JavaScript from Beginner to Professional: Learn JavaScript quickly by building fun, interactive, and dynamic web apps, games, and pages

 

Introduction: Why JavaScript, and Why Now?

In the ever-evolving world of web development, one language has remained consistently essential: JavaScript. From creating responsive websites and dynamic user interfaces to powering full-scale web applications, JavaScript is the heartbeat of the modern web.

But for many beginners, learning JavaScript can feel overwhelming. The syntax, the concepts, the way it interacts with HTML and CSS—it can all seem like a tangled mess. What if you could skip the frustration and jump straight into building fun, interactive things from day one?

That’s the promise of the book “JavaScript from Beginner to Professional.” Rather than burying you in theory, this book takes a hands-on, project-based approach that empowers you to learn by doing. You won’t just read about JavaScript—you’ll use it to build real, exciting projects that bring your ideas to life.

Whether you're a complete newcomer to coding or someone looking to finally "get" JavaScript, this book offers a refreshing path to confidence, creativity, and competence in one of the world’s most popular programming languages.

What You’ll Learn

The book is structured to take you from absolute beginner to someone who can confidently build interactive, browser-based applications. It covers everything from the basics of the language to building games and apps using DOM manipulation and real-world logic.

Here’s a breakdown of what you’ll learn:

JavaScript Fundamentals

• Vrrays and objects
• Functions and loops
• Conditional logic (if/else, switch, ternary)
• Theariables, strings, numbers, and booleans
• A basics of debugging and logging

These core building blocks are taught through fun mini-challenges that cement understanding through real coding exercises.

The DOM & Events

You’ll explore how JavaScript interacts with the Document Object Model (DOM) to change web pages in real time:

• Selecting elements with querySelector, getElementById, etc.
• Changing content, styles, and attributes
• Listening for and handling events (clicks, input, form submit)
• Animating elements and controlling interactivity

This is the stage where your learning truly becomes visual—you’ll see your code come to life.

Project-Based Learning

What sets this book apart is its focus on real-world projects, such as:

• A quiz game with scoring
• A calculator
• A to-do list app with local storage
• An e-commerce product page
• A weather app that pulls data from an API

Each project is broken down into manageable parts, and you’re encouraged to experiment, refactor, and even customize these apps.

Modern JavaScript Techniques

The book goes beyond beginner basics and gently introduces modern JS features:

• Arrow functions
• Destructuring
• Template literals
• Spread/rest operators

ES Modules

• Asynchronous JavaScript (fetch, Promises, async/await)
• Basic interaction with external APIs

This prepares you for next-level learning with frontend frameworks like React, Vue, or Svelte.

How You’ll Learn

Build First: You’ll start writing code from the very first chapter.

Mini Challenges: After each concept, you’ll apply what you learned.

Fun Projects: Each project is engaging and creatively designed.

Clear Explanations: Simple, conversational writing style makes even tough concepts digestible.

Stretch Goals: Want a challenge? Each project includes ways to expand or improve your app.

Who Should Read This Book?

This book is ideal for:

• Absolute beginners who have never written a line of code.
• HTML/CSS learners who want to take the next step and make their sites interactive.
• Self-taught developers looking to solidify their JS foundations.
• Students and bootcamp grads needing extra project work and clarity.
• Junior frontend devs preparing for technical interviews or advancing to frameworks.

If you’ve ever said, “I know a bit of JavaScript, but I’m not confident building things,”—this book is for you.

Standout Features

Visual coding style—everything is explained with both code and outcomes.

Project-based structure—you build as you go.

Beginner-friendly tone—no assumptions, no jargon.

Modern JavaScript included—up-to-date and future-proof.

Real-time learning—encourages using the browser console and live code editors like CodePen.

Hard Copy : JavaScript from Beginner to Professional: Learn JavaScript quickly by building fun, interactive, and dynamic web apps, games, and pages

Kindle : JavaScript from Beginner to Professional: Learn JavaScript quickly by building fun, interactive, and dynamic web apps, games, and pages

Conclusion: Start Building, Start Growing

Learning JavaScript can feel intimidating at first—but it doesn’t have to be. JavaScript from Beginner to Professional takes the complexity out of coding and replaces it with clarity, creativity, and confidence.

Instead of memorizing dry syntax or solving disconnected coding puzzles, you’ll build real things that live and breathe in your browser. You’ll learn how to think like a developer, solve problems like a developer, and most importantly—have fun like a developer.

Whether your goal is to launch a new career, build your own projects, or simply understand how the web works, this book offers a practical, enjoyable path from total beginner to someone who can confidently write and ship JavaScript code.

In a world increasingly driven by interactive digital experiences, there’s never been a better time to learn JavaScript. And there’s rarely been a better book to learn it with.

Read More

Building Agentic AI Systems: Create intelligent, autonomous AI agents that can reason, plan, and adapt

 

Building Agentic AI Systems – A Blueprint for the Future of Autonomous Intelligence

"What if AI could act—not just answer? Think, plan, and improve—not just predict? Building Agentic AI Systems shows you how to make that leap."

Overview

Building Agentic AI Systems is not just a technical manual—it's a manifesto for the future of intelligent machines. As we enter the post-LLM (large language model) era, the focus is shifting from passive models to autonomous agents: systems that can reason about the world, plan over time, take initiative, and learn from experience.

This book serves as a comprehensive guide to designing, building, and deploying AI agents that are not just reactive, but proactive—capable of decision-making, tool use, and adaptive behavior in complex environments.

 What Are Agentic AI Systems?

Agentic AI refers to AI systems that operate with a degree of autonomy, often with these four core capabilities:

Reasoning – The ability to evaluate options and make inferences.

Planning – Creating a sequence of actions to achieve long-term goals.

Tool Use – Calling external resources (e.g., APIs, search engines, code compilers).

Adaptation – Learning from feedback and adjusting behavior.

Think of tools like AutoGPT, OpenAI Agents, LangChain, or multi-agent ecosystems that complete tasks without needing step-by-step instructions. This book is a deep dive into the theory and engineering behind them.

What You’ll Learn

Each chapter is crafted with a blend of foundational theory, implementation strategies, and real-world use cases. Here's a breakdown of the key takeaways:

Chapter 1: The Rise of Agency in AI

Why traditional ML is not enough.

Key concepts: autonomy, intentionality, embodiment, alignment.

Historical context from GOFAI to LLM-driven agents.

Chapter 2: Architecting Autonomous Agents

Agent loop: Observe → Think → Act → Reflect.

Architectures: ReAct, Reflexion, CAMEL, AutoGPT.

Memory models: episodic, semantic, and working memory.

Chapter 3: Reasoning and Planning

Rule-based vs. probabilistic reasoning.

Classical planning (STRIPS, PDDL) vs. LLM-driven chaining.

Long-term planning with vector databases and recursive thinking.

Chapter 4: Tool Use and Environment Interaction

Calling APIs, using plugins, and executing code.

Toolformer-style fine-tuning and retrieval-augmented generation.

Integrating browser, file, and coding tools for enhanced cognition.

Chapter 5: Multi-Agent Collaboration

Building swarms, teams, and societies of agents.

Communication protocols and coordination strategies.

Use cases in simulations, games, research, and logistics.

Chapter 6: Feedback, Learning, and Safety

Online learning and continual improvement.

Reward shaping, human-in-the-loop supervision, and safe exploration.

Ethical design and alignment challenges.

Practical Projects in the Book

The book doesn’t just describe—it teaches by doing. You’ll build:

AutoResearcher – A multi-agent system that autonomously explores scientific questions using live data.

TaskCommander – An LLM-powered task agent that can manage your calendar, write emails, and retrieve documents.

CodeArchitect – An AI engineer that builds, tests, and refactors software components.

Simulife – A virtual city populated with AI agents driven by goals, memory, and personality.

Each project includes step-by-step walkthroughs, code snippets (Python + LangChain/OpenAI), and prompts for customization.

Why This Book Matters

The shift toward agentic AI mirrors a broader trend in computing: moving from tools that serve us when asked, to partners that act on our behalf.

This book is a playbook for future-ready AI engineers. As models become cheaper and more powerful, the value will move up the stack—to systems that are persistent, adaptive, and agentive.

Whether you want to build AI co-workers, autonomous researchers, or digital products that think for themselves, this book gives you the framework, tools, and mindset to get started.

Who Should Read This?

AI Practitioners who want to go beyond model training and into full-system design.

Software Developers exploring autonomous workflows and intelligent agents.

Product Managers building AI-native platforms and apps.

Researchers and Students studying cognitive science, reinforcement learning, or artificial general intelligence.

Hard Copy : Building Agentic AI Systems: Create intelligent, autonomous AI agents that can reason, plan, and adapt

Kindle : Building Agentic AI Systems: Create intelligent, autonomous AI agents that can reason, plan, and adapt

Final Thoughts

Building Agentic AI Systems doesn’t just show you how to build intelligent agents—it helps you rethink what intelligence means in the context of machines.

It’s not about copying humans—it’s about designing autonomous, goal-driven systems that can amplify what humans do, while acting in structured, purposeful ways.

If you're ready to move beyond prompts and into systems that reason, plan, act, and learn, this book is your guide.

Read More

Python Coding challenge - Day 508| What is the output of the following Python Code?

 

  Code Explanation:

1. Creating a generator expression

x = (i*i for i in range(3))

Explanation:

Creates a generator x that will yield squares of numbers 0, 1, and 2 (i.e., 0, 1, 4).

The generator is lazy — it doesn’t calculate anything until you ask for values.

Output:

None

2. Getting the first value from the generator

print(next(x))

Explanation:

Calls next(x) to get the first value from the generator.

3. Getting the second value from the generator

print(next(x))

Explanation:

Calls next(x) again.

4. Recreating the generator expression (resetting)

x = (i*i for i in range(3))

Explanation:

Assigns a new generator to x.

This resets the sequence back to start from 0 again.

Output:

None

5. Getting the first value from the new generator

print(next(x))

Explanation:

Calls next(x) on the new generator.

Final Output:

0

1

0

Read More

Python Coding challenge - Day 505| What is the output of the following Python Code?

 

Code Explanation:

1. import math

Purpose: Imports the built-in Python math module, which contains mathematical functions, including sqrt (square root).

Effect: Allows you to use math.sqrt() to calculate square roots.

2. numbers = [4, 9, 16]

Purpose: Defines a list called numbers containing three integers.

Effect: This list is the input data that will be processed by the functions below.

3. def f(x):

Purpose: Defines a function named f that takes one argument x.

Effect: Prepares a function that can be called with a value to perform some operation (defined in the next line).

4. return round(x, 1)

Purpose: Inside function f, returns the value of x rounded to 1 decimal place.

Effect: This ensures the final output is a float rounded to one decimal digit.

5. def g(lst):

Purpose: Defines a function named g that takes one argument lst (expected to be a list).

Effect: Prepares a function that will process the list and return a result.

6. return sum(map(math.sqrt, lst))

Purpose:

map(math.sqrt, lst) applies the math.sqrt function to each element in the list, producing their square roots.

sum(...) adds all those square roots together.

Effect: g(lst) returns the sum of the square roots of the numbers in the list.

7. print(f(g(numbers)))

Purpose: Calls function g on the list numbers, then passes the result to function f, and prints the final output.

Step-by-step:

g(numbers) computes sqrt(4) + sqrt(9) + sqrt(16) → 2 + 3 + 4 = 9.

f(9) rounds 9 to one decimal place → 9.0.

print() outputs 9.0 to the console.

Effect: Outputs the rounded sum of square roots of the list numbers.

Final Output:

9.0

Read More

Python Coding Challange - Question with Answer (01240525)

 

Step-by-Step Explanation:

1. person = "Bob"
   A variable person is assigned the string value "Bob".

2. Calling change_name(person)
   The function change_name is called with person as the argument. So inside the function, name becomes "Bob" (a copy of the string).

3. Inside change_name
   This line:

   
   name = "Alice"

   reassigns the local variable name to the string "Alice".
   However, this does not change the original variable person outside the function.

   • In Python, strings are immutable.

   • When you pass person to the function, you pass a copy of the reference to "Bob".

   • Reassigning name to "Alice" just makes the local variable name point to a different string.

   • The original person variable still points to "Bob".

4. print(person)
   This prints "Bob" because the original person was never changed outside the function.

---

✅ Final Output:

Bob

---

 Key Concept:

In Python, immutable types like strings cannot be changed in-place.
Reassigning a function argument only affects the local copy, not the original variable.

 PYTHON FOR MEDICAL SCIENCE

https://pythonclcoding.gumroad.com/l/luqzrg

Read More

Unlock the Power of Data with Power BI

Unlock the Power of Data with Power BI: A Comprehensive Guide

In today’s digital era, data is the new oil—but raw data by itself holds little value unless it’s refined, analyzed, and visualized effectively. That’s where Microsoft Power BI comes into play.

Power BI has emerged as one of the most powerful business analytics tools available today. It allows organizations and individuals to turn mountains of data into actionable insights, dynamic dashboards, and interactive reports—all without needing deep technical expertise.

Whether you're a business professional, analyst, or decision-maker, this blog will help you understand why Power BI is a game changer and how you can unlock its full potential.

What is Power BI?

Power BI is a cloud-based suite of business analytics tools developed by Microsoft. It enables users to:

Connect to a wide range of data sources

Transform and model data

Create stunning data visualizations

Share insights across teams and organizations

The platform includes:

Power BI Desktop (for report development)

Power BI Service (online SaaS platform for sharing)

Power BI Mobile Apps (for accessing reports on-the-go)

Power BI Embedded (for developers to integrate into custom apps)

Why Power BI?

Here’s why Power BI stands out in the world of business intelligence:

1. User-Friendly Interface

Power BI has a modern, intuitive drag-and-drop interface that’s accessible even to those without a technical background. Creating visuals is as easy as selecting fields and dropping them onto a canvas.

2. Seamless Integration

It integrates smoothly with Microsoft products like Excel, Azure, and Teams, as well as third-party services such as Salesforce, Google Analytics, and databases like SQL Server, PostgreSQL, and more.

3. Powerful Data Modeling

With tools like Power Query and DAX (Data Analysis Expressions), users can clean, transform, and model data efficiently, enabling complex calculations and logic with ease.

4. Real-Time Dashboards

Monitor key performance indicators (KPIs) and metrics in real-time. Power BI’s dashboards auto-refresh to provide up-to-date insights from streaming or frequently updated data sources.

5. Scalable & Secure

Whether you're a startup or a global enterprise, Power BI scales to your needs. It also offers robust security features including row-level security, encryption, and compliance with major regulatory standards.

Key Features That Make Power BI Powerful

Interactive Visualizations

Choose from a wide variety of visuals: bar charts, heatmaps, treemaps, gauges, KPI indicators, custom visuals, and more.

Scheduled Data Refresh

Automate data refreshes to keep your reports current without manual intervention.

Data Connectivity

Power BI can connect to hundreds of data sources—on-premises or in the cloud—including:

Excel files

SharePoint

SQL databases

REST APIs

Web pages

JSON and XML files

AI-Powered Insights

Utilize AI capabilities such as natural language queries (Q&A visual), key influencer analysis, anomaly detection, and text analytics—all built into the platform.

 Role-Based Access Control

Set user permissions to control who sees what, ensuring data privacy and compliance.

Use Cases Across Industries

Power BI has applications across nearly every sector:

Finance: Real-time P&L dashboards, budgeting, forecasting, and risk analysis

Retail: Sales performance tracking, customer segmentation, inventory optimization

Healthcare: Patient care metrics, operational efficiency, claims analysis

Manufacturing: Production monitoring, quality control, supply chain analytics

Marketing: Campaign ROI, lead tracking, customer behavior analysis

Learning Resources

To master Power BI, here are some great learning paths:

Microsoft Learn – Power BI Modules

Coursera – Data Visualization with Power BI

LinkedIn Learning – Power BI Essential Training

YouTube – Guy in a Cube (official Power BI evangelists)

The Future of Business Intelligence is Here

As organizations become increasingly data-centric, Power BI bridges the gap between data and decision-making. It democratizes analytics by empowering every employee—from frontline workers to executives—to make data-driven decisions.

With continuous innovation, integrations with Azure AI, and a strong user community, Power BI is not just a tool—it’s a platform built for the future of business analytics.

Join Free : Unlock the Power of Data with Power BI

Final Thoughts

Unlocking the power of data doesn’t require you to be a data scientist. With Power BI, you can:

Visualize trends

Uncover hidden patterns

Monitor performance

Make impactful decisions

Whether you're new to analytics or looking to enhance your existing toolkit, Power BI offers the flexibility, scalability, and functionality you need.

Read More

Preparing Data for Analysis with Microsoft Excel

 

Mastering Excel for Data Analysis: A Deep Dive into Coursera’s “Preparing Data for Analysis with Microsoft Excel”

In today’s data-driven world, proficiency in Microsoft Excel is more than just a valuable skill—it’s a necessity. Whether you're a budding data analyst, a business professional, or someone looking to enhance your data management capabilities, Coursera's course, “Preparing Data for Analysis with Microsoft Excel,” offers a comprehensive pathway to mastering Excel for data analysis.

Course Overview

Offered by Microsoft and hosted on Coursera, this beginner-friendly course is part of the Microsoft Power BI Data Analyst Professional Certificate. With over 325,000 enrollments and a stellar 4.7-star rating from more than 4,300 reviews, it's evident that this course resonates with learners worldwide. 

Key Details:

Duration: Approximately 19 hours

Level: Beginner (no prior experience required)

Language: English (with subtitles in 29 languages)

Certification: Shareable certificate upon completion

Skills Acquired: Data cleansing, data manipulation, Excel formulas, pivot tables, Power BI integration, and more.

Course Structure

The course is meticulously structured into four modules, each designed to build upon the previous, ensuring a cohesive learning experience.

1. Excel Fundamentals

This module lays the groundwork by introducing essential Excel elements and techniques. Learners will explore worksheet creation, formatting, and features that facilitate viewing large datasets. Accurate calculation methods are also covered, setting the stage for more advanced topics.

2. Formulas and Functions

Delving deeper, this module focuses on the backbone of Excel—formulas and functions. Learners will understand their significance in data analysis and how they're applied in real-world business scenarios.

3. Preparing Data for Analysis Using Functions

Here, the course introduces common functions that aid in preparing Excel data for analysis, especially when integrating with tools like Power BI. This practical module equips learners with the skills to manipulate data efficiently.

4. Final Project and Assessment

The culmination of the course involves a hands-on project, allowing learners to apply the skills they've acquired. This real-world assessment reinforces learning and boosts confidence in using Excel for data analysis.

Why Enroll?

1. Beginner-Friendly Approach

No prior experience with Excel or data analysis? No problem. The course is tailored for newcomers, ensuring that foundational concepts are thoroughly covered.

2. Comprehensive Curriculum

From basic Excel operations to preparing data for advanced analysis, the course offers a well-rounded education, making it a valuable resource for enhancing analytical skills.

3. Practical Application

With 21 assignments and a final project, learners get ample hands-on experience, ensuring that theoretical knowledge is effectively translated into practical skills.

4. Career Advancement

As part of the Microsoft Power BI Data Analyst Professional Certificate, this course serves as a stepping stone for those aiming to delve deeper into data analysis and visualization, opening doors to various career opportunities.

5. Flexible Learning

The self-paced nature of the course allows learners to progress according to their schedules, making it ideal for working professionals and students alike.

Learner Testimonials

The course has garnered positive feedback from learners:

"This course helps you to get into the world of Excel. It is not a complete package but it has all the foundation components to help you explore more."

"When starting this course I thought I knew these functions and things, but when doing it I realized I only knew 60% of the programs and some functions. Went in deep—good one."

Additional Resources

For those interested in supplementary materials, a GitHub repository contains practice files associated with the course: 

Join Free : Preparing Data for Analysis with Microsoft Excel

Conclusion

“Preparing Data for Analysis with Microsoft Excel” stands out as a comprehensive, beginner-friendly course that bridges the gap between basic Excel usage and advanced data analysis. Its structured approach, practical assignments, and integration with Power BI make it an invaluable resource for anyone looking to harness the power of Excel in data-driven roles

Read More

Programming in Python: A Hands-on Introduction Specialization

 

Programming in Python: A Hands-on Introduction Specialization – A Complete Overview

If you're looking to start your programming journey, Python is often the best place to begin. Among the many available online courses, the "Programming in Python: A Hands-on Introduction Specialization" on Coursera, offered by Rice University, stands out as a top choice. This beginner-friendly specialization walks you through Python fundamentals in a practical, interactive way that’s perfect for learners with no prior coding experience.

About the Course

This specialization is structured by Rice University and available through Coursera. It’s a beginner-level course series designed to be completed in about 4 months if you spend around 3 hours per week. The course is led by Professors Dr. Scott Rixner and Dr. Joe Warren, who are known for their effective and approachable teaching style.

The course provides hands-on coding practice directly in the browser, with an emphasis on problem-solving, real programming concepts, and incremental learning. You also receive a certificate upon completion.

Course Structure

The specialization consists of four separate courses, each building upon the last.

1. Python Programming Essentials

This is where the journey begins. You'll learn what programming is, how Python works, and start using variables, expressions, conditionals, loops, and functions. It focuses on building a strong foundational understanding with real coding exercises, even if you’ve never written a line of code before.

2. Python Data Representations

This course focuses on Python’s powerful data types like strings, lists, tuples, and dictionaries. You'll also learn how to read and write files, manipulate structured data, and apply these tools in simple programs. This step is essential for working with real-world data later on.

3. Python Data Analysis

In this course, you shift gears into the world of data science. You'll use the Pandas library to import, clean, and analyze data. Basic visualizations are also introduced using Matplotlib, allowing you to generate insights from datasets. This is a great introduction to analytical programming and data-driven thinking.

4. Python Programming Projects

The final course is project-based. You apply everything you've learned by building full Python programs. These projects simulate real-world tasks like text analysis or data manipulation. This phase cements your understanding and helps you develop more independence as a coder.

Key Features of the Specialization

One of the standout features is the in-browser coding environment, which means you can start coding immediately—no installation needed. Each exercise is auto-graded, giving you instant feedback and helping you identify mistakes early.

Additionally, the content is broken into short, digestible videos and exercises, making it easy to fit into a busy schedule. The visual teaching style is especially helpful for new learners, and the professors' clarity in explanations adds to the course's appeal.

Who Should Take This Course?

This specialization is best suited for:

• Absolute beginners in programming
• Career changers moving into tech, automation, or data analysis
• Students or professionals in non-technical fields
• Anyone who prefers a structured, academic-style approach to learning

However, if you're already familiar with Python or another programming language, you might find this course too basic. It's designed to move at a beginner’s pace, so experienced programmers may want to look for intermediate or advanced material.

Pros and Cons

Pros:

Clear and beginner-friendly instruction

Interactive, hands-on learning

Strong conceptual grounding

Real coding projects to reinforce learning

Prestigious university-backed certificate

Cons:

Slow for learners with prior coding experience

Not focused on specific career paths like web development or machine learning

Capstone projects may feel simple to some

Learning Tips

To get the most out of this specialization:

Practice regularly—coding is like a muscle that builds with use.

Repeat exercises with modifications to deepen your understanding.

Use forums and community discussions to resolve doubts.

Once you're comfortable, move to a real code editor like VSCode to simulate professional workflows.

Join Free : Programming in Python: A Hands-on Introduction Specialization

Conclusion: Is It Worth It?

Yes, absolutely—this is one of the best beginner Python courses available online. The “Programming in Python: A Hands-on Introduction” specialization delivers real, applicable knowledge in a supportive and structured environment. If you're just starting your programming journey and want to build strong Python fundamentals, this is an excellent place to begin.

Whether you're exploring coding out of curiosity or planning a career shift, this specialization provides the skills and confidence to take the next step.

Read More

Microsoft Python Development Professional Certificate

 

Microsoft Python Development Professional Certificate: A Complete Guide for 2025

Python is the go-to language for web development, automation, data science, and AI. If you're looking to learn Python in a structured, project-based way, the Microsoft Python Development Professional Certificate on edX is one of the best options available today. Developed by Microsoft, this course series takes you from beginner to job-ready, even if you have no prior programming experience.

What Is the Microsoft Python Development Professional Certificate?

This is a multi-course professional certification program offered by Microsoft via edX. It’s designed to teach you Python from the ground up, with a hands-on approach to programming fundamentals, data handling, APIs, object-oriented design, and even version control.

Unlike generic tutorials, this program focuses on practical skills, real-world projects, and tools used in industry. Once completed, you’ll receive a shareable, Microsoft-backed certificate, ideal for resumes and LinkedIn.

Why Learn Python?

Python is widely used across various industries due to its simplicity and versatility. It powers everything from Instagram and Spotify to NASA and Netflix. Companies are hiring Python developers for roles in:

Software Development

Automation Engineering

Data Analysis

QA Testing

Scripting & Infrastructure

Learning Python can help you automate tasks, analyze data, or even build full applications. Whether you're a student, career changer, or professional looking to upskill, Python is one of the most rewarding programming languages to master.

Course Breakdown: What's Included?

The certificate program is divided into multiple self-paced courses, each building on the previous one. Here's what you'll learn:

1. Introduction to Python Programming

You'll start with the basics — learning Python syntax, variables, control flow, loops, and functions. This course builds the foundation for everything that follows.

Hands-on Project: Create a basic calculator and a number guessing game.

2. Object-Oriented Programming in Python

This course dives into how Python handles classes, objects, inheritance, and encapsulation — concepts critical to building real-world applications.

Project Idea: Build a student grading system or library management app.

3. Data Structures and File Handling

Here, you’ll master lists, dictionaries, sets, tuples, and work with file I/O. You'll also learn how to parse and store data using formats like CSV and JSON.

Hands-on Task: Create a note-taking app or contact manager.

4. Working with APIs

Learn how to connect to real-world services using HTTP and APIs. You’ll fetch data from web servers, parse JSON, and use libraries like requests.

Project: Build a weather or movie info app using free public APIs.

5. Debugging, Testing & Error Handling

This course covers debugging techniques, writing unit tests, and managing exceptions. You’ll learn how to write stable, production-ready code.

Mini Project: Add error handling and unit tests to a Python app.

6. Git and Version Control

Learn Git fundamentals: clone, commit, push, pull, and branching. You’ll use GitHub to manage code, collaborate, and document your work.

Task: Fork and contribute to a GitHub project.

7. Final Capstone Project

This is where you bring it all together. You’ll design and build a complete Python application using everything you've learned.

Capstone Ideas:

A task automation tool

A stock price tracker

A portfolio analytics dashboard

Key Skills You’ll Gain

Throughout the program, you’ll master:

Python programming (beginner to intermediate)

Object-Oriented Programming (OOP)

Data structures and algorithms

API integration and automation

Debugging and testing

File and JSON handling

Git and GitHub version control

These skills are applicable in fields like web development, data science, DevOps, and QA.

Who Should Take This Certificate?

This course is perfect for:

Absolute beginners in coding

Career switchers entering tech

Students seeking supplemental training

IT professionals expanding into software

Business/data analysts looking to automate workflows

No degree or prior programming experience is required — just a willingness to learn.

Career Outcomes and Benefits

With this certificate, you’ll be ready for roles such as:

Python Developer

QA Automation Engineer

Junior Software Engineer

Data Analyst (Python-based)

Scripting or DevOps roles

The certificate also enhances your credibility when applying for internships or freelance gigs. Combined with your capstone project and GitHub portfolio, it becomes a strong entry point into tech.

Tips for Success

To get the most out of this program:

Practice daily — Code every day, even in short sessions

Build projects — Go beyond the exercises

Use GitHub — Document your work and build a portfolio

Join forums — Participate in edX discussions, Reddit, or Discord groups

Stay consistent — Create a study routine that works for you

Join Free : Microsoft Python Development Professional Certificate

Final Thoughts

The Microsoft Python Development Professional Certificate is a top-tier, beginner-friendly program that doesn’t just teach you Python — it teaches you how to think like a developer. With a Microsoft-issued credential and practical projects, it offers both credibility and competence.

If you're looking to break into tech or add coding to your skill set in 2025, this is a highly recommended path.

Read More

Python Coding Challange - Question with Answer (01230525)

 

Step-by-step Explanation:

1. x = 5
   You define a variable x and assign it the integer value 5.

2. Calling double(x)
   You pass x to the function double.

3. Inside the function double(n)
   n receives a copy of the value of x (which is 5).
   The line n *= 2 is the same as n = n * 2, so n becomes 10.

   However, this change only affects the local variable n, not the original x.

4. Back in the main program
   After the function call, x is still 5 because:

   • Integers are immutable in Python.

   • Assigning a new value to n inside the function does not change x.

---

✅ Final Output:

print(x)  # Output: 5

---

 Key Concept:

In Python, immutable objects like integers, strings, and tuples are passed by value (actually, by object reference, but since they can't change, it's like by value). So any changes inside a function don't affect the original variable.

 APPLICATION OF PYTHON IN FINANCE

https://pythonclcoding.gumroad.com/l/zrisob

Read More

Python Coding challenge - Day 504| What is the output of the following Python Code?

 

Code Explanation:

1. Import the Matplotlib Library

import matplotlib.pyplot as plt

Imports the pyplot module from matplotlib, commonly used for plotting in Python.

This module provides a MATLAB-like interface for creating plots and figures.

2. Create a Single Subplot

ax = plt.subplot()

Creates a single axes (subplot) and assigns it to the variable ax.

Equivalent to fig, ax = plt.subplots() but shorter when you only need one plot.

This is the area where the plot will be drawn.

3. Plot a Line on the Axes

ax.plot([1, 2, 3], [4, 5, 6], label='Line')

Plots a line using x-values [1, 2, 3] and y-values [4, 5, 6].

The label='Line' is used for legend identification.

4. Add a Legend with Custom Position

ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.1))

Adds a legend to the plot to label the plotted line.

loc='upper center': anchor point inside the legend box.

bbox_to_anchor=(0.5, -0.1):

Positions the legend outside the plot area, horizontally centered (x=0.5), slightly below the axes (y=-0.1).

5. Save the Plot to a File

plt.savefig('plot.png')

Saves the entire figure (not just the plot) to a file named plot.png.

Useful for exporting plots in scripts or automated reports.

6. Access and Print the Legend’s X-Coordinate

print(ax.get_legend().get_bbox_to_anchor()._bbox.x0)

ax.get_legend() retrieves the legend object.

.get_bbox_to_anchor() gets the bounding box anchor object.

._bbox.x0 accesses the x-coordinate (left side) of the bounding box — in this case: 0.5.

Final Output

0.5

This is the x-coordinate of the anchor point of the legend relative to the axes' bounding box.

Read More

Python Coding challenge - Day 503| What is the output of the following Python Code?

 

Code Explanation:

1. Create NumPy Array

a = np.array([1, 2, 3])

Creates a NumPy array a with values [1, 2, 3].

Shape: (3,), dtype: int64

a now → [1, 2, 3]

2. Assign Reference (Not a Copy)

b = a

b is not a new array — it references the same memory as a.

So, any change in b will reflect in a and vice versa.

3. Modify Element via Reference

b[0] = 99

Changes the first element of b to 99.

Since a and b are the same object, a also becomes [99, 2, 3].

4. Create a Deep Copy

c = a.copy()

Creates an independent copy of a.

Now c = [99, 2, 3], but it does not share memory with a or b.

5. Modify the Copy

c[1] = 88

Changes the second element of c to 88.

c becomes [99, 88, 3].

a and b remain [99, 2, 3].

6. Sum All Arrays and Print Result

print(np.sum(a) + np.sum(b) + np.sum(c))

a = [99, 2, 3] → sum = 104

b = [99, 2, 3] → same as a → sum = 104

c = [99, 88, 3] → sum = 190

Total Sum: 104 + 104 + 190 = 398

Final Output: 398

Read More

3D Checkboard Surface Pattern using python

 

import numpy as np

import matplotlib.pyplot as plt

x = np.linspace(-5, 5, 100)

y = np.linspace(-5, 5, 100)

x, y = np.meshgrid(x, y)

z = np.sin(x) * np.cos(y)  

checkerboard = ((np.floor(x) + np.floor(y)) % 2) == 0

colors = np.zeros(x.shape + (3,))

colors[checkerboard] = [1, 1, 1] 

colors[~checkerboard] = [0, 0, 0]  

fig = plt.figure(figsize=(6, 6))

ax = fig.add_subplot(111, projection='3d')

ax.plot_surface(x, y, z, facecolors=colors, rstride=1, cstride=1)

ax.set_title("3D Checkerboard Surface", fontsize=14)

ax.set_box_aspect([1, 1, 0.5])  

ax.axis('off')  

plt.tight_layout()

plt.show()

#source code --> clcoding.com 

Code Explanation:

1. Import Libraries

import numpy as np

import matplotlib.pyplot as plt

numpy (as np): Used for creating grids and performing numerical calculations (like sin, cos, floor, etc.).

matplotlib.pyplot (as plt): Used for plotting graphs and rendering the 3D surface.

 

2. Create Grid Coordinates (x, y)

x = np.linspace(-5, 5, 100)

y = np.linspace(-5, 5, 100)

x, y = np.meshgrid(x, y)

np.linspace(-5, 5, 100): Generates 100 evenly spaced values from -5 to 5 for both x and y.

np.meshgrid(x, y): Creates 2D grids from the 1D x and y arrays — necessary for plotting surfaces.

 

3. Define Surface Height (z values)

z = np.sin(x) * np.cos(y)

This creates a wavy surface using a trigonometric function.

Each (x, y) point gets a z value, forming a 3D landscape.

 

4. Generate Checkerboard Pattern

checkerboard = ((np.floor(x) + np.floor(y)) % 2) == 0

np.floor(x): Takes the floor (integer part) of each x and y coordinate.

Adds the floored x + y, and checks if the sum is even (i.e., divisible by 2).

If so → True (white square), else → False (black square).

This results in a checkerboard-like boolean mask.

 

5. Assign Colors to Checkerboard

colors = np.zeros(x.shape + (3,))

colors[checkerboard] = [1, 1, 1]

colors[~checkerboard] = [0, 0, 0]

colors = np.zeros(x.shape + (3,)): Initializes an array for RGB colors (shape: rows × cols × 3).

For True cells in checkerboard, assign white [1, 1, 1].

For False cells, assign black [0, 0, 0].

 

6. Set Up 3D Plot

fig = plt.figure(figsize=(8, 6))

ax = fig.add_subplot(111, projection='3d')

Creates a figure and a 3D subplot using projection='3d'.

 

7. Plot the Checkerboard Surface

ax.plot_surface(x, y, z, facecolors=colors, rstride=1, cstride=1)

Plots the 3D surface using x, y, z data.

facecolors=colors: Applies the checkerboard color pattern.

rstride and cstride: Row/column steps for rendering — set to 1 for full resolution.

 

8. Customize the View

ax.set_title("3D Checkerboard Surface", fontsize=14)

ax.set_box_aspect([1, 1, 0.5])

ax.axis('off')

set_title(): Sets the plot title.

set_box_aspect(): Controls aspect ratio: x:y:z = 1:1:0.5 (compressed z).

axis('off'): Hides axis ticks and labels for a clean look.

 

9. Render the Plot

plt.tight_layout()

plt.show()

tight_layout(): Adjusts spacing to prevent overlap.

show(): Renders the 3D checkerboard surface.

 

Read More

Python Coding challenge - Day 502| What is the output of the following Python Code?

 

Code Explanation:

Line 1: Import reduce function

from functools import reduce

Explanation:

reduce() is a function from the functools module.

It repeatedly applies a function to the items of an iterable, reducing the iterable to a single cumulative value.

Line 2: Define the list numbers

numbers = [1, 2, 3, 4]

Explanation:

A list of integers is created: [1, 2, 3, 4].

Line 3: Define function f

f = lambda x: x * 2

Explanation:

This lambda function doubles the input.

Example: f(5) returns 10.

Line 4: Define function g

g = lambda lst: reduce(lambda a, b: a + b, lst)

Explanation:

g is a function that:

Takes a list lst.

Uses reduce() to sum all elements of the list.

Example: g([1, 2, 3, 4]) will compute 1 + 2 + 3 + 4 = 10.

Line 5: Combine functions and print result

print(f(g(numbers)))

Step-by-step Evaluation:

g(numbers):

Input: [1, 2, 3, 4]

Sum = 1 + 2 + 3 + 4 = 10

f(10):

10 * 2 = 20

Final Output:

20

Read More

Python Coding challenge - Day 501| What is the output of the following Python Code?

 

Code Explanation:

Line 1: Define combine function

def combine(f, g):

    return lambda x: f(g(x))

Explanation:

This function takes two functions f and g as inputs.

It returns a new anonymous function (lambda) that takes an input x, applies g(x) first, and then applies f() to the result of g(x).

In other words, it returns f(g(x)) — this is called function composition.

Line 2: Define f

f = lambda x: x ** 2

Explanation:

f is a lambda function that squares its input.

Example: f(4) = 4 ** 2 = 16

Line 3: Define g

g = lambda x: x + 2

Explanation:

g is a lambda function that adds 2 to its input.

Example: g(3) = 3 + 2 = 5

Line 4: Compose functions using combine

h = combine(f, g)

Explanation:

h is now a new function created by combining f and g.

h(x) will compute f(g(x)), which is:

First: g(x) → add 2

Then: f(g(x)) → square the result

Line 5: Call and print h(3)

print(h(3))

Step-by-step Evaluation:

h(3) = f(g(3))

g(3) = 3 + 2 = 5

f(5) = 5 ** 2 = 25

So, h(3) = 25

Final Output:

25

Read More

Spring System Design in Practice: Build scalable web applications using microservices and design patterns in Spring and Spring Boot

 

Spring System Design in Practice — A Detailed Review and Key Takeaways

As the software world rapidly moves toward microservices and distributed systems, mastering scalable system design becomes not just a bonus skill but a necessity. "Spring System Design in Practice" is a hands-on, practical guide that offers an essential roadmap for developers, architects, and tech leads who want to harness the power of Spring Boot, microservices architecture, and design patterns.

In this blog, we’ll break down the structure, key themes, and practical insights of the book, and explain why it’s a must-read for Java/Spring developers aiming to build robust and scalable systems.

Book Overview

Full Title: Spring System Design in Practice: Build Scalable Web Applications Using Microservices and Design Patterns in Spring and Spring Boot

Best for: Mid-level to senior Java/Spring developers, architects, backend engineers

The book takes a problem-solution approach, focusing on real-world use cases and system-level design challenges. It teaches how to break a monolith into microservices, choose the right design patterns, and build high-performance, secure, and scalable applications using Spring Boot, Spring Cloud, and other related tools.

Key Topics Covered

1. Monolith to Microservices Transition

The book begins by illustrating why and when you should move away from monoliths. It presents practical strategies for decomposing a monolithic application and transitioning to microservices incrementally using Spring Boot.

Highlights:

• Domain-driven decomposition
• Strangler fig pattern
• Service boundaries and Bounded Contexts

2. Core Microservices Principles in Spring

Each microservice is treated as a mini-application. The book details the fundamental practices:

• Using Spring Boot for lightweight services
• Leveraging Spring WebFlux for reactive programming
• Managing inter-service communication via REST and gRPC
• Patterns Explored:
• API Gateway
• Circuit Breaker (Resilience4j)
• Service Discovery (Spring Cloud Netflix Eureka)

3. Design Patterns for Scalable Systems

This is arguably the most valuable section. The book dives deep into classic and cloud-native design patterns like:

• Repository Pattern (for clean data access)
• Command Query Responsibility Segregation (CQRS)
• Event Sourcing
• Saga Pattern (for distributed transactions)
• Outbox Pattern
• Bulkhead and Rate Limiting

Each pattern is explained with practical code samples and trade-offs.

4. System Design Case Studies

This is where theory meets reality. The book includes multiple case studies such as:

• E-commerce system
• Payment gateway
• Order management service
• Each case study demonstrates:
• Domain modeling
• API design
• Database design
• Service integration

5. Infrastructure and DevOps

To build truly scalable systems, infrastructure is key. The book covers:

Containerization with Docker

Deploying to Kubernetes

Using Spring Cloud Config Server for centralized configuration

Observability with Sleuth, Zipkin, and Prometheus/Grafana

6. Security and Resilience

Security in microservices can be tricky. The book teaches:

OAuth2 and JWT with Spring Security

Securing service-to-service calls

Implementing TLS, API keys, and mutual TLS

It also emphasizes graceful degradation, circuit breakers, and retries to ensure high availability.

Who Should Read This Book?

This book is perfect for:

• Backend Developers looking to level up their Spring ecosystem skills
• Tech Leads & Architects who design and manage distributed systems
• DevOps Engineers wanting to understand system requirements from the developer's perspective
• Students & Interviewees preparing for system design interviews

Pros

• Practical approach with step-by-step code examples
• Covers both design theory and engineering practices
• Deep dives into design patterns with real-world scenarios
• Infrastructure and DevOps coverage (Docker, Kubernetes)

 Cons

•  Assumes basic familiarity with Spring; not ideal for total beginners
• Some topics (e.g., gRPC or GraphQL) could use more depth

Hard Copy : Spring System Design in Practice: Build scalable web applications using microservices and design patterns in Spring and Spring Boot

Kindle : Spring System Design in Practice: Build scalable web applications using microservices and design patterns in Spring and Spring Boot

Final Takeaway

"Spring System Design in Practice" is more than just a programming book — it’s a manual for building real-world systems in the modern, cloud-native world. Whether you're migrating a monolith, designing a new microservice, or scaling an existing platform, this book gives you the tools, insights, and patterns to do it right.

Read More

Python Coding Challange - Question with Answer (01220525)

 

Key Concepts:

🔹 lst=[] is a mutable default argument.

• In Python, default argument values are evaluated only once when the function is defined, not each time it’s called.

• That means the same list (lst) is reused across multiple calls unless a new one is explicitly provided.

---

Step-by-Step Execution:

First Call:

append_item(1)

val = 1

• No list is passed, so lst defaults to []

• 1 is appended to the list → list becomes [1]

• It returns [1]

Second Call:

append_item(2)

val = 2

• Still using the same list as before ([1])

• 2 is appended → list becomes [1, 2]

• It returns [1, 2]

---

✅ Output:

[1]
[1, 2]

---

 How to Avoid This Pitfall:

To make sure a new list is used for each call, use None as the default and create the list inside the function:

def append_item(val, lst=None):
    if lst is None:
       lst = []
    lst.append(val)     
    return lst

Now each call will work with a fresh list.

APPLICATION OF PYTHON IN FINANCE

https://pythonclcoding.gumroad.com/l/zrisob

Read More

Machine Learning Basics

 

Machine Learning Basics: A Complete Beginner's Guide

What is Machine Learning?

Machine Learning (ML) is a subfield of Artificial Intelligence that enables computers to learn from data and make predictions or decisions without being explicitly programmed. Instead of following hard-coded rules, ML systems use statistical techniques to identify patterns in data and apply those patterns to new, unseen information. For example, an ML model can learn to recognize cats in images after analyzing thousands of labeled photos. Just like humans learn from experience, machines learn from data.

Why is Machine Learning Important?

Machine learning has become a core technology in almost every industry. It powers the personalized recommendations on Netflix and Amazon, enables virtual assistants like Siri and Alexa to understand speech, helps banks detect fraudulent transactions, and supports doctors in diagnosing diseases. Its ability to make data-driven decisions at scale makes it one of the most transformative technologies of the 21st century.

Data: The Foundation of Machine Learning

At the heart of machine learning is data. Models are trained using datasets that contain examples of what the system is expected to learn. These examples include features (inputs like age, temperature, or words in a sentence) and labels (the desired output, such as a category or value). The more accurate, complete, and relevant the data, the better the model’s performance. A model trained on poor-quality data will struggle to deliver useful predictions.

Training and Testing Models

Machine learning involves two primary phases: training and testing. During training, the model studies a dataset to learn patterns. Once trained, it is evaluated on a separate testing dataset to see how well it performs on new data. This helps determine if the model can generalize beyond the examples it was trained on. A good model strikes a balance — it must be complex enough to capture patterns but not so specific that it only works on the training data (a problem known as overfitting).

Types of Machine Learning

There are three major categories of machine learning:

Supervised Learning

In supervised learning, the algorithm is given labeled data — meaning each input has a known output. The model learns to map inputs to outputs. Common applications include spam detection, sentiment analysis, and price prediction.

Unsupervised Learning

Unsupervised learning works with unlabeled data. The model tries to uncover hidden patterns or groupings within the dataset. Examples include customer segmentation, recommendation systems, and topic modeling.

Reinforcement Learning

In reinforcement learning, an agent learns to make decisions by interacting with its environment and receiving feedback in the form of rewards or penalties. It’s widely used in robotics, game AI (like AlphaGo), and self-driving cars.

Common Algorithms (Simplified)

Machine learning uses various algorithms to solve different types of problems. Some basic ones include:

Linear Regression: Predicts a numerical value (e.g., house price).

Logistic Regression: Used for binary classification (e.g., spam or not spam).

Decision Trees: Splits data into decision paths based on rules.

K-Nearest Neighbors (KNN): Classifies new data points based on similarity to known points.

Neural Networks: Inspired by the brain, used for complex tasks like image and speech recognition.

These algorithms vary in complexity and are chosen based on the problem type and data characteristics.

Challenges in Machine Learning

Machine learning isn’t magic — it comes with its own set of challenges:

Overfitting: When a model learns the training data too well, including its noise or errors, leading to poor performance on new data.

Underfitting: When a model is too simple to capture the underlying patterns in the data.

Bias and Fairness: If the training data reflects human biases, the model can perpetuate and even amplify them — leading to unfair or unethical outcomes.

Understanding and addressing these issues is critical for building reliable and responsible ML systems.

Tools and Languages Used in ML

While deep technical knowledge isn’t required to grasp ML basics, professionals often use the following tools:

Languages: Python (most popular), R

Libraries: scikit-learn, TensorFlow, PyTorch, Keras

Platforms: Google Colab, Jupyter Notebooks, Kaggle, AWS SageMaker

These tools allow data scientists to build, test, and deploy ML models efficiently.

How to Start Learning Machine Learning

You don’t need to be a programmer to begin learning about ML. Here’s how to start:

Understand the Concepts: Take beginner-friendly courses like “Machine Learning for All” on Coursera or watch YouTube explainers.

Learn Basic Python: Most ML is done in Python, and basic programming skills go a long way.

Explore Datasets: Use public data on platforms like Kaggle to practice.

Try Mini Projects: Build simple projects like spam filters, movie recommenders, or image classifiers.

Practice and experimentation are key to gaining hands-on experience.

The Future of Machine Learning

Machine learning will continue to revolutionize how we work, communicate, and solve problems. It’s already being used in fields like agriculture, education, finance, transportation, and climate science. As the technology becomes more accessible, we’ll see a rise in citizen data scientists — professionals in every field using ML tools to make better decisions and drive innovation.

Join Free : Machine Learning Basics

Final Thoughts

Machine Learning may sound complex, but at its core, it's about learning from data and making predictions. As we enter an increasingly data-driven world, understanding ML—even at a basic level—will help you become a more informed and empowered citizen. Whether you’re a student, a professional, or just curious, the best time to start learning about machine learning is now.

Read More

Chrono Web Pattern using Python

 

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

r = np.linspace(0.1, 5, 200)

theta = np.linspace(0, 2 * np.pi, 200)

r, theta = np.meshgrid(r, theta)

X = r * np.cos(theta)

Y = r * np.sin(theta)

Z = np.sin(4 * theta - 2 * r) * np.exp(-0.1 * r)

fig = plt.figure(figsize=(6, 6))

ax = fig.add_subplot(111, projection='3d')

ax.plot_surface(X, Y, Z, cmap='viridis', edgecolor='black', linewidth=0.1)

ax.set_title('Chrono Web', fontsize=18, fontweight='bold')

ax.axis('off')

ax.view_init(elev=30, azim=45)

plt.tight_layout()

plt.show()

#source code --> clcoding.com

Code Explanation:

1. Importing Libraries

import numpy as np

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

numpy is used for numerical operations, especially for creating arrays and mathematical functions.

matplotlib.pyplot is the plotting library used for visualization.

mpl_toolkits.mplot3d enables 3D plotting capabilities in matplotlib.

2. Create the Polar Grid

r = np.linspace(0.1, 5, 200)

theta = np.linspace(0, 2 * np.pi, 200)

r, theta = np.meshgrid(r, theta)

r (radius) goes from 0.1 to 5 in 200 steps.

theta (angle) goes from 0 to 2π (a full circle) in 200 steps.

np.meshgrid creates a 2D grid from these vectors, so we can calculate X, Y, and Z values over the full polar coordinate system.

3. Convert Polar Coordinates to Cartesian

X = r * np.cos(theta)

Y = r * np.sin(theta)

Converts each point in the polar grid into Cartesian coordinates.

This is needed because matplotlib 3D plots are in X-Y-Z space.

4. Define Z Values (Height) – the "Chrono Web" Pattern

Z = np.sin(4 * theta - 2 * r) * np.exp(-0.1 * r)

This formula creates radial sine wave ripples.

4 * theta gives a rotational (angular) ripple with 4 waves per rotation.

-2 * r makes the wave shift inward or outward, creating a spiraling effect.

np.exp(-0.1 * r) damps the wave amplitude as the radius increases — simulating fading over distance, like time decay.

5. Set Up the Plot

fig = plt.figure(figsize=(10, 8))

ax = fig.add_subplot(111, projection='3d')

fig = plt.figure(...) creates the figure window with a specific size.

add_subplot(..., projection='3d') initializes a 3D plot.

6. Draw the Surface

ax.plot_surface(X, Y, Z, cmap='viridis', edgecolor='black', linewidth=0.1)

plot_surface draws a 3D surface.

cmap='viridis' gives a smooth color gradient.

edgecolor='black', linewidth=0.1 adds a subtle grid to give a web-like structure.

7. Customize the Plot

ax.set_title('Chrono Web', fontsize=18, fontweight='bold')

ax.axis('off')

ax.view_init(elev=30, azim=45)

set_title(...) adds a bold title to the plot.

axis('off') hides the axes for a cleaner, more artistic look.

view_init(...) sets the camera angle (elevation = 30°, azimuth = 45°) for 3D viewing.

8. Final Layout and Display

plt.tight_layout()

plt.show()

tight_layout() adjusts the spacing to fit all elements nicely.

plt.show() renders the plot window and displays the final "Chrono Web" 3D pattern.

Read More