CSS Preprocessors: Explore tools like Sass or Less to enhance your CSS workflow.

*Week 7-8: Backend Development*
1. Server-side Programming: Learn a backend language like Node.js with Express, Python with Django or Flask, or Ruby on Rails.
2. Databases: Understand basics of database management systems like MongoDB, MySQL, or PostgreSQL.
3. APIs: Learn how to build and consume APIs to connect your frontend and backend.

Additional Tips:
* Practice regularly by building projects. Start with simple ones and gradually increase complexity.
* Utilize online resources like tutorials, documentation, and forums like Stack Overflow and GitHub.
* Network with other developers through online communities and attend webinars or meetups.
* Stay updated with industry trends and best practices by following blogs and podcasts.

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Beginner to Expert Level Web Development Resources: (https://t.me/webdevresourcestp)

Networking for Web Developers (https://www.udacity.com/course/networking-for-web-developers--ud256)

Intro to JavaScript (https://www.udacity.com/course/intro-to-javascript--ud803)

Object-Oriented JavaScript (https://www.udacity.com/course/object-oriented-javascript--ud711)

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🔰 RFCs to understand JSON,JWT,SAML,0Auth indepth 🔰

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How do you start AI and ML ?

Where do you go to learn these skills? What courses are the best?

There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.

What’s more important than how you start is why you start.

Start with why.

Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.

Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.

Got a why? Good. Time for some hard skills.

I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course

You can completed courses from (in order):

Treehouse / youtube( free) - Introduction to Python

Udacity - Deep Learning & AI Nanodegree

fast.ai - Part 1and Part 2

They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.

If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.

AI: https://t.me/airesourcestp

ML: https://t.me/mlresourcestp

Like for more ❤️

All the best 👍👍

Essential Tools & Programming Languages for Software Developers

👉 Integrated Development Environments (IDEs):
- Visual Studio Code: A lightweight but powerful source code editor that supports various programming languages and extensions.
- IntelliJ IDEA: A popular IDE for Java development, also supporting other languages through plugins.
- Eclipse: Another widely used IDE for Java, with extensive plugin support for other languages.

👉 Version Control Systems:
- Git: A distributed version control system that allows developers to track changes in their codebase, collaborate with others, and manage project history. GitHub, GitLab, and Bitbucket are popular platforms that use Git.

👉 Programming Languages:
- JavaScript: Essential for web development, with frameworks like React, Angular, and Vue.js for front-end development and Node.js for server-side programming.
- Python: Known for its simplicity and versatility, used in web development (Django, Flask), data science (NumPy, Pandas), and automation.
- Java: Widely used for building enterprise-scale applications, Android app development, and backend systems.
- C#: A language developed by Microsoft, primarily used for building Windows applications and games using the Unity engine.
- C++: Known for its performance, used in system/software development, game development, and applications requiring real-time processing.
- Ruby: Known for its simplicity and productivity, often used in web development with the Ruby on Rails framework.

👉 Web Development Frameworks:
- React: A JavaScript library for building user interfaces, particularly single-page applications.
- Angular: A TypeScript-based framework for building dynamic web applications.
- Django: A high-level Python web framework that encourages rapid development and clean, pragmatic design.
- Spring: A comprehensive framework for Java that provides infrastructure support for developing Java applications.

👉 Database Management Systems:
- MySQL: An open-source relational database management system.
- PostgreSQL: An open-source object-relational database system with a strong emphasis on extensibility and standards compliance.
- MongoDB: A NoSQL database that uses a flexible, JSON-like format for storing data.

👉 Containerization and Orchestration:
- Docker: A platform that allows developers to package applications into containers, ensuring consistency across multiple environments.
- Kubernetes: An open-source system for automating deployment, scaling, and management of containerized applications.

👉 Cloud Platforms:
- Amazon Web Services (AWS): A comprehensive cloud platform offering a wide range of services, including computing power, storage, and databases.
- Microsoft Azure: A cloud computing service created by Microsoft for building, testing, deploying, and managing applications.
- Google Cloud Platform (GCP): A suite of cloud computing services provided by Google.

👉 CI/CD Tools:
- Jenkins: An open-source automation server that helps automate the parts of software development related to building, testing, and deploying.
- Travis CI: A continuous integration service used to build and test software projects hosted on GitHub.

👉 Project Management and Collaboration:
- Jira: A tool developed by Atlassian for bug tracking, issue tracking, and project management.
- Trello: A visual tool for organizing tasks and projects into boards.

Programming & Data Analytics Resources

Best Programming Resources

Like for more ❤️

Join for more free courses

ENJOY LEARNING👍👍

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If I were to start Data Analytics in 2025 💫🚀

❯ Python
http://cs50.harvard.edu/python/2022/

https://www.freecodecamp.org/learn/data-analysis-with-python/

https://t.me/pythonresourcestp

❯ SQL
http://online.stanford.edu/courses/soe-ydatabases0005-databases-relational-databases-and-sql

https://www.freecodecamp.org/learn/relational-database/

https://topmate.io/learning_resources/1456762

https://tinyurl.com/4rwc9v5a

❯ Excel
https://excel-practice-online.com/

https://t.me/dataanalysisresourcestp/35

❯ Power BI
https://www.freecodecamp.org/learn/data-visualization/

https://t.me/dataanalysisresourcestp/7

https://www.workout-wednesday.com/power-bi-challenges/

❯ Tableau
https://t.me/dataanalysisresourcestp/30

https://www.tableau.com/learn/training

❯ Mathematics (incl. Statistics)
ocw.mit.edu/search/?d=Mathematics&s=department_course_numbers.sort_coursenum

http://www.sherrytowers.com/cowan_statistical_data_analysis.pdf

❯ Data Science
https://t.me/datascienceresourcestp/25

cognitiveclass.ai/courses/data-science-101

http://kaggle.com/learn

https://t.me/datascienceresourcestp/25

❯ Machine Learning
http://developers.google.com/machine-learning/crash-course

https://www.freecodecamp.org/learn/machine-learning-with-python/

❯ Artificial Intelligence
https://t.me/airesourcestp

https://udacity.com/course/intro-to-artificial-intelligence--cs271

introtodeeplearning.com

https://t.me/mlresourcestp

❯ Data Engineering
https://tinyurl.com/fadtk827

https://t.me/datascienceresourcestp/23

Join for more free resources

Like for more ❤️

ENJOY LEARNING👍👍

Artificial Intelligence isn't easy!

It’s the transformative field that enables machines to think, learn, and act autonomously.

To truly excel in Artificial Intelligence, focus on these key areas:

0. Understanding AI Foundations: Learn the core concepts of AI, such as search algorithms, knowledge representation, and logic-based reasoning.

1. Mastering Machine Learning: Deepen your understanding of supervised and unsupervised learning, as well as reinforcement learning for building intelligent systems.

2. Diving into Neural Networks: Understand the architecture and workings of neural networks, including deep learning models, convolutional networks (CNNs), and recurrent networks (RNNs).

3. Working with Natural Language Processing (NLP): Learn how machines interpret human language for tasks like text generation, translation, and sentiment analysis.

4. Reinforcement Learning and Decision Making: Explore how AI learns through interactions with its environment to optimize actions and outcomes, from gaming to robotics.

5. Developing AI Models: Master tools like TensorFlow, PyTorch, and Keras for building, training, and evaluating machine learning and deep learning models.

6. Ethical AI and Bias: Understand the challenges of fairness, transparency, and ethical considerations when developing AI systems.

7. AI in Computer Vision: Dive into image recognition, object detection, and segmentation techniques for enabling machines to "see" and understand the visual world.

8. AI in Robotics: Learn how AI empowers robots to navigate, interact, and make decisions autonomously in the physical world.

9. Staying Updated with AI Trends: The AI landscape evolves quickly—stay on top of new algorithms, research papers, and applications emerging in the field.

AI is about developing systems that think, learn, and adapt in ways that mimic human intelligence.

💡 Embrace the complexity of building intelligent systems that not only solve problems but also innovate and create.

Free Books and Courses to Learn Artificial Intelligence👇👇

Introduction to AI Free Udacity Course

12 AI Tools to improve your productivity

Peter Flach AI Publications

Introduction to AI for Business Free Udemy Course

Top Platforms for Building Data Science Portfolio

Artificial Intelligence: Foundations of Computational Agents Free Book

Learn Basics about AI Free Udemy Course

Amazing AI Reverse Image Search

By focusing on these skills, you’ll gain a strong understanding of AI concepts and practical skills in Python, machine learning, and neural networks.

Like for more similar content ❤️

Join for more free courses

ENJOY LEARNING 👍👍

10 BEST TREND ANALYSIS AI TOOLS FOR BUSINESSES, MARKETERS AND CONTENT CREATORS ON TECHNOLOGY, FINANCE, CONSUMER BEHAVIOR & e.t.c

1. Trends Critical (https://trendscritical.com/) - Leverages AI to enable rapid trend analysis in under 90 seconds and across over 50 languages. Classifies trends into distinct lifecycle stages.

2. Brandwatch (https://www.brandwatch.com/suite/consumer-intelligence/) - Provides insights from social media, blogs, reviews aggregated across over 1.4 trillion posts. Enables advanced sentiment analysis.

3. Talkwalker (https://www.talkwalker.com/) - Analyzes billions of conversations across various digital channels to understand brand perception, customer behavior, and industry trends.

4. Microsoft Power BI (https://t.me/dataanalysisresourcestp/39) - Identifies trends through visualization and integration with various data sources. Features quick insights cards and real-time data streaming.

5. Qlik Sense (https://www.qlik.com/us/products/qlik-sense) - Facilitates analysis of large datasets using associative database technology. Includes built-in ETL and robust visualization tools.

6. AI Tools & Resources (https://t.me/airesourcestp) - Stay ahead of the curve with this curated Telegram channel. It’s packed with the latest AI tools, resources, and updates to keep you informed and inspired.

7. IBM Cognos Analytics (https://www.ibm.com/products/cognos-analytics) - Offers data preparation, reporting, predictive analytics and an AI assistant. Emphasizes automated insights.

8. Zoho Analytics (https://www.zoho.com/) - Focuses on self-service with intuitive interfaces for forecasting, trend analysis and report generation.

9. Meltwater (https://www.meltwater.com/) - Sources over 500M pieces of content daily for comprehensive media and social media monitoring.

10. TIBCO Spotfire (https://www.spotfire.com/) - Provides advanced visualization and interactive data exploration across different sources. Enables self-service analytics.

AI Tools to 10X your Productivity: https://t.me/airesourcestp/43

Follow this WhatsApp Channel for More Resources
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R

Make Money with Help Of ChatGPT

25 AI Tools to Boost Your Productivity ⭐️

Audio ✔️
1. Lovo.ai
2. Speechify.com
3. Murf.ai
4. Media.io

Website ✔️
1. 10web.io
2. Durable.co
3. Alliai.com
4. Subpage.app

Video ✔️
1. Steve.ai
2. Pictory.ai
3. Deepbrain.io
4. Heygen.com

Research ✔️
1. Paperpal.com
2. Beta.monic.ai
3. Consensus.app
4. Perplexity.ai
5. You.com

Presentations ✔️
1. Beautiful.ai
2. Simplified.com
3. Slidesgo.com
4. Sendsteps.com

Content Creation ✔️
1. Lovo.ai
2. Writesonic.com
3. Jasper.ai
4. Stockimg.ai
5. Copy.ai

60 AI Tools to finish work in Minutes: https://t.me/airesourcestp/35

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C++ Programming Roadmap
|
|-- Fundamentals
| |-- Basics of Programming
| | |-- Introduction to C++
| | |-- Setting Up Development Environment (IDE: Code::Blocks, Visual Studio, etc.)
| | |-- Compiling and Running C++ Programs
| |
| |-- Syntax and Structure
| | |-- Basic Syntax
| | |-- Variables and Data Types
| | |-- Operators (Arithmetic, Relational, Logical, Bitwise)
|
|-- Control Structures
| |-- Conditional Statements
| | |-- If-Else Statements
| | |-- Switch Case
| |
| |-- Loops
| | |-- For Loop
| | |-- While Loop
| | |-- Do-While Loop
| |
| |-- Jump Statements
| | |-- Break, Continue
| | |-- Goto Statement
|
|-- Functions and Scope
| |-- Defining Functions
| | |-- Function Syntax
| | |-- Parameters and Arguments (Pass by Value, Pass by Reference)
| | |-- Return Statement
| |
| |-- Function Overloading
| | |-- Overloading Functions with Different Parameters
| |
| |-- Scope and Lifetime
| | |-- Local and Global Scope
| | |-- Static Variables
|
|-- Object-Oriented Programming (OOP)
| |-- Basics of OOP
| | |-- Classes and Objects
| | |-- Member Functions and Data Members
| |
| |-- Constructors and Destructors
| | |-- Constructor Types (Default, Parameterized, Copy)
| | |-- Destructor Basics
| |
| |-- Inheritance
| | |-- Single and Multiple Inheritance
| | |-- Protected Access Specifier
| | |-- Virtual Base Class
| |
| |-- Polymorphism
| | |-- Function Overriding
| | |-- Virtual Functions and Pure Virtual Functions
| | |-- Abstract Classes
| |
| |-- Encapsulation and Abstraction
| | |-- Access Specifiers (Public, Private, Protected)
| | |-- Getters and Setters
| |
| |-- Operator Overloading
| | |-- Overloading Operators (Arithmetic, Relational, etc.)
| | |-- Friend Functions
|
|-- Advanced C++
| |-- Pointers and Dynamic Memory
| | |-- Pointer Basics
| | |-- Dynamic Memory Allocation (new, delete)
| | |-- Pointer Arithmetic
| |
| |-- References
| | |-- Reference Variables
| | |-- Passing by Reference
| |
| |-- Templates
| | |-- Function Templates
| | |-- Class Templates
| |
| |-- Exception Handling
| | |-- Try-Catch Blocks
| | |-- Throwing Exceptions
| | |-- Standard Exceptions
|
|-- Data Structures
| |-- Arrays and Strings
| | |-- One-Dimensional and Multi-Dimensional Arrays
| | |-- String Handling
| |
| |-- Linked Lists
| | |-- Singly and Doubly Linked Lists
| |
| |-- Stacks and Queues
| | |-- Stack Operations (Push, Pop, Peek)
| | |-- Queue Operations (Enqueue, Dequeue)
| |
| |-- Trees and Graphs
| | |-- Binary Trees, Binary Search Trees
| | |-- Graph Representation and Traversal (DFS, BFS)
|
|-- Standard Template Library (STL)
| |-- Containers
| | |-- Vectors, Lists, Deques
| | |-- Stacks, Queues, Priority Queues
| | |-- Sets, Maps, Unordered Maps
| |
| |-- Iterators
| | |-- Input and Output Iterators
| | |-- Forward, Bidirectional, and Random Access Iterators
| |
| |-- Algorithms
| | |-- Sorting, Searching, and Manipulation
| | |-- Numeric Algorithms
|
|-- File Handling
| |-- Streams and File I/O
| | |-- ifstream, ofstream, fstream
| | |-- Reading and Writing Files
| | |-- Binary File Handling
|
|-- Testing and Debugging
| |-- Debugging Tools
| | |-- gdb (GNU Debugger)
| | |-- Valgrind for Memory Leak Detection
| |
| |-- Unit Testing
| | |-- Google Test (gtest)
| | |-- Writing and Running Tests
|
|-- Deployment and DevOps
| |-- Version Control with Git
| | |-- Integrating C++ Projects with GitHub
| |-- Continuous Integration/Continuous Deployment (CI/CD)
| | |-- Using Jenkins or GitHub
| |
|   |--Free courses
| | |--https://www.udacity.com/course/c-for-programmers--ud210
|   |   |--Microsoft Documentation (https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019)

|   |   |--Udemy Course (https://www.udemy.com/course/introduction-to-algorithms-and-data-structures-in-c/)

Join https://t.me/techpsyche for more free resources

ENJOY LEARNING 👍👍

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Data Analyst Interview Questions
[Python, SQL, PowerBI]

1. Is indentation required in python?
Ans: Indentation is necessary for Python. It specifies a block of code. All code within loops, classes, functions, etc is specified within an indented block. It is usually done using four space characters. If your code is not indented necessarily, it will not execute accurately and will throw errors as well.

2. What are Entities and Relationships?
Ans:
Entity: An entity can be a real-world object that can be easily identifiable. For example, in a college database, students, professors, workers, departments, and projects can be referred to as entities.

Relationships: Relations or links between entities that have something to do with each other. For example – The employee’s table in a company’s database can be associated with the salary table in the same database.

3. What are Aggregate and Scalar functions?
Ans: An aggregate function performs operations on a collection of values to return a single scalar value. Aggregate functions are often used with the GROUP BY and HAVING clauses of the SELECT statement. A scalar function returns a single value based on the input value.

4. What are Custom Visuals in Power BI?
Ans: Custom Visuals are like any other visualizations, generated using Power BI. The only difference is that it develops the custom visuals using a custom SDK. The languages like JQuery and JavaScript are used to create custom visuals in Power BI

Join for More: (https://t.me/pythonresourcestp)

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Research is Your Only Way Out: You can't grow by just listening to someone else's perspective. You gotta dig in and do the work yourself.

Outcome is King: People only care about the end result. Nobody gives a damn if you built something from scratch or used AI – as long as it delivers.

AI is Your Productivity Multiplier (Not Your Replacement… Unless You Let It Be): AI can either replace you or boost your productivity by miles. This isn't new; it's been happening since forever. Think phones replacing letters, better cars replacing old ones—it's evolution, baby!

Now, let's dive a bit deeper:
Forget the "gurus" and their spoon-fed advice. Research is the only path to real growth. Nobody cares if you built your system from scratch or glued it together with AI tools – the outcome is king.

AI isn't here to steal your job; it's here to supercharge your productivity. Think of it like the evolution of communication. Remember those clunky letter-writing days? Replaced by phones, then emails, and now instant messaging. It's a constant cycle. AI is the next upgrade, folks.

Sure, those no-code tools are handy, but they can't create truly bespoke masterpieces. That's where the real opportunity lies: building custom solutions that blow minds.

Your Mission (Should You Choose to Accept It):

This week, ditch the tutorials and dive headfirst into AI fundamentals. Research how this magic works – learn about Machine Learning (ML), Large Language Models (LLMs), Agents, and all the other cool acronyms. This knowledge will be your weapon in the AI revolution.

Let's go out there and conquer the world of AI, together!

Python AI Roadmap: https://t.me/airesourcestp/39

Machine Learling Baby Steps: https://t.me/mlresourcestp/27

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