Valentine's Week List 2026 in Python

 

Source Code:

from PIL import Image, ImageDraw, ImageFont

# Canvas size
W, H = 900, 1100
img = Image.new("RGB", (W, H), "#fdfcff")  # soft white background
d = ImageDraw.Draw(img)

# Safe font loader
def font(size):
    try:
        return ImageFont.truetype("arial.ttf", size)
    except:
        return ImageFont.load_default()

title_f = font(46)
text_f  = font(32)

# Title (brighter gradient-style color)
d.text((200, 32), "Valentine's Week List 2026", fill="#6c1ce7", font=title_f)

# Card data with BRIGHT pastel colors
cards = [
    ("Rose Day", "Feb 7",  "rose.jpeg",      "#ffe6ea"),
    ("Propose Day", "Feb 8", "ring.jpeg",    "#e6f0ff"),
    ("Chocolate Day", "Feb 9", "choc.jpeg",  "#ffe9f3"),
    ("Teddy Day", "Feb 10", "teddy.jpeg",    "#e6fff7"),
    ("Promise Day", "Feb 11", "promise.jpeg","#fff0e6"),
    ("Hug Day", "Feb 12", "hug.jpeg",        "#fff6dd"),
    ("Kiss Day", "Feb 13", "kiss.jpeg",      "#f0e9ff"),
    ("Valentine Day", "Feb 14", "heart.jpeg","#e6ffe6")
]

y = 120
for name, date, emoji_file, color in cards:
    # Soft shadow (lighter + realistic)
    d.rounded_rectangle(
        (90, y+8, 830, y+100),
        radius=32,
        fill="#dcdcdc"
    )

    # Card (bright pastel)
    d.rounded_rectangle(
        (80, y, 840, y+92),
        radius=32,
        fill=color
    )

    # Emoji
    emoji = Image.open(emoji_file).convert("RGBA").resize((52, 52))
    img.paste(emoji, (115, y+20), emoji)

    # Text
    d.text((190, y+32), name, fill="#222222", font=text_f)
    d.text((680, y+32), date, fill="#444444", font=text_f)

    y += 115

# Watermark (clean & subtle)
d.text((400, 1055), "Source Code : clcoding.com", fill="#999999", font=text_f)

# Save & show
img.save("valentine_week_BRIGHT.png")
img.show()

Output:

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AI Deep Learning Projects with TensorFlow Specialization

 

Deep learning has evolved from cutting-edge research to a cornerstone technology in industries ranging from healthcare and autonomous vehicles to natural language processing and recommendation systems. But moving from theory to practice — where models are trained, evaluated, optimized, and deployed — can be a steep challenge for learners.

That’s exactly where the AI Deep Learning Projects with TensorFlow Specialization on Coursera shines. This practical, project-based series takes you beyond isolated tutorials and into real AI systems built with TensorFlow — one of the world’s most powerful and widely used deep learning platforms.

Whether you’re an aspiring AI engineer, a data scientist looking to expand into deep learning, or a developer aiming to build intelligent applications, this specialization equips you with hands-on experience solving real problems using neural networks and TensorFlow.

---

Why This Specialization Matters

Many deep learning courses focus purely on theory or predefined “toy” examples. But real-world AI requires more: the ability to design complete solutions — from reading raw data and preprocessing, to training, validating, tuning, and deploying deep models that perform reliably in practice.

This specialization is structured around projects that reflect real tasks and industry needs, giving you not just knowledge, but experience building AI systems that work.

---

What You’ll Learn — In Action

The specialization is organized around a series of project modules, each guiding you through the stages of building, evaluating, and improving deep learning models using TensorFlow.

1. Core TensorFlow Skills for Deep Learning

Before tackling complex tasks, you’ll master essential TensorFlow tools:

• TensorFlow fundamentals and model building

• Custom layers, optimizers, and network configurations

• Efficient data pipelines and preprocessing

• Debugging and visualization during training

These skills form the foundation of every deep learning workflow.

---

2. Image-Based Deep Learning Projects

Images are one of the richest sources of data. You’ll work on projects such as:

• Convolutional Neural Networks (CNNs) for image classification

• Object detection and localization

• Transfer learning with pretrained architectures like MobileNet or ResNet

• Image segmentation for pixel-wise tasks

These projects help you build models that see and interpret visual information — a core capability of modern AI.

---

3. Sequence Modeling with Neural Networks

Many real applications involve sequential data like time series or language. You’ll build systems using:

• Recurrent Neural Networks (RNNs)

• Long Short-Term Memory (LSTM) units

• Gated Recurrent Units (GRUs)

These models allow machines to reason about temporal patterns — powering things like text generation, speech modeling, and predictive analytics over time.

---

4. Natural Language Understanding and Generation

Language is a complex form of data, and TensorFlow’s ecosystem makes it accessible. You’ll build projects involving:

• Tokenization and text embedding

• Sentiment classification

• Language translation or text-summarization workflows

• Attention mechanisms and sequence-to-sequence learning

Working with language data helps unlock AI applications in chatbots, automated content analysis, and more.

---

5. Generative Models and Creative AI

Beyond classification and prediction, the specialization explores generative AI, including:

• Autoencoders for feature learning and reconstruction

• Generative adversarial networks (GANs)

• Synthetic data generation

• Creative content generation tasks

These techniques help you build models that generate data — a rapidly growing area of AI innovation.

---

6. Deployment and Real-World Integration

Building models is only part of the story — deployment matters too. You’ll learn how to:

• Export and save TensorFlow models

• Integrate models into applications (e.g., via REST APIs)

• Use TensorFlow Serving or deployment platforms

• Monitor performance in production environments

This prepares you to take models from experimentation to real-world usage.

---

Tools and Ecosystem You’ll Master

Throughout the specialization, you’ll work with:

• TensorFlow and Keras — for model building and training

• Python — the main language for AI workflows

• Jupyter Notebooks — interactive experimentation environments

• Data preprocessing utilities — handling real datasets

• Visualization tools — to interpret training dynamics

These tools are widely used in industry and research — skills you can carry into your career.

---

Skills You’ll Walk Away With

By completing this specialization, you’ll be able to:

• Build, train, and evaluate deep learning models with TensorFlow

• Apply CNNs to vision tasks and RNNs to sequence data

• Work with text data for language understanding and generation

• Use advanced architectures like transformers and generative models

• Deploy AI models into production-ready systems

• Communicate your process, choices, and performance clearly

These abilities make you job-ready for roles like AI engineer, deep learning specialist, machine learning developer, or data scientist.

---

Who Should Take This Specialization

This specialization is ideal for learners who:

• Already understand basic machine learning concepts

• Want to move into deep learning and AI engineering

• Are building their technical portfolio with real projects

• Seek hands-on experience with industry tools

• Aim to implement deep learning in professional environments

While prior experience with Python and basic ML is helpful, the specialization guides you step by step — meaning motivated beginners can also progress successfully.

---

Join Now: AI Deep Learning Projects with TensorFlow Specialization

Conclusion

The AI Deep Learning Projects with TensorFlow Specialization isn’t just a collection of tutorials — it’s a practical, project-focused learning journey that equips you to tackle real AI problems with real impact.

By building end-to-end systems across images, sequences, text, and generative tasks, you’ll learn more than code — you’ll learn how deep learning solutions are built, evaluated, optimized, and deployed in practice.

If your goal is to become a skilled AI practitioner capable of building production-ready models, this specialization provides a structured and engaging path to get there. By the end, you’ll not only understand deep learning — you’ll have built it.

Deep learning powers tomorrow’s technology — and with this specialization, you can start building it today.

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Machine Learning: an overview

 

Machine learning has become one of the most transformative technologies of the 21st century. It powers recommendation systems, fuels autonomous vehicles, enhances medical diagnostics, and even helps generate creative content. But for many people just starting their AI journey, machine learning can feel overwhelming — filled with complex algorithms, technical jargon, and mathematical concepts.

The Machine Learning: An Overview course on Coursera offers a clear, accessible, and high-level introduction to this fascinating field. Part of the Artificial Intelligence Overview specialization, this course is designed to demystify machine learning for learners from all backgrounds — whether you’re a student exploring future career paths, a professional curious about AI applications, or a business leader seeking to understand how intelligent systems can impact your organization.

---

Why This Course Is Valuable

Machine learning is no longer a niche topic; it’s a core competency in many industries. But before diving into code or advanced models, it’s essential to understand what machine learning really is, how it works at a conceptual level, and why it matters. This course gives you that foundational perspective without overwhelming you with mathematics or programming upfront.

Instead of teaching individual tools or languages, it explains:

• What machine learning does

• How different types of learning work

• Where machine learning is used

• How data drives intelligent systems

• Ethical, social, and practical considerations

This big-picture viewpoint helps you understand the landscape of machine learning before you dive deeper technically.

---

What You’ll Learn

1. What Machine Learning Is — and Isn’t

At its core, machine learning is about teaching computers to learn patterns from data rather than being explicitly programmed for every rule. The course explains:

• The difference between traditional programming and machine learning

• Why machine learning works better for complex, pattern-rich problems

• How learning systems are built around examples

This sets the stage for all future exploration in the field.

---

2. Types of Machine Learning

Machine learning isn’t a single monolithic technique — it consists of different approaches suited to different tasks:

• Supervised Learning: Learning from labeled examples

• Unsupervised Learning: Finding structure in unlabeled data

• Reinforcement Learning: Learning by trial and feedback

The course breaks down these categories with intuitive explanations and real examples, helping you understand when and why each type is used.

---

3. Machine Learning in Everyday Life

One of the most helpful parts of the course is its focus on practical applications. You’ll explore how machine learning shows up in:

• Recommendation systems (movies, products, music)

• Email spam filtering

• Fraud detection in finance

• Predictive maintenance in manufacturing

• Medical diagnosis and imaging

Seeing these real-world use cases helps you connect abstract concepts to concrete value.

---

4. How Models Learn from Data

Without delving deeply into math, the course explains:

• How models are trained using data

• What makes a good dataset

• Why features matter

• The idea of training versus testing

• How models improve with more data

These concepts help you understand the mechanics of learning without getting lost in algorithms.

---

5. Evaluation and Ethics

Machine learning isn’t just about building models — it’s about knowing whether they work and whether their use is responsible. The course covers:

• How performance is evaluated

• Why fairness, bias, and ethics matter

• The risks of relying on automated decisions without oversight

This makes the course especially useful for professionals who need to think critically about deploying AI systems in real contexts.

---

Who Should Take This Course

This course is ideal for learners who want:

• A clear, non-technical introduction to machine learning

• An understanding of how AI and ML fit into business, research, and society

• A foundation before moving into more advanced technical courses

• A conceptual framework to discuss machine learning intelligently

You don’t need programming experience or advanced math — this course is designed so that conceptual clarity comes first.

---

Why a High-Level View Matters

Jumping straight into technical details can be intimidating and confusing. This course gives you the why before the how. It helps you:

• Understand how machine learning relates to data science and AI

• Distinguish between different types of learning systems

• Recognize where machine learning can (and can’t) be used

• Think critically about real-world implications

This perspective is valuable whether you plan to build machine learning systems yourself or collaborate with technical teams.

---

Join Now: Machine Learning: an overview

Conclusion

Machine Learning: An Overview is more than a course — it’s a starting point for understanding one of the most transformative technologies of our time. It equips you with a conceptual framework that makes advanced topics easier to learn later, and it helps you see where machine learning fits into the broader landscape of AI, business, and society.

If you’ve ever wondered how intelligent systems actually learn, how they make predictions, or how they’re used in the real world — this course gives you a clear, approachable, and insightful tour of the fundamentals.

Whether you’re preparing for a career in data science, exploring AI for your business, or simply curious about how machine learning powers the world around you, this course gives you the clarity and confidence to move forward.

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Advanced AI and Machine Learning Techniques and Capstone

 

The field of artificial intelligence and machine learning isn’t just about learning algorithms — it’s about applying them effectively to complex problems and delivering solutions that scale, perform reliably, and create real value. If you’ve already built a foundation in data science and machine learning, the Advanced AI and Machine Learning Techniques and Capstone course on Coursera is designed to take you to the next level.

Part of the Microsoft AI and ML Engineering specialization, this advanced course equips you with high-impact techniques used by AI professionals. It culminates in a capstone project where you integrate everything you’ve learned into a comprehensive solution — bridging theory and practice.

Whether you’re an aspiring machine learning engineer, AI practitioner, or seasoned developer expanding your skillset, this course will deepen your technical expertise and sharpen your problem-solving capabilities.

---

Why Advanced Techniques Matter

Basic models work well for structured, clean datasets. But real-world problems are messy, complex, and require advanced strategies such as:

• Feature engineering and model optimization

• Ensemble learning and boosting

• Deep learning for unstructured data

• Model interpretability and responsible AI practices

• End-to-end solutions with data pipelines and deployment

This course prepares you to handle these challenges confidently — with hands-on experience and practical frameworks.

---

What You’ll Learn

1. Advanced Model Optimization and Tuning

Training a model is only the beginning. To maximize performance, the course teaches you how to:

• Apply hyperparameter tuning (grid search, random search, Bayesian optimization)

• Evaluate models rigorously with cross-validation

• Handle imbalanced data effectively

• Perform feature engineering that improves predictive power

These skills help ensure your AI systems generalize well and perform reliably on new data.

---

2. Deep Learning for Complex Data

Structured tables aren’t the only source of insight. The course covers deep learning techniques for:

• Image data and computer vision

• Sequential data like text or time series

• Neural network architectures (CNNs, RNNs, LSTMs)

• Transfer learning with pretrained models

These topics prepare you for solving tasks where pattern recognition and representation learning matter most.

---

3. Ensemble Methods and Boosting

Advanced methods like Random Forests, Gradient Boosting Machines (e.g., XGBoost, LightGBM), and stacking help you:

• Combine model strengths

• Reduce overfitting

• Improve accuracy and robustness

These methods are widely used in industry competitions and corporate analytics workflows.

---

4. Explainability and Responsible AI

In high-stake domains like healthcare and finance, understanding model behavior is essential. You’ll learn how to:

• Interpret model decisions

• Use tools like SHAP and LIME for explainability

• Address bias and fairness concerns

• Communicate results to stakeholders

These practices aren’t just ethical — they’re increasingly required in regulated industries.

---

5. End-to-End Project Engineering

Beyond models, the course teaches how to build full ML solutions that include:

• Data pipelines and transformation logic

• Feature stores and scalable preprocessing

• Model versioning and tracking

• Deployment using cloud-based or containerized solutions

This ensures your AI systems are production-ready and maintainable.

---

The Capstone Experience

A highlight of the course is the capstone project — a comprehensive, applied challenge where you:

• Define a real data problem

• Build and preprocess datasets

• Select and tune appropriate models

• Evaluate performance against metrics

• Interpret and explain results

• Deploy or present your solution

This capstone is more than an assignment — it’s a portfolio piece you can share with employers or clients.

---

Tools and Technologies You’ll Master

Throughout the course, you’ll work with tools and platforms widely used in industry:

• Python — for model implementation and scripting

• Scikit-Learn, TensorFlow, PyTorch — for classical and deep learning

• Jupyter Notebooks — for interactive development

• Cloud AI/ML services — for scaling and deployment

• Model tracking tools — for experiment management

These tools prepare you for real jobs and real engineering workflows.

---

Who Should Take This Course

This course is ideal for learners who already have:

• A solid foundation in machine learning fundamentals

• Some experience with Python and data analysis

• Familiarity with basic modeling techniques

It’s great for:

• Machine learning engineers

• AI practitioners and developers

• Data scientists aiming for senior roles

• Professionals building AI in production environments

Whether you’re moving into advanced analytics, building intelligent products, or solving complex data problems, this course is an excellent next step.

---

Why This Course Is Worth It

Many people know machine learning at a conceptual level but struggle when faced with real data and production constraints. This course bridges that gap by:

• Deepening your technical competence

• Giving you practical frameworks for complex problems

• Integrating advanced models with software engineering practices

• Providing a tangible, real-world project through the capstone

Instead of isolated exercises, you learn in the context of meaningful, connected workflows — just like an AI engineer does on the job.

---

Join Now: Advanced AI and Machine Learning Techniques and Capstone

Conclusion

The Advanced AI and Machine Learning Techniques and Capstone course on Coursera offers a structured, practical, and career-focused path into advanced data science and AI engineering. By combining sophisticated models, responsible AI practices, deployment strategies, and a comprehensive capstone project, you gain:

• A deeper understanding of advanced machine learning

• Hands-on experience with real AI technologies

• A portfolio piece that demonstrates your capability

• Skills that are directly applicable to industry roles

If you’re ready to go beyond basic tutorials and build AI systems that scale, perform, and deliver impact, this course will take you there.

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Data Science Beyond the Basics (ML+DS) Specialization

 

If you’ve already dipped your toes into data science and feel comfortable with core concepts like Python, basic statistics, and introductory models, you’re ready for the next big step. Enter the Data Science Beyond the Basics (ML+DS) Specialization on Coursera — a focused program designed to take you past the fundamentals and into real-world, machine learning-powered analytics.

This specialization is ideal for learners who want to do more than follow tutorials; it’s for those who want to build, evaluate, optimize, and deploy data-driven solutions that can make an impact in business, research, or technology.

---

Why “Beyond the Basics” Matters

Many resources introduce data science with simple examples like predicting prices or classifying emails. Those are valuable starting points — but real challenges in the field often involve messy data, complex models, careful evaluation, and thoughtful interpretation.

This specialization pushes you beyond entry-level tasks into areas that professionals deal with every day:

• Choosing the right model for a problem

• Handling advanced data preprocessing

• Evaluating models with rigor

• Understanding model assumptions and limitations

• Applying machine learning responsibly and effectively

Instead of teaching what to do step-by-step, this program helps you think like a data scientist.

---

What You’ll Learn

1. Advanced Machine Learning Techniques

You’ll explore a range of more powerful models and methods, including:

• Gradient boosting and ensemble approaches

• Regularization and model complexity control

• Models for structured and unstructured data

• Strategies for reducing overfitting and improving generalization

These techniques help you tackle real data science problems where basic models fall short.

---

2. Data Engineering and Feature Preparation

Good science depends on good data. This specialization dives into:

• Transforming and scaling features

• Encoding categorical variables

• Handling high-dimensional data

• Engineering new features to boost model performance

These skills are essential in practice, where raw datasets rarely come clean or ready to use.

---

3. Model Evaluation and Validation

Superficial accuracy isn’t enough. You’ll learn how to:

• Choose the right evaluation metrics for different tasks

• Use cross-validation and hold-out testing effectively

• Compare models with statistical rigor

• Understand bias-variance trade-offs and diagnostic tools

This makes your models not just functional, but trustworthy in deployment.

---

4. Practical Machine Learning Workflows

Data science is a workflow — not a single step. This specialization teaches you how to:

• Structure pipelines from data cleaning to modeling

• Automate and reproduce analyses

• Use software tools for versioning and collaboration

• Package models for deployment

These workflows are what separate academic examples from industry-ready solutions.

---

5. Real-World Projects and Case Studies

One of the most valuable features of this specialization is hands-on experience. You’ll work with real datasets and real problems such as:

• Predicting business outcomes

• Performing customer segmentation

• Building recommendation systems

• Interpreting and visualizing predictive results

These projects help you build a portfolio of work you can show to employers or collaborators.

---

Tools and Technologies You’ll Use

This specialization teaches with tools widely used in industry and research environments, such as:

• Python — for analysis and modeling

• Pandas and NumPy — for data manipulation

• Scikit-Learn — for classical machine learning

• Visualization libraries — for insights and communication

• Possibly TensorFlow or PyTorch — depending on project depth

These tools give you real, transferable skills that employers value.

---

Who This Specialization Is For

This program is ideal for learners who:

• Already understand basic data science concepts

• Want to build more advanced models confidently

• Seek career growth in analytics, AI, or data engineering

• Are ready to move from tinkering to solving real problems

• Want a structured learning path with project-based experience

It’s perfect for professionals looking to upskill, students preparing for jobs, and anyone who wants to go deeper than surface-level tutorials.

---

Why It’s a Great Next Step

Think of this specialization as the bridge between:

✔ Introductory tutorials that give you understanding
and
✔ Professional-grade skills that let you deliver impact.

Many learners reach a plateau after basic courses — capable of running simple models, but unsure how to handle real challenges like scale, messy data, model selection, evaluation, and deployment. This program helps you cross that gap with structured modules, expert guidance, and practical projects.

---

Join Now: Data Science Beyond the Basics (ML+DS) Specialization

Conclusion

The Data Science Beyond the Basics (ML+DS) Specialization is more than just another online course — it’s a career accelerator. By focusing on advanced techniques, rigorous evaluation, practical workflows, and hands-on projects, it prepares you for real data science work — not just academic examples.

It equips you to:

• Handle complex datasets with confidence

• Choose and tune models for real problems

• Evaluate results responsibly and accurately

• Build workflows that can scale to production

• Present insights that influence decisions

If you’re ready to go beyond tutorials and start building real-world machine learning solutions, this specialization provides a clear, practical, and impactful path forward.

Data science isn’t just about learning — it’s about applying what you learn in ways that matter. This program helps you make that leap.

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Assignment 5(List,Tuple)

 

Part A: Lists

1. Write a Python program to create a list of 6 integers and display the list.

2. Write a program to access and print:

   • the first element

   • the last element
     from a given list using indexing.

3. Write a Python program to take 5 numbers from the user and store them in a list.

4. Write a program to add three new elements to an existing list using the append() method.

5. Given a list of numbers, write a program to remove a specific element entered by the user.

6. Write a program to sort a list of integers in ascending order.

7. Write a program to sort the same list in descending order.

8. Write a Python program to find the maximum and minimum values in a list.

9. Write a program to calculate the sum and average of elements in a list.

10. Write a program to count how many times a given element appears in a list.

---

Part B: Tuples

11. Write a Python program to create a tuple of 5 strings and print it.

12. Write a program to access elements of a tuple using indexing.

13. Write a Python program to demonstrate that tuples are immutable by attempting to modify an element.

14. Given a tuple of integers, write a program to count the occurrence of a specific number.

15. Write a Python program to convert a tuple into a list, add an element, and convert it back into a tuple.

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Python Coding Challenge - Question with Answer (ID -040226)

 

What is really happening?

Step 1 – Initial list

arr = [1, 2, 3]

The list has three values.

---

Step 2 – Loop execution

for i in arr:

Python takes each value one by one and stores it in i.

Iteration	i value	i * 10
1st	1	10
2nd	2	20
3rd	3	30

---

Step 3 – The mistake

i = i * 10

Here you are changing only i, not the list.

i is just a copy, not a reference to arr elements.

So:

• arr[0] stays 1

• arr[1] stays 2

• arr[2] stays 3

---

Final Result

print(arr)

Output:

[1, 2, 3]

---

Memory visualization (easy way)

Think like this:

arr  → [1, 2, 3]
i    → 1 → 10 (dies)
i    → 2 → 20 (dies)
i    → 3 → 30 (dies)

i changes, but arr never changes.

---

Correct way to modify the list

Using index:

for i in range(len(arr)):
    arr[i] = arr[i] * 10

Or Pythonic:

arr = [x * 10 for x in arr]

---

Interview one-liner:

In Python, loop variables hold values, not references for immutable types like integers.

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