Python Coding challenge - Day 860| What is the output of the following Python Code?

Code Explanation:

1. Class X Definition

class X:

    def get(self): return "X"

A class X is created.

It defines a method get() that returns the string "X".

Any object of class X can call get() and receive "X".

2. Class Y Definition

class Y:

    def get(self): return "Y"

Another class Y is defined.

It also has a method get(), but this one returns "Y".

This sets up a scenario where both parent classes have the same method name.

3. Class Z Uses Multiple Inheritance

class Z(X, Y):

Class Z inherits from both X and Y.

Because X is listed first, Python’s Method Resolution Order (MRO) will look into:

Z

X

Y

object

4. Z Overrides get()

    def get(self): return super().get() + "Z"

Z defines its own version of the method get().

super().get() calls the parent version according to MRO.

Since X comes before Y, super().get() calls X.get(), returning "X".

"Z" is then concatenated.

So the final result becomes:

"X" + "Z" = "XZ"

5. Printing the Result

print(Z().get())

Creates an object of class Z.

Calls the overridden version of get().

Which internally calls X’s get() and adds "Z" to it.

Output becomes:

XZ

Final Output

XZ

 

400 Days Python Coding Challenges with Explanation

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Python Coding challenge - Day 859| What is the output of the following Python Code?

 

Code Explanation:

1. Class Definition

class Num:

This defines a new class named Num.

Objects of this class will store a numeric value and support custom division behavior.

2. Constructor Method (__init__)

    def __init__(self, x):

        self.x = x

__init__ is the constructor that runs when an object is created.

It takes x and stores it inside the object as self.x.

So every Num object holds a number.

3. Operator Overloading (__truediv__)

    def __truediv__(self, other):

        return Num(self.x - other.x)

__truediv__ defines behavior for the division operator /.

But here division is custom, not mathematical division.

When you do n1 / n2, Python calls this method.

Instead of dividing, it subtracts the values:

→ self.x - other.x

It returns a new Num object containing the result.

4. Creating First Object

n1 = Num(10)

Creates an object n1 with x = 10.

5. Creating Second Object

n2 = Num(4)

Creates an object n2 with x = 4.

6. Performing the “Division”

print((n1 / n2).x)

Calls the overloaded division: n1 / n2.

Internally executes __truediv__:

→ 10 - 4 = 6

Returns Num(6).

.x extracts the value, so Python prints:

6

Final Output

6

500 Days Python Coding Challenges with Explanation

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2026 calendar with Calendar Week (CW) in Python

Quick summary: this short script prints a neat month-by-month calendar for a given year with ISO calendar week (CW) numbers in the left column. It uses Python’s built-in calendar and datetime modules, formats weeks so Monday is the first day, and prints each week with its ISO week number.

---

Why this is useful

ISO calendar week numbers are commonly used in project planning, timesheets, and logistics. Having a compact textual calendar that shows the CW next to every week makes it easy to align dates to planning periods.

---

The code (conceptual outline)

Below I explain the main parts of the script step by step. The screenshot of the original code / output is included at the end.

1) Imports and year variable

import calendar
import datetime

clcoding = 2026  # Year

• calendar provides utilities to generate month layouts (weeks → days).

• datetime gives date objects used to compute the ISO week number.

• clcoding is just the chosen variable that holds the year to print — change it to print a different year.

2) Make Monday the first weekday

calendar.setfirstweekday(calendar.MONDAY)

• calendar.monthcalendar() respects whatever first weekday you set. For ISO weeks, Monday-first is the standard, so we set it explicitly.

• Note: ISO week numbers returned by date.isocalendar() also assume Monday-first; these two choices are consistent.

3) Iterate over months

for month in range(1, 13):
    print(f"{calendar.month_name[month]} {clcoding}".center(28))
    print("CW  Mon Tue Wed Thu Fri Sat Sun")

• Loop months 1..12.

• Print the month heading and a header row that shows CW plus Monday→Sunday day names.

• .center(28) centers the header in a ~28-character width for nicer output.

4) Build the month grid and compute CW for each week

for week in calendar.monthcalendar(clcoding, month):
    # Find the first valid date in the week to get the CW
    first_day = next((d for d in week if d != 0), None)
    cw = datetime.date(clcoding, month, first_day).isocalendar()[1]

    days = " ".join(f"{d:>3}" if d != 0 else "   " for d in week)
    print(f"{cw:>3} {days}")
print()

Explain each piece:

• calendar.monthcalendar(year, month) returns a list of weeks; each week is a list of 7 integers. Days that fall outside the month are 0. Example week: [0, 0, 1, 2, 3, 4, 5].

• first_day = next((d for d in week if d != 0), None) picks the first non-zero day in the week. We use that date to determine the ISO week number for the row.

  • That is: if a week contains at least one day of the current month, the week’s week-number is the ISO week of that first valid day.

• datetime.date(year, month, first_day).isocalendar()[1]:

  • .isocalendar() returns a tuple (ISO_year, ISO_week_number, ISO_weekday).

  • We take [1] to get the ISO week number (CW).

  • Important subtlety: ISO week numbers can assign early Jan days to week 52/53 of the previous ISO year and late Dec days to week 1 of the next ISO year. Using isocalendar() on an actual date handles that correctly.

• days = " ".join(f"{d:>3}" if d != 0 else " " for d in week):

  • Formats each day as a 3-character right-aligned field; zeros become three spaces so columns line up.

• print(f"{cw:>3} {days}") prints the CW (right aligned in 3 spaces) and then the seven day columns.

---

Edge cases & notes

• first_day will always be non-None for monthcalendar weeks because each week row returned by monthcalendar covers the month and will include at least one day != 0. (So the None fallback is defensive.)

• ISO week numbers can be 52 or 53 for adjacent years. If you need to show the ISO year too (e.g., when the week belongs to the previous/next ISO year), use .isocalendar()[0] to get that ISO year.

• calendar.setfirstweekday() only affects the layout (which weekday is the first column). ISO week semantics assume Monday-first — so they match here.

• To start weeks on Sunday instead, set calendar.SUNDAY, but ISO week numbers will then visually mismatch ISO semantics (ISO weeks still mean Monday-based weeks), so be careful.

---

Quick customization ideas

• Show ISO year with week: iso_year, iso_week, _ = datetime.date(...).isocalendar() and print f"{iso_year}-{iso_week}".

• Save to file instead of printing: collect lines into a string and write to a .txt or .md.

• Different layout: use calendar.TextCalendar and override formatting if you want prettier text blocks.

• Highlight current date: compare with datetime.date.today() and add a * or color (if terminal supports it).

---

Example output

The script produces month blocks like this (excerpt):

   January 2026
CW  Mon Tue Wed Thu Fri Sat Sun
  1   1   2   3   4
  2   5   6   7   8   9  10  11
  3  12  13  14  15  16  17  18
  4  19  20  21  22  23  24  25
  5  26  27  28  29  30  31

(Each month prints similarly with its calendar week numbers aligned left.)

---

Final thoughts

This is a compact, readable way to generate a full-year calendar annotated with ISO calendar week numbers using only Python’s standard library. It’s easy to adapt for custom output formats, exporting, or including ISO year information when weeks spill across year boundaries

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Neural Networks and Deep Learning

 

Introduction

Deep learning is one of the most powerful branches of AI, enabling systems to learn complex patterns from data by mimicking how the human brain works. The Neural Networks and Deep Learning course on Coursera is the perfect entry point into this field. Taught by Andrew Ng and others from DeepLearning.AI, this course gives learners a solid foundation in neural network basics — from understanding the math behind layers to building deep networks for real applications.

---

Why This Course Matters

• Foundational for Deep Learning: This course teaches the core concepts you will need before diving into more advanced topics like convolutional neural networks (CNNs) or sequence models.

• Expert Instruction: With Andrew Ng as an instructor, the course combines deep expertise with clear teaching, helping learners understand even the tricky mathematical ideas.

• Hands-on Practice: You don’t just watch — you actually implement neural networks using vectorized code, build forward and backward propagation, and train models from scratch.

• Part of a Bigger Path: This is the first course in the Deep Learning Specialization, so it sets you up for follow-up courses on optimization, CNNs, and more.

• Broad Skill Gains: You gain skills in Python programming, calculus, linear algebra, machine learning, and deep learning — all of which are very valuable in data science and AI roles.

---

What You Will Learn

1. Introduction to Deep Learning

You begin by exploring why deep learning has become so prominent. The course covers major trends driving AI, and real-world applications where neural networks make a difference. This gives you a clear picture of how deep learning could fit into your projects or career.

2. Neural Network Basics

In this module, you learn to frame problems with a neural network mindset. You’ll understand how to set up a network, work with vectorized implementations, and use basic building blocks like activations, weights and biases. These basics are essential to start creating effective models.

3. Shallow Neural Networks

Here you build a neural network with a single hidden layer. You study forward propagation (how inputs move through the network) and backpropagation (how errors are used to update weights). By the end, you’ll know how to train a simple neural network on a dataset.

4. Deep Neural Networks

Finally, you scale up: you learn the major computations behind deep learning architectures and build deep networks. You also explore practical issues like initializing parameters, optimizing learning, and understanding how deep networks apply to tasks such as computer vision.

---

Who Should Take This Course

• Intermediate Learners: If you have some programming experience (especially in Python) and want to learn how neural networks work.

• Aspiring AI/ML Engineers: Those who want to build a strong foundation in deep learning before moving to more advanced topics.

• Students & Researchers: Anyone studying machine learning, artificial intelligence, or data science who needs a clear and structured introduction to neural networks.

• Practitioners: Data scientists and engineers who use machine learning and want to move into deep learning for image, text, or other data types.

---

How to Maximize Your Learning

• Follow Along With Coding: Whenever there’s a programming assignment, try to code it yourself. Change things, break things, and learn actively.

• Use a GPU: Training deep neural networks is faster with a GPU — use Google Colab or a GPU machine if possible.

• Visualize Training: Plot loss curves, activation functions, and weights — visualization helps you understand how training is progressing.

• Work on a Small Project: Try applying what you’ve learned to a toy dataset (like MNIST) — build a simple classifier using your own network.

• Review Math: If some linear algebra or calculus concepts are unclear, revisit them — these foundations help you understand how neural networks actually learn.

• Prepare for Next Courses: As part of the Deep Learning Specialization, this course is just the beginning. Use what you learn here to dive deeper in the follow-up courses.

---

What You’ll Walk Away With

• A strong conceptual understanding of what neural networks are and how they work.

• Practical experience building shallow and deep neural networks from scratch.

• Confidence to use forward and backward propagation in your own projects.

• Foundational skills in Python, calculus, and machine learning.

• A Coursera certificate that demonstrates your competence and readiness to tackle more advanced AI courses.

---

Join Now: Neural Networks and Deep Learning

Conclusion

The Neural Networks and Deep Learning course on Coursera is a powerful and accessible entry point into deep learning. Whether you're aiming to build AI applications or simply understand how neural networks function, this course gives you the theory, practice, and confidence to move forward. It’s highly recommended for anyone serious about mastering AI.

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AI Fundamentals with Claude

 

Introduction

AI Fundamentals with Claude is a beginner-friendly Coursera course that’s part of the Real-World AI for Everyone specialization. The course teaches you how to think and work with Claude, Anthropic’s AI assistant, helping you structure prompts, use it as a brainstorming partner, and leverage its power to generate polished content like resumes, plans, and even mock website copy.

---

Why This Course Matters

• Practical AI for Everyone: It’s not just for developers — this course is designed for non-technical professionals who want to use AI in their daily work.

• Learn to Prompt Effectively: One of its core strengths is teaching you how to create context-rich prompts using patterns like “role,” “goal,” and “tone,” so Claude responds in more useful and tailored ways.

• Creativity & Productivity Boost: With Claude as your thinking partner, you can brainstorm ideas, plan projects, or even structure deliverables like websites or reports.

• Responsible AI Use: You gain an understanding of how to structure interactions with Claude thoughtfully, considering human-computer interaction and ethical use.

---

What You’ll Learn

1. Real-World AI

This module introduces practical AI use and helps you set realistic expectations: instead of learning about deep learning architectures, you focus on how Claude can be applied to real, everyday tasks — making it less abstract and more grounded.

2. Understanding Claude

You’ll learn what Claude is, how it “thinks,” and how to communicate with it effectively. The course explains Claude’s capabilities and limitations so you can make better use of it as a personal assistant or collaborator.

3. Document Generation with Claude

This is the heart of the course: designing prompts that turn rough ideas into polished outputs. You’ll use Claude to write professional-grade text, such as resumes or project plans, and learn how to guide it using structured context.

4. Problem-Solving with Claude

In the final part, you learn to use Claude to brainstorm ideas, solve complex problems, and scale your ideation. You practice turning vague or rough concepts into actionable plans — using Claude as a creative partner.

---

Who Should Take This Course

• Professionals & Knowledge Workers: Anyone who writes, plans or strategizes as part of their work (managers, consultants, marketers, product people) will find this useful.

• Non-Technical Learners: You don’t need programming experience — just a willingness to experiment with AI.

• Future AI Users: If you’re curious about how AI assistants can change your work processes, this is a practical, low-barrier entry point.

• Students: Learners who want to build fluency with AI tools like Claude to support their academic or side projects.

---

How to Get the Most Out of It

• Practice Prompting: Try different styles — role-based prompts, goal-based prompts, tone control — and see how Claude’s responses change.

• Use Claude for Real Tasks: Bring Claude into your workflow: draft emails, brainstorm project ideas, or outline proposals.

• Reflect on Output: After Claude generates content, review it critically: what worked? what didn’t? How would you refine your prompt?

• Iterate and Experiment: Don’t settle for the first answer — tweak the prompt, refine context, or add constraints to improve Claude’s response.

• Share and Learn: Join the discussion forum, share your experiments, and learn from how others are using Claude in real-world ways.

---

What You’ll Walk Away With

• Confidence in using Claude for idea generation, writing, and problem-solving.

• A solid understanding of how to structure prompts to get better, more relevant AI responses.

• Skills in using an AI assistant to transform raw ideas into professional deliverables.

• Improved productivity and creative capability by using AI as a thinking partner.

• A certificate from Coursera that shows you’ve mastered foundational AI collaboration skills.

---

Join Now: AI Fundamentals with Claude

Conclusion

AI Fundamentals with Claude is an excellent course for anyone looking to bring AI into their daily life or work — without needing to code or study advanced ML. It empowers you to collaborate with Claude in a way that is structured, effective, and purposeful. Whether you want to write better, brainstorm smarter, or plan more clearly, this course gives you the tools to use Claude as a real partner.

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AI and Machine Learning Essentials with Python Specialization

 

Introduction

Artificial Intelligence and Machine Learning are reshaping industries across the world — but to build powerful models, one needs a strong foundation in both theory and practical skills. The AI & Machine Learning Essentials with Python specialization offers exactly that: a carefully structured learning path, teaching not only how to code ML and AI systems in Python, but also why these systems work. Designed by experts from the University of Pennsylvania, this specialization is ideal for learners who want to understand core principles, build models, and get ready for more advanced AI work.

---

Why This Specialization Is Valuable

• Strong Conceptual Foundation: Beyond coding, the specialization delves into philosophical and theoretical aspects of AI, giving a deeper understanding of why and how intelligent systems work.

• Balanced Curriculum: It doesn’t just focus on machine learning — there are courses on statistics, deep learning, and AI fundamentals to build a well-rounded skillset.

• Hands-On Python Implementation: You’ll implement algorithms and models using Python, making the learning experience practical and applicable to real-world tasks.

• Statistical Literacy: With a dedicated course on statistics, you’ll develop a solid grasp of probability, inference, and their role in ML — which is essential for building reliable models.

• Career-Ready Skills: Whether you want to work in data science, ML engineering, or continue with advanced AI studies, this specialization gives you the building blocks to succeed.

---

What You Will Learn

1. Artificial Intelligence Essentials

You’ll start by exploring the foundations of AI: its history, philosophical questions, and key algorithms. Topics include rational agents, state-space search (like A* and breadth-first search), and how you can model intelligent behavior in Python. This course helps you understand what “intelligence” means in computer systems, and how to simulate simple decision-making agents.

2. Statistics for Data Science Essentials

This course covers the statistical tools that machine learning relies heavily upon. You’ll learn about probability theory, the central limit theorem, confidence intervals, maximum likelihood estimation, and other key concepts. Through Python, you’ll apply these ideas to real data, helping you understand how uncertainty works in data-driven systems.

3. Machine Learning Essentials

Here, you dive into core supervised learning algorithms: linear regression for prediction, logistic regression for classification, and other fundamental techniques. The course explains statistical learning theory (like bias-variance trade-off) and provides hands-on experience building models in Python. You’ll also learn how to evaluate models, tune them, and interpret their behavior.

4. Deep Learning Essentials

In the final part of the specialization, you’ll explore neural networks. The course introduces perceptrons, multilayer neural networks, and backpropagation. You’ll implement a deep learning model in Python, and understand how to preprocess data, train networks, and apply them to real-world tasks. This gives you a practical starting point for more advanced deep learning specialization.

---

Who Should Take It

• Intermediate learners who already know basic Python and want to deeply understand AI and ML fundamentals.

• Aspiring ML engineers or data scientists who need a structured way to learn theory + code.

• Students and researchers who want to build a solid base before tackling advanced topics like reinforcement learning or large-scale systems.

• Professionals in adjacent fields (e.g. software engineers, analysts) who wish to add AI/ML capabilities to their skillset.

---

How to Make the Most of This Specialization

1. Set a realistic study plan: The specialization is estimated to take around 4 months at 8 hours/week. Divide your time across the four courses.

2. Practice in Python: Whenever you learn a new algorithm or concept, code it yourself — experiment with toy datasets and tweak parameters.

3. Apply concepts to real data: Get a publicly available dataset (Kaggle, UCI) and apply your regression, classification, or neural network knowledge to it.

4. Keep a learning journal: Note down key ideas, code snippets, model experiments, and reflections on why certain models perform better.

5. Discuss and network: Join course discussion forums or study groups — explaining what you learned is one of the best ways to deepen your understanding.

6. Plan your next step: Once you finish, decide whether to specialize further in deep learning, NLP, MLOps, or computer vision based on your career goals.

---

What You’ll Walk Away With

• A strong understanding of AI as a field — its goals, challenges, and foundational algorithms.

• Proficiency in statistical thinking and how it supports machine learning models.

• Experience building machine learning models from scratch in Python.

• An introduction to deep learning, plus a working neural network you’ve coded and trained.

• A shareable certificate that demonstrates your commitment and foundational AI/ML knowledge.

• A clear roadmap for where to go next in your AI learning journey.

---

Join Now: AI and Machine Learning Essentials with Python Specialization

Conclusion

The AI & Machine Learning Essentials with Python specialization is a compelling choice for anyone serious about understanding and building intelligent systems. By combining theory, statistical reasoning, and hands-on Python coding, it sets a strong foundation — whether you aim to become an ML engineer, data scientist, or pursue research. If you're ready to invest in your AI education with both depth and practicality, this specialization offers a clear, structured, and powerful learning path.

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Python Coding Challenge - Question with Answer (01201125)

 

Explanation:

x = 0

Initializes the variable x with 0.

x will act as the counter for the loop.

while x < 5:

Starts a while loop that will run as long as x is less than 5.

Loop iterations will occur for x = 0, 1, 2, 3, 4.

if x % 2 == 0: print("E", end='')

Checks if x is even (x % 2 == 0).

If true, prints "E".

end='' ensures that the next output prints on the same line without a newline.

elif x % 2 != 0: print("O", end='')

If the if condition is false, checks if x is odd (x % 2 != 0).

If true, prints "O" on the same line.

x += 1

Increments x by 1 after each iteration.

This moves the loop forward and prevents an infinite loop.

Step-by-Step Execution Table

x Condition     True Output x after increment

0 x % 2 == 0 → True     E     1

1 x % 2 != 0 → Tru e     O     2

2 x % 2 == 0 → True         E     3

3 x % 2 != 0 → True         O     4

4 x % 2 == 0 → True         E     5

Final Output:

EOEOE

Network Engineering with Python: Create Robust, Scalable & Real-World Applications

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Python Coding challenge - Day 858| What is the output of the following Python Code?

 

Code Explanation:

1. Class Definition

class Counter:

A new class named Counter is defined.

It will contain a class variable and a class method.

2. Class Variable

    count = 0

count is a class variable, meaning it belongs to the class, not to any individual object.

It is shared by all instances of the class.

Initial value is set to 0.

3. Class Method Definition

    @classmethod

    def inc(cls):

        cls.count += 1

@classmethod makes inc() a class method.

A class method receives the class itself (cls) instead of an object (self).

cls.count += 1 increases the shared class variable count by 1.

No object creation is needed — this method can be called using the class directly.

4. First Class Method Call

Counter.inc()

Calls the class method inc() using the class name.

This increments count from 0 to 1.

5. Second Class Method Call

Counter.inc()

Calls the method again.

count is incremented from 1 to 2.

6. Printing the Class Variable

print(Counter.count)

Prints the final value of the shared class variable count.

Since inc() was called twice, the value is:

Output:

2

500 Days Python Coding Challenges with Explanation

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Python Coding challenge - Day 857| What is the output of the following Python Code?

 

Code Explanation:

1. Class Definition

class Box:

A new class named Box is created.

Objects of this class will store a number and support a custom subtraction operation.

2. Constructor Method (__init__)

    def __init__(self, n):

        self.n = n

The constructor runs whenever a Box object is created.

It takes one parameter n.

self.n = n stores the value in the object's attribute n.

Each Box object will now hold its own number.

3. Operator Overloading (__sub__)

    def __sub__(self, other):

        return Box(self.n * other.n)

This overrides the - (subtraction) operator.

Instead of subtracting, it multiplies the values (self.n * other.n).

It returns a new Box object containing the result.

Example: b1 - b2 is interpreted as b1.__sub__(b2).

4. Creating the First Object

b1 = Box(2)

A new Box object is created with n = 2.

Internally: b1.n = 2.

5. Creating the Second Object

b2 = Box(5)

Another Box object is created with n = 5.

Internally: b2.n = 5.

6. Using the Overloaded - Operator

print((b1 - b2).n)

b1 - b2 calls the overloaded method:

b1.__sub__(b2)

Inside __sub__:

self.n * other.n → 2 * 5 = 10

A new Box object is created with n = 10.

(b1 - b2).n prints 10.

Final Output

10

400 Days Python Coding Challenges with Explanation

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Network Engineering with Python

 

Create Robust, Scalable & Real-World Applications

A Complete Guide for Modern Network Engineers, Developers & Automation Enthusiasts

---

💡 Build the Network Solutions the Future Runs On

Networks are the backbone of every modern application — and Python is the most powerful tool to automate, secure, and scale those networks.

Whether you’re an aspiring network engineer, a developer transitioning into Infrastructure Automation, or a student building real skills for real jobs…
this book gives you hands-on, production-ready knowledge that actually matters.

Inside, you’ll master Python by building real-world tools, not reading theory.

---

📘 What You’ll Learn

✔️ Network Architecture & Core Protocols

Learn TCP/IP, routing, switching, DNS, DHCP & more — explained the modern way.

✔️ Python Essentials for Network Engineering

From sockets to threading, from APIs to async — everything a network engineer must know.

✔️ Build Real Tools (Step-by-Step)

✓ Network scanners
✓ Packet sniffers
✓ SSH automation
✓ REST API network clients
✓ Log analyzers
✓ Monitoring dashboards
✓ Firewall rule automation
✓ Load balancing concepts
… and much more.

✔️ Automation, APIs, Cloud & DevOps

Master Netmiko, Paramiko, Nornir, RESTCONF, SNMP, Ansible, and cloud networking workflows.

✔️ Production-Ready Best Practices

Error handling, scaling, testing, performance optimization & secure coding patterns.

---

🧠 Who Is This For?

This book is perfect for:

• Network Engineers wanting automation superpowers

• Python Developers entering Infra, DevOps, Cloud

• Students building portfolio projects

• Self-taught learners wanting in-demand, job-ready skills

• Anyone who wants to build scalable network applications

No advanced math. No unnecessary theory.
Just clean, practical, real-world Python.

---

🛠️ What You Get

• Full 𝘀𝘁𝗲𝗽-𝗯𝘆-𝘀𝘁𝗲𝗽 book (PDF + EPUB)

• Downloadable source code for all projects

• CLI, GUI & API-based examples

• Real-world mini-projects you can instantly use in your portfolio

• Lifetime updates

• Commercial-use license

---

⭐ Why CLCODING?

We create books that are simple, practical, and easy to implement — designed for students, working professionals, and self-learners.
Join thousands of learners using CLCODING books to build their tech careers.

---

📈 What You Can Build After Reading This

You will be able to create:

• A complete network scanner

• Device configuration automation system

• Custom packet analyzer

• Network status dashboard

• Cloud networking API scripts

• Firewall & routing automation tools

• Real-time monitoring tools

• Log analyzer with alerting

And you’ll understand exactly how networks work at a deeper level.

---

🔥 Level Up Your Network Engineering Career

Python is the future of networking.
This book shows you how to use it — properly.

Download: Network Engineering with Python

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AI Engineer Agentic Track: The Complete Agent & MCP Course

 

Introduction

Agentic AI is the future of AI systems — intelligent agents that don’t just respond to prompts, but plan, act, collaborate, and persist state. The Udemy course “AI Engineer Agentic Track: The Complete Agent & MCP Course” is built to help engineers, developers, and AI enthusiasts master this paradigm. By working through real-world projects and modern frameworks, the course guides you into designing, building, and deploying autonomous AI agents that are production-ready.

---

Why Agentic AI Matters

Traditional generative AI (like chatbots) is reactive — it answers when prompted, but doesn’t take initiative or maintain long-lived goals. Agentic AI, by contrast, brings autonomy. Agents can perceive, reason, act, and adapt. This shift unlocks powerful new applications: teams of agents working together, agents that talk to tools, and multi-step workflows that run without constant human oversight. Learning how to build such agents can put you at the forefront of the next AI revolution.

---

Course Overview: What You Will Learn

This course is designed as a hands-on, six-week program in which you'll build eight real-world projects using modern agentic AI frameworks and protocols. The key technologies covered include:

• OpenAI Agents SDK

• CrewAI (for multi-agent orchestration)

• LangGraph (for workflow graph design)

• AutoGen (meta-agents that can spawn other agents)

• MCP (Model Context Protocol) — to build scalable, distributed, and tool-integrated agents

By the end of the course, you’ll know how to deploy agents, run multi-agent systems, and architect an autonomous AI environment.

---

Core Concepts

Agentic AI: What It Really Is

Agentic AI refers to systems composed of intelligent agents that can:

1. Perceive their environment (gather data from APIs, memory, or user input)

2. Reason and make plans using LLMs + tool integrations

3. Act by calling tools, triggering actions, or interacting with systems

4. Learn and adapt, maintaining internal memory over time

This architecture is more powerful than traditional LLM usage because agents can execute multi-step goals, coordinate with other agents, and maintain context.

Model Context Protocol (MCP)

A central part of this course is MCP, a protocol that enables LLM agents to interact with external tools, services, or databases through a standard interface. Rather than building custom integrations for each system, MCP makes it easier to scale agents, connect them to new tools, and maintain modularity. 

What You’ll Build: Project Highlights

The course is intensely project-based. Here are some of the eight flagship projects you’ll create:

1. Career Digital Twin

   • Build an agent that represents you—responds, engages, and communicates as a “digital version” of yourself, for example, to potential employers.

2. SDR (Sales) Agent

   • Create an agent that can draft and send professional outreach emails, simulating a sales representative.

3. Deep Research Agent Team

   • Design a team of agents that collectively research a chosen topic, break down sub-tasks, gather information, and synthesize insights.

4. Stock Picker Agent

   • Use CrewAI to build a financial agent that analyzes data and suggests investment opportunities.

5. Engineering Team with CrewAI

   • Deploy a multi-agent engineering system: planner agents, coder agents, tester agents working in Docker to build and test software.

6. Browser Sidekick with LangGraph

   • Build an “Operator Agent” that lives in your browser (via LangGraph), acting as a sidekick and helping you navigate tasks or automate workflows.

7. Agent Creator using AutoGen

   • Build a meta agent that can create other agents (agent factory) using AutoGen, unlocking dynamic, self-replicating agent systems.

8. Capstone: Autonomous Trading Floor

   • Build a trading system where four agents, backed by MCP servers, use dozens of tools to autonomously analyze, decide, and trade — all in a coordinated multi-agent setup.

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Skills and Techniques You’ll Master

Framework Mastery

By working on the projects, you’ll get expert-level exposure to:

• OpenAI Agents SDK: Setting up agents, reasoning, tool integration

• CrewAI: Orchestrating multiple agents to collaborate on tasks

• LangGraph: Defining workflows as graphs, building event-driven logic

• AutoGen: Enabling agents to create other agents, meta-programming

Distributed Agent Execution

Using MCP, you will learn how to deploy agents across multiple servers, enabling:

• Scalable tool interaction

• Persistent memory and state

• Modular, production-level AI systems

Architecture Patterns

• Task decomposition: how to break down goals into sub-tasks

• Agent roles: planner, executor, coordinator

• Memory design: short-term vs long-term memory

• Guardrails and safety: restricting actions, adding oversight

Multi-Agent Strategy

• How to make agents collaborate and communicate

• Role-based agent teams (e.g., research, coding, trading)

• Context handoff between agents

• Error handling and fault tolerance in agent workflows

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Why This Course Is Powerful for Your Career

• Cutting-Edge Relevance: Agentic AI is rapidly becoming mainstream in AI engineering. This course trains you in the exact skills that top companies are seeking.

• Strong Portfolio: By building real-world multi-agent applications, you’ll have concrete demos to showcase.

• Scalable Architectures: Learning MCP means you can design systems that grow — integrate new tools, scale agent compute, and build production workflows.

• Autonomous Agents: You’ll be able to design agents that work independently — reducing manual oversight and increasing efficiency.

• Future-Proof Skillset: As AI moves from single-agent chatbots to ecosystems of agents, you’ll be ready to lead the change.

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Challenges and Considerations

• Complexity: Multi-agent systems are much more complex than simple LLM-based bots. They require careful design, orchestration, and debugging.

• Cost: Running agents, especially with many tools via MCP, could incur API and server costs.

• Security Risks: Agents with powerful tool access can pose security risks. Proper guardrails, authentication, and safe design are essential.

• Performance Management: Ensuring that agents coordinate effectively without getting stuck or performing redundant work is non-trivial.

• Learning Curve: If you’re new to LLMs, Python, or distributed systems, the course’s pace and architectures may feel challenging.

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Who Should Take This Course

• AI Engineers / ML Engineers: Engineers who want to build autonomous agent systems rather than just chatbots.

• Software Developers: Developers interested in combining LLMs with tool execution, workflows, and orchestration.

• Startup Founders / Entrepreneurs: People building AI-first products where agents can automate tasks, workflows, or business logic.

• AI Researchers: Those who want hands-on experience with multi-agent systems, MCP, and emerging agentic patterns.

• Tech Leaders: Architects and product leads who want to understand the next-generation AI architecture to plan for scalable systems.

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The Bigger Picture: Agentic AI Trends

1. Standardization with MCP: The Model Context Protocol (MCP) is becoming a foundational standard for how agents communicate with tools and services, enabling modular and interoperable systems.

2. Security & Governance: As agentic AI systems grow, so do the risks. Research is already focusing on formalizing safety, security, and functional correctness of agentic systems.

3. Multi-Agent Workflows: Instead of a single AI, we’re building teams of agents. These teams can plan, collaborate, and execute complex tasks autonomously.

4. Memory & Learning: Persistent memory systems are central — agents must remember past interactions, learn, and adapt over time to function meaningfully.

5. Production Deployment: With frameworks like CrewAI, AutoGen, and LangGraph, agentic workflows are moving out of the lab and into production environments.

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Join Now: AI Engineer Agentic Track: The Complete Agent & MCP Course

Conclusion

The “AI Engineer Agentic Track: The Complete Agent & MCP Course” is a powerful and forward-looking course for anyone serious about building next-gen AI systems. By blending hands-on projects, cutting-edge frameworks, and distributed architectures, it equips you with the ability to design, deploy, and manage autonomous agents. Whether you're an engineer, researcher, or innovator, this course offers a direct path to master the technology that will define the future of AI.

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Learn Gen AI for Testing: Functional , Automation, Agent AI

 

Introduction

Generative AI (Gen AI) is transforming the software testing landscape by enabling intelligent automation, faster test design, and AI-driven decision-making. The Udemy course “Learn Gen AI for Testing: Functional, Automation, Agent AI” focuses on teaching testers how to apply AI across the entire Software Testing Life Cycle. It’s designed for both beginners and experienced QA professionals who want to upgrade their skills for the future of testing.

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What This Course Covers

This course provides a complete introduction to using Gen AI in requirement analysis, manual test design, test automation, and creating agent-based AI systems. It shows how testers can use AI to generate test cases, write automation scripts, design a framework, and build intelligent assistants. The best part is that it does not require any prior programming or AI knowledge, making it suitable for all testers.

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Importance of This Course

This course is important because it bridges traditional QA practices with modern AI-driven techniques. Instead of relying purely on manual test creation or writing long scripts, testers learn how to work alongside AI to enhance accuracy, speed, and coverage. It also sheds light on how AI tools can be integrated into every phase of testing, helping testers become more efficient and productive.

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AI Across the STLC

A major strength of this course is its coverage of the entire Software Testing Life Cycle. It teaches how to use AI for requirement analysis, test planning, estimation, test design, execution, and reporting. This approach helps testers understand how Gen AI can support both technical and managerial aspects of testing.

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Agent-Based Testing

The course introduces learners to the concept of AI agents—autonomous systems that can interact with browsers, navigate applications, execute workflows, and validate results. Agent-based testing reduces manual effort, increases reliability, and opens the door to advanced automation techniques that go beyond traditional scripting tools.

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RAG (Retrieval-Augmented Generation) for Testing

One of the unique highlights of the course is learning about RAG-based systems. This method uses your own requirement documents or test artifacts to provide more accurate AI outputs. Students learn to build a testing assistant that can “talk to” requirement documents, extract information, and help design better test cases.

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Automation with Selenium and Playwright

Beyond test design, the course also covers how Gen AI can assist in writing Selenium and Playwright automation scripts. Students learn how AI can help construct a complete automation framework by generating reusable code, suggesting improvements, and speeding up scripting tasks.

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Quality Management with AI

For test managers and leads, the course demonstrates how AI can support decision-making activities. This includes using Gen AI for test estimation, resource planning, risk prediction, and generating progress or quality reports. It shows how AI can become a valuable assistant for QA leadership roles.

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Strengths of the Course

The course is beginner-friendly and practical, offering hands-on experience in building real AI tools for testing. It covers a wide range of topics—functional testing, automation, agent-based testing, and test management—making it valuable for any QA professional. It also ensures learners get exposure to modern AI libraries and techniques used in industry.

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Challenges to Consider

Like all AI tools, Gen AI outputs must be validated by humans. AI-generated test cases or scripts may need refinement. Also, AI technologies evolve quickly, requiring ongoing learning. Some AI agents may behave differently with the same input due to non-deterministic behavior. Testers must be aware of these limitations and adapt accordingly.

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Who Should Enroll

This course is ideal for manual testers, automation engineers, QA leads, business analysts, SDETs, and anyone wanting to integrate AI into testing. Whether you're just starting in QA or looking to upgrade your skills to stay competitive, this course provides the knowledge you need to embrace AI-driven testing practices.

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Join Now: Learn Gen AI for Testing: Functional , Automation, Agent AI

Conclusion

The “Learn Gen AI for Testing: Functional, Automation, Agent AI” course is a powerful resource for modern QA professionals. It prepares testers for the future by combining traditional testing skills with the latest AI capabilities. By exploring Gen AI, automation, agents, and RAG systems, learners can significantly enhance their productivity and value in the testing ecosystem.

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