x = 5
→ We assign the value 5 to the variable x.
if x > 2:
→ Since x = 5, this condition is True, so we enter the first if block.
Inside that block:
→ This is False because 5 < 4 is not true. So this block is skipped.
Next:
→ This is True because x = 5.
→ So "Exact" gets printed.
.PNG)
Want to build a strong foundation in statistics—the backbone of data science, machine learning, and analytics?
The Statistics 101 course from Cognitive Class, developed by IBM, is the perfect starting point. Whether you're a student, professional, or data enthusiast, this course will help you understand data at a deeper level — and best of all, it’s completely free!
This self-paced, beginner-friendly course is designed to teach you the fundamentals of statistics from the ground up. No prior knowledge required!
🔹 Platform: Cognitive Class (IBM)
🔹 Level: Beginner
🔹 Duration: ~12–15 hours
🔹 Cost: 100% Free
🔹 Certificate: Yes (IBM Verified)
The course offers a clear and concise introduction to key statistical concepts, using simple examples and easy-to-follow lessons.
What is statistics?
Importance of statistics in the real world
Populations vs. samples
Categorical vs. numerical data
Discrete vs. continuous variables
Levels of measurement (nominal, ordinal, interval, ratio)
Measures of central tendency: mean, median, mode
Measures of dispersion: range, variance, standard deviation
Frequency tables and distributions
Histograms, pie charts, box plots
Interpreting visual data
Identifying outliers
Probability theory in simple terms
Events, outcomes, and sample spaces
Basic probability rules
What is inference?
Confidence intervals
Hypothesis testing (conceptual level)
✅ No prior knowledge required
✅ Perfect for data science & analytics beginners
✅ Taught by IBM experts
✅ Includes quizzes & hands-on examples
✅ Earn a free, shareable IBM certificate
At the end of the course, you’ll receive a digital certificate issued by IBM — great for your resume, LinkedIn profile, or student portfolio.
“I finally understand what standard deviation really means. This course made stats simple!”
“A great crash course on the concepts every data professional should know.”
Students new to data science, business, or research
Professionals looking to refresh core statistical concepts
Anyone interested in understanding data better
Sign up for a free account
Enroll and start learning at your own pace!
Whether you're planning to become a data scientist, exploring machine learning, or just want to become more data-literate, statistics is your essential first step.
The Statistics 101 course by Cognitive Class (IBM) is free, accessible, and certified — making it the ideal way to get started.
.PNG)
Are you ready to turn raw data into compelling visual stories?
The Data Visualization with Python course offered by Cognitive Class (an initiative by IBM) is a beginner-friendly, hands-on course that teaches you how to create stunning and insightful visualizations using Python — and it’s completely FREE.
Data visualization is one of the most important skills in data science, analytics, and business intelligence. This course walks you through the fundamentals and advanced techniques using popular Python libraries like Matplotlib, Seaborn, and Folium.
🔹 Platform: Cognitive Class (by IBM)
🔹 Level: Beginner to Intermediate
🔹 Duration: ~15 hours
🔹 Cost: Free
🔹 Certificate: Yes, from IBM
This course is packed with interactive lessons, real datasets, and practical labs to help you visualize data like a pro.
What is data visualization?
Why visualization matters in data science
Types of charts and when to use them
Line plots, bar charts, pie charts, histograms
Plot customization: labels, legends, colors, styles
Creating beautiful statistical plots
Box plots, violin plots, swarm plots
Heatmaps and pair plots
Visualizing geographic data
Plotting location data on maps
Adding markers, choropleths, and popups
Combining multiple plots
Creating storytelling visuals
Best practices for layout and design
Matplotlib – Core plotting library
Seaborn – High-level statistical graphics
Folium – For interactive leaflet maps
Pandas – For data manipulation
Jupyter Notebooks – For hands-on practice
✅ Real-world Datasets – Analyze global economic trends, population, crime stats, and more
✅ Hands-on Labs – Learn by doing inside your browser
✅ No Prior Data Viz Knowledge Needed
✅ Earn a Verified Certificate by IBM
✅ Completely Free
At the end of the course, you can earn an IBM-recognized certificate to showcase your skills on LinkedIn, GitHub, or your portfolio.
"I never thought visualizing data could be this exciting. This course made it simple and fun!"
"Now I can create compelling charts and dashboards for my reports at work. Thank you, IBM!"
Beginners in data science or analytics
Business analysts looking to improve presentations
Students and professionals curious about data storytelling
🎯 Visit the course page:
👉 https://cognitiveclass.ai/courses/data-visualization-python
🆓 Sign up with a free account and start learning instantly!
In the era of data overload, the ability to tell clear, concise, and compelling visual stories is a superpower.
The Data Visualization with Python course by IBM via Cognitive Class is the perfect first step toward mastering this skill — whether you're in business, data science, or just curious.
It’s interactive, hands-on, project-based, and 100% free.
.PNG)
Want to master data analysis using Python? Whether you're an aspiring data analyst, data scientist, or simply curious about data-driven decision making, the Data Analysis with Python course by Cognitive Class (by IBM) is a must-try — and it’s 100% FREE!
This self-paced course focuses on teaching you how to analyze data using the most popular Python libraries — like Pandas, Numpy, and Scipy — and visualize data using Matplotlib, Seaborn, and Folium.
🔹 Platform: Cognitive Class (by IBM)
🔹 Difficulty: Intermediate
🔹 Duration: ~20 hours
🔹 Cost: Free
🔹 Certificate: Yes, from IBM
The course is well-structured into several modules that walk you through everything from importing data to building regression models.
Reading data from different file types (CSV, Excel, SQL)
Exploring and cleaning datasets using Pandas
Identifying and handling missing values
Data formatting and normalization
Binning and indicator variables
Grouping, pivoting, and summarizing data
Detecting outliers and understanding distributions
Using boxplots, histograms, and scatter plots
Introduction to machine learning models
Linear regression and multiple regression
Model evaluation metrics (MAE, MSE, R²)
Splitting data into training and testing sets
Cross-validation and ridge regression
Refining models for better accuracy
Pandas – For data manipulation
NumPy – For numerical operations
Matplotlib & Seaborn – For visualization
Scikit-learn – For model building
Statsmodels, Folium, and more
Complete the course and receive a verified certificate by IBM – a great way to validate your skills and showcase them on LinkedIn or your resume.
✅ Practical & Project-Based
✅ Hands-on Labs with Jupyter Notebooks
✅ Real-world datasets used in analysis
✅ Recognized IBM certificate
✅ Absolutely Free! No hidden costs
“I finally understand how to clean, analyze, and visualize data. The concepts were easy to grasp with examples that felt real.”
“This course helped me get my first freelance data analysis project!”
Data science and AI beginners
Business analysts & engineers
Anyone who has basic Python knowledge and wants to apply it to real-world datasets
👉 Visit: https://cognitiveclass.ai/courses/data-analysis-python
👉 Sign up for a free account
👉 Enroll and start learning today!
Data is the new oil, and this course teaches you how to refine it!
Whether you're aiming for a career in data science, improving your current job skills, or just exploring the data world out of curiosity, the Data Analysis with Python course is a perfect launchpad.
Offered by IBM, backed by practical labs, and 100% free — there’s no reason to wait.
.PNG)
Are you looking to kickstart your Data Science journey with Python?
Look no further! The Python for Data Science course by Cognitive Class, an IBM initiative, is one of the best free learning resources available for beginners and aspiring data scientists.
This self-paced course introduces you to the fundamentals of Python programming with a strong emphasis on its application in data science.
🔹 Platform: Cognitive Class (by IBM)
🔹 Level: Beginner
🔹 Duration: ~15 hours
🔹 Cost: FREE
🔹 Certificate: Yes, after completion
The course is thoughtfully divided into five modules, each building a strong foundation for data science applications using Python:
Why Python is popular in Data Science
Installation and setup (Anaconda, Jupyter Notebooks)
Writing your first Python program
Variables and data types
Expressions and operators
String operations
Working with lists, tuples, and dictionaries
Creating and modifying lists and dictionaries
Nesting and indexing
Practical use cases in Data Science
Reading and writing files
Introduction to Pandas for data manipulation
Loading datasets, filtering, and summarizing data
Using Matplotlib and Seaborn
Creating line plots, bar charts, scatter plots
Visualizing real-world datasets
✅ Beginner-Friendly: No prior programming experience required
✅ Hands-On Labs: Learn by doing with Jupyter Notebooks
✅ Real-World Examples: Practical applications of Python in data analysis
✅ Free Certification: Great for your resume and LinkedIn
✅ Offered by IBM: Recognized and trusted globally
Upon passing the quizzes and final exam, you’ll receive a verified certificate from IBM through Cognitive Class — a valuable addition to your data science portfolio.
"This is the perfect course to start your Python and Data Science journey. Everything is clearly explained, and the labs make learning fun!"
"Thanks to this course, I cracked my first Data Analyst internship."
Visit: https://cognitiveclass.ai/courses/python-for-data-science
Sign up for a free account
Enroll and start learning at your own pace
The Python for Data Science course by IBM’s Cognitive Class is more than just an introduction — it’s your first real step into the world of data analysis, machine learning, and artificial intelligence.
Whether you’re a student, professional, or curious learner, this course will give you the confidence to code with Python and explore the fascinating field of Data Science.
🔗 Start learning now:
👉 https://cognitiveclass.ai/courses/python-for-data-science
This means i will go through the numbers:
1, 2, 3, 4, 5
1 % 2 == 0 → ❌ False → does not continue
i == 5 → ❌ False → does not break
✅ print(1, end=" ") → prints 1
2 % 2 == 0 → ✅ True → continue (skips the rest of loop)
❌ Nothing is printed.
3 % 2 == 0 → ❌ False
i == 5 → ❌ False
✅ print(3, end=" ") → prints 3
4 % 2 == 0 → ✅ True → continue
❌ Nothing is printed.
5 % 2 == 0 → ❌ False
i == 5 → ✅ True → break
Loop stops before printing 5
Skips even numbers using continue
Stops the loop when i is 5 using break
Only odd numbers less than 5 get printed
.png)
.png)
Welcome to the first course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will learn how to build foundational Cloud computing infrastructure, including websites involving serverless technology and virtual machines. You will also learn how to apply Agile software development techniques to projects which will be useful in building portfolio projects and global-scale Cloud infrastructures.
This course is ideal for beginners as well as intermediate students interested in applying Cloud computing to data science, machine learning and data engineering. Students should have beginner level Linux and intermediate level Python skills. For your project in this course, you will build a statically hosted website using the Hugo framework, AWS Code Pipelines, AWS S3 and GitHub.
Explain the business case for responsible AI.
Identify ethical considerations with AI using issue spotting best practices.
Describe how Google developed and put their AI Principles into practice and leverage their lessons learned.
Adopt a framework for how to operationalize responsible AI in your organization.
.png)
[] is an empty list.
for i in []: means "loop over each item in the list".
But since the list is empty, there are no items to iterate over.
So, the loop body (print("Hello")) never executes.
Nothing is printed.
No errors occur.
The loop just skips execution entirely.
The code runs silently and produces no output.
%20by%20Allen%20B.%20Downey.jpg)
A Practical Guide to Probability and Statistics for Programmers and Data Scientists
Think Stats, 3rd Edition is not your typical statistics textbook. Written by Allen B. Downey—respected computer scientist and educator—the book provides a practical, programming-first introduction to statistical thinking using Python.
This edition is thoroughly updated for Python 3.10+, Pandas, and modern data analysis practices, making it a go-to resource for beginners looking to master statistics from the ground up using real-world data.
Beginner to intermediate Python programmers
Aspiring data scientists
Students pursuing statistics or machine learning
Readers who prefer hands-on learning through code over theory-heavy books
Unlike traditional stat books, Think Stats introduces concepts through Python code. You’ll learn to:
Clean, explore, and visualize data using Pandas and Matplotlib
Understand distributions, probability, and hypothesis testing
Model real-world phenomena through simulation
Each chapter uses authentic datasets, such as:
The National Survey of Family Growth (NSFG)
Other large-scale, publicly available survey data
This approach makes learning engaging and relevant, with exercises rooted in reality.
The book covers:
Descriptive statistics
Probability distributions
Statistical testing (t-tests, p-values)
Estimation and confidence intervals
Regression analysis
Bayesian inference basics
Think Stats is freely available under a Creative Commons license. That means:
You can read it online or download the PDF
You can reproduce and modify the code and exercises
Perfect for self-learners or educators
Updated for modern Python environments
Refined examples with clearer, cleaner code
Integration with the EmpiricalDist module for probability distributions
Expanded exercises to reinforce each concept
Improved explanations for readers without a math background
If you want to:
Understand statistics by writing code
Build intuition about probability, distributions, and statistical testing
Use Python as a tool to explore and communicate data stories
…then Think Stats is your ideal companion.
It’s less about formulas and more about thinking statistically in real-world situations.
“The best way to learn statistics is to do statistics.”
And that’s the motto Think Stats lives by.
⭐️⭐️⭐️⭐️⭐️ (5/5)
Think Stats (3rd Edition) is accessible, code-driven, and practical—a perfect gateway for anyone interested in data science, especially those with a programming mindset.
Whether you're prepping for a data science interview or exploring statistics for fun, this book will give you the foundation to think statistically with confidence.
By Joseph Babcock & Raghav Bali
Rating: ⭐⭐⭐⭐⭐ (5/5)
Ideal For: Data scientists, machine learning engineers, and developers diving into the world of LLMs, GANs, VAEs, and diffusion models.
Generative AI with Python and PyTorch (2nd Edition) is a comprehensive and hands-on guide for anyone looking to master the latest in generative artificial intelligence. Whether you're exploring text generation with LLMs or creating images using GANs and diffusion models, this book equips you with the tools, theory, and practical skills to bring your AI ideas to life.
Authored by Joseph Babcock, a machine learning PhD, and Raghav Bali, a seasoned data scientist with patents in AI, the book reflects their real-world experience, blending technical depth with application-first learning.
This book dives deep into cutting-edge GenAI topics, offering clarity on:
LLMs and Transformers: Understand how models like GPT-4 and Llama revolutionize NLP.
Prompt Engineering: Learn advanced techniques like ReAct, Chain-of-Thought, and Prompt Query Language.
Diffusion Models & AI Art: Go beyond GANs and generate images using state-of-the-art methods like CLIP and Stable Diffusion.
LLM Optimization: Apply LoRA, PEFT, and RLHF to fine-tune and optimize large models.
Tooling Up: Build powerful pipelines using LangChain, RAG, and LlamaIndex for real-world deployment.
📌 Introduction to Generative AI: A solid grounding in how generative models work.
🧱 Building Blocks of Deep Neural Networks: A must-read refresher or starter.
✍️ Text Generation with LLMs: Covers both classical LSTMs and state-of-the-art transformers.
🔗 LLM Toolbox: Explore GPT-4, LangChain, RAG, and more.
🎨 Image Generation: Includes hands-on with GANs, VAEs, style transfer, and even deepfakes.
🎭 Diffusion & CLIP Models: Stay on the edge of visual AI innovation.
Real-world applications: Projects and case studies make theory directly actionable.
Up-to-date content: Covers GPT-4, Llama, Mistral, and modern ecosystem tools.
Balanced depth: From conceptual foundations to hands-on coding, it’s both deep and digestible.
Bonus: Buy the print or Kindle version and get a free PDF eBook.
If you're a:
Python-savvy data scientist aiming to break into GenAI,
ML engineer ready to scale LLM projects,
Developer curious about building tools with LangChain or LlamaIndex,
…this book is an essential addition to your AI shelf.
📝 Note: A basic understanding of Python and machine learning is required.
In a rapidly evolving AI landscape, this book is your North Star for mastering GenAI in practice. It's not just about learning models—it's about building them, optimizing them, and deploying them to solve real problems.
Whether you're generating text, crafting art, or pushing the frontier of AI applications, Generative AI with Python and PyTorch is your hands-on playbook.
Available in both print and Kindle editions—with free PDF included.
Perfect for your 2025 learning roadmap.
Let’s navigate the GenAI frontier—one line of PyTorch at a time.
Written by CLCODING – Helping you decode the future of AI and Python.
.png)
.png)
Artificial Intelligence (AI) is transforming the world at an unprecedented pace. From healthcare to finance, transportation to entertainment, AI systems are increasingly embedded in our daily lives, automating complex tasks, enhancing decision-making, and creating new opportunities. However, as AI's influence grows, so do concerns about its ethical implications, fairness, transparency, and impact on society. This is where Responsible AI comes into play.
Responsible AI refers to the design, development, and deployment of AI systems in a way that is ethical, transparent, fair, accountable, and aligned with human values. It emphasizes ensuring that AI technologies serve humanity positively without causing harm or perpetuating biases.
Unlike simply focusing on technological advancements or performance metrics, Responsible AI considers the broader societal, legal, and moral implications of AI systems.
Mitigating Bias and Discrimination
AI models learn from data, and if the data contains historical biases, AI systems may unintentionally perpetuate or amplify discrimination. This can lead to unfair treatment in hiring, lending, policing, and more.
Ensuring Transparency and Explainability
Many AI models, especially deep learning, operate as “black boxes,” making decisions difficult to interpret. Lack of transparency can erode trust and prevent users from understanding how decisions are made.
Protecting Privacy and Security
AI often relies on vast amounts of personal data. Responsible AI ensures that data privacy is respected and that AI systems are secure from malicious attacks.
Accountability and Governance
When AI causes harm or fails, it’s critical to have mechanisms to hold developers, deployers, and organizations accountable and to govern AI use responsibly.
Building Trust with Users and Society
AI systems that are fair, transparent, and respect human rights foster greater public trust and wider adoption.
Several frameworks and organizations have proposed principles to guide responsible AI. The most commonly referenced pillars include:
1. Fairness
AI should avoid bias and discrimination. It must provide equitable outcomes across different groups regardless of race, gender, age, or other protected characteristics.
2. Transparency
Users should understand how AI decisions are made. Clear documentation, explainable models, and open communication are essential.
3. Accountability
Developers and organizations should be answerable for AI outcomes. This includes having clear lines of responsibility and processes for auditing and redress.
4. Privacy and Security
AI systems must protect user data, comply with privacy laws, and safeguard against unauthorized access or misuse.
5. Safety and Reliability
AI should operate safely and robustly in real-world conditions without causing unintended harm.
6. Human-Centeredness
AI should augment human capabilities, respect human rights, and allow for human oversight.
Despite the ideals, implementing Responsible AI faces challenges such as:
Complexity of AI models making explainability difficult.
Data limitations, including bias in training data or lack of diverse datasets.
Balancing innovation with regulation, where overly strict rules may stifle AI advancement.
Global diversity in ethical standards complicating universal guidelines.
Establish Ethical AI Guidelines
Create clear policies defining what responsible AI means for your organization and align with global best practices.
Conduct Impact Assessments
Evaluate potential risks and impacts of AI systems before deployment, including fairness audits and privacy reviews.
Ensure Diverse and Inclusive Data
Collect and curate datasets representing diverse populations to reduce bias.
Invest in Explainability Tools
Use AI models and tools that provide interpretable outputs to enhance transparency.
Promote Human Oversight
Design AI workflows that allow humans to review, override, or audit AI decisions.
Continuous Monitoring and Governance
Implement ongoing monitoring to detect issues and adapt AI systems responsibly over time.
Educate and Train Teams
Ensure that developers, managers, and stakeholders understand responsible AI principles and ethics.
As AI continues to evolve, Responsible AI will become even more critical. Emerging technologies such as generative AI, autonomous systems, and AI-powered decision-making bring new ethical considerations. Collaboration between governments, industry, academia, and civil society will be essential to shape policies and standards that ensure AI benefits all.
By committing to Responsible AI today, we lay the foundation for a future where AI is a force for good—empowering individuals, promoting fairness, and enriching society.
Responsible AI is not just a technical challenge; it is a societal imperative. By embedding ethics, fairness, transparency, and accountability into AI development and deployment, we can harness AI’s transformative power while safeguarding human values and rights. Whether you are a developer, policymaker, or user, understanding and advocating for Responsible AI is key to building a trustworthy and inclusive AI-powered world.
.png)
.png)
%20by%20Allen%20B.%20Downey.jpg)
.png)
%20by%20Allen%20B.%20Downey.jpg)
.png)
Posts
All Comments
