Which of the following are valid return statements?

 

Which of the following are valid return statements?

return (a, b, c)

return a + b + c

return a, b, c

All three of the provided return statements are valid in Python, but they have different implications:

return (a, b, c): This returns a single tuple containing the values of a, b, and c. For example, if a = 1, b = 2, and c = 3, the function would return the tuple (1, 2, 3).

return a + b + c: This returns the sum of a, b, and c. If a = 1, b = 2, and c = 3, the function would return the value 6.

return a, b, c: This returns a tuple containing the values of a, b, and c. This is a more concise way of expressing the first option. For the same example values, it would return the tuple (1, 2, 3).

Choose the one that fits your specific use case and the desired return value format in your function.

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Python Coding challenge - Day 113 | What is the output of the following Python Code?

 

The code you provided is attempting to generate a Fibonacci sequence and append the values to the list lst. However, it seems like there's a small mistake in your code. The range in the list comprehension should go up to 4, not 5, to generate the Fibonacci sequence up to the 4th element. Here's the corrected code:

lst = [0, 1]

[lst.append(lst[k - 1] + lst[k - 2]) for k in range(2, 5)]

print(lst)

After running this code, lst will be:

[0, 1, 1, 2, 3]

This list represents the Fibonacci sequence up to the 4th element. The initial list [0, 1] is extended by three more elements generated by adding the last two elements of the list to get the next one.

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How to write program that generates a list of integer coordinates for all points in the first quadrant from (1, 1) to (5, 5) using list comprehension?

 

You can use list comprehension to generate a list of integer coordinates for all points in the first quadrant from (1, 1) to (5, 5). Here's an example in Python:

coordinates = [(x, y) for x in range(1, 6) for y in range(1, 6)]

print(coordinates)

This code will produce a list of tuples where each tuple represents a coordinate in the first quadrant, ranging from (1, 1) to (5, 5). The range(1, 6) is used to include values from 1 to 5 (inclusive) for both x and y.

After running this code, coordinates will contain the following list:

[(1, 1), (1, 2), (1, 3), (1, 4), (1, 5),  (2, 1), (2, 2), (2, 3), (2, 4), (2, 5),  (3, 1), (3, 2), (3, 3), (3, 4), (3, 5),  (4, 1), (4, 2), (4, 3), (4, 4), (4, 5),  (5, 1), (5, 2), (5, 3), (5, 4), (5, 5)]

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Python Coding challenge - Day 112 | What is the output of the following Python Code?

 

Code :

x = 15

y = 10

result = x if x < y else y

print(result)

Solution and Explanation: 

Let's break down the code step by step:

Variable Initialization:

x = 15

y = 10

Two variables, x and y, are initialized with the values 15 and 10, respectively.

Conditional Expression:

result = x if x < y else y

This line uses a conditional expression. The syntax is a if condition else b, meaning if the condition is true, the value of a is assigned to the variable; otherwise, the value of b is assigned. In this case, it's checking whether x is less than y. If it is true, result will be assigned the value of x, otherwise, it will be assigned the value of y.

Printing the Result:

print(result)

Finally, the code prints the value of result.

Execution:

In this specific example, x (15) is not less than y (10). Therefore, the conditional expression evaluates to y, and the value 10 is assigned to result. The print(result) statement then outputs 10 to the console.

In summary, the code compares the values of x and y and assigns the smaller value to the variable result, which is then printed. In this particular case, the output will be 10 because y is smaller than x.

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50 Algorithms Every Programmer Should Know

 

Delve into the realm of generative AI and large language models (LLMs) while exploring modern deep learning techniques, including LSTMs, GRUs, RNNs with new chapters included in this 50% new edition overhaul

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features

• Familiarize yourself with advanced deep learning architectures
• Explore newer topics, such as handling hidden bias in data and algorithm explainability
• Get to grips with different programming algorithms and choose the right data structures for their optimal implementation

Book Description

The ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works.

You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them.

Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use.

You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT.

Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks.

By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.

What you will learn

• Design algorithms for solving complex problems
• Become familiar with neural networks and deep learning techniques
• Explore existing data structures and algorithms found in Python libraries
• Implement graph algorithms for fraud detection using network analysis
• Delve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examples
• Create a recommendation engine that suggests relevant movies to subscribers
• Grasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMs

Who this book is for

This computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code.

Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful.

Python programming experience is a must, knowledge of data science will be helpful but not necessary.

Table of Contents

1. Core Algorithms
2. Data Structures
3. Sorting and Searching Algorithms
4. Designing Algorithms
5. Graph Algorithms
6. Unsupervised Machine Learning Algorithms
7. Supervised Learning Algorithms
8. Neural Network Algorithms
9. Natural Language Processing
10. Sequential Models
11. Advanced Machine Learning Models
12. Recommendation Engines
13. Algorithmic Strategies for Data Handling
14. Large-Scale Algorithms
15. Evaluating Algorithmic Solutions
16. Practical Considerations

Hard Copy : 50 Algorithms Every Programmer Should Know: Tackle computer science challenges with classic to modern algorithms in machine learning, software design, data systems, and cryptography

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How much do you know about Modules and packages in python?

 

a. A function can belong to a module and the module can belong to a

package.

Answer

True

b. A package can contain one or more modules in it.

Answer

True

c. Nested packages are allowed.

Answer

True

d. Contents of sys.path variable cannot be modified.

Answer

False

e. In the statement import a.b.c, c cannot be a function.

Answer

True

f. It is a good idea to use * to import all the functions/classes defined in a

module.

Answer

True

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PostgreSQL for Everybody Specialization

 

What you'll learn

How to use the PostgreSQL database effectively

Explore database design principles

Dive into database architecture and deployment strategies

Compare and contrast SQL and NoSQL database design approaches and acquire skills applicable to data mining and application development

Join Free: PostgreSQL for Everybody Specialization

Specialization - 4 course series

Across these four courses, you’ll learn how to use the PostgreSQL database and explore topics ranging from database design to database architecture and deployment. You’ll also compare and contrast SQL and NoSQL approaches to database design. The skills in this course will be useful to learners doing data mining or application development.

Applied Learning Project

This course series utilizes a custom autograding environment for an authentic set of graded and practice assignments, including: creating and manipulating tables, designing data models, constructing advanced queries, techniques for working with text in databases, including regular expressions, and more.

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Python Data Structures

 

What you'll learn

Explain the principles of data structures & how they are used

Create programs that are able to read and write data from files

Store data as key/value pairs using Python dictionaries

Accomplish multi-step tasks like sorting or looping using tuples

Join Free: Python Data Structures

There are 7 modules in this course

This course will introduce the core data structures of the Python programming language. We will move past the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform increasingly complex data analysis. This course will cover Chapters 6-10 of the textbook “Python for Everybody”.  This course covers Python 3.

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Web Applications for Everybody Specialization

 

What you'll learn

Installing your development environment

Developing a database application with PHP and MySQL

Using JavaScript to interact with a PHP web app

Modeling many-to-many relationships 

Join Free: Web Applications for Everybody Specialization

Specialization - 4 course series

This Specialization is an introduction to building web applications for anybody who already has a basic understanding of responsive web design with JavaScript,  HTML, and CSS. Web Applications for Everybody is your introduction to web application development. You will develop web and database applications in PHP, using SQL for database creation, as well as functionality in JavaScript, jQuery, and JSON.

Over the course of this Specialization, you will create several web apps to add to your developer portfolio. This Specialization (and its prerequisites) will prepare you, even if you have little to no experience in programming or technology, for entry level web developer jobs in PHP.

You’ll demonstrate basic concepts, like database design, while working on assignments that require the development of increasing challenging web apps. From installing a text editor to understanding how a web browser interacts with a web server to handling events with JQuery, you’ll gain a complete introductory overview of web application development.

Applied Learning Project

The courses in this specialization feature assignments requiring development of increasingly challenging web sites, to demonstrate basic concepts as they are introduced.  The projects will demonstrate the students skills in HTML, CSS, PHP, SQL, and JavaScript.

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Generative AI Essentials: Overview and Impact

 

What you'll learn

Learn how generative AI works

Explore the benefits and drawbacks of generative AI

Learn how generative AI can integrate into our daily lives

Join Free: Generative AI Essentials: Overview and Impact

There is 1 module in this course

With the rise of generative artificial intelligence, there has been a growing demand to explore how to use these powerful tools not only in our work but also in our day-to-day lives. Generative AI Essentials: Overview and Impact introduces learners to large language models and generative AI tools, like ChatGPT. In this course, you’ll explore generative AI essentials, how to ethically use artificial intelligence, its implications for authorship, and what regulations for generative AI could look like. This course brings together University of Michigan experts on communication technology, the economy, artificial intelligence, natural language processing, architecture, and law to discuss the impacts of generative AI on our current society and its implications for the future.

This course is licensed CC BY-SA 4.0 with the exclusion of the course image.

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Python 3 Programming Specialization

What you'll learn

Learn Python 3 basics, from the basics to more advanced concepts like lists and functions.

Practice and become skilled at solving problems and fixing errors in your code.

Gain the ability to write programs that fetch data from internet APIs and extract useful information.

Join Free: Python 3 Programming Specialization

Specialization - 5 course series

This specialization teaches the fundamentals of programming in Python 3. We will begin at the beginning, with variables, conditionals, and loops, and get to some intermediate material like keyword parameters, list comprehensions, lambda expressions, and class inheritance.

You will have lots of opportunities to practice. You will also learn ways to reason about program execution, so that it is no longer mysterious and you are able to debug programs when they don’t work.

By the end of the specialization, you’ll be writing programs that query Internet APIs for data and extract useful information from them.  And you’ll be able to learn to use new modules and APIs on your own by reading the documentation. That will give you a great launch toward being an independent Python programmer.

This specialization is a good next step for you if you have completed 

Python for Everybody but want a more in-depth treatment of Python fundamentals and more practice, so that you can proceed with confidence to specializations like 

Applied Data Science with Python

But it is also appropriate as a first set of courses in Python if you are already familiar with some other programming language, or if you are up for the challenge of diving in head-first.

Applied Learning Project

By the end of the second course, you will create a simple sentiment analyzer that counts the number of positive and negative words in tweets. In the third course, you will mash up two APIs to create a movie recommender. The final course, Python Project: pillow, tesseract, and opencv (Course 5), is an extended project in which you'll perform optical character recognition (OCR) and object detection in images.

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Python Coding challenge - Day 111 | What is the output of the following Python Code?

 

Code : 

complex_num = 4 + 3j

print(abs(complex_num))

Solution and Explanantion:

The above code calculates the absolute value (magnitude) of a complex number and prints the result. In this case, the complex number is 4 + 3j. The absolute value of a complex number is given by the square root of the sum of the squares of its real and imaginary parts.

Let's break it down:

complex_num = 4 + 3j

This line creates a complex number with a real part of 4 and an imaginary part of 3.

print(abs(complex_num))

This line calculates the absolute value of the complex number using the abs function and then prints the result. The output will be:

5.0

So, the absolute value of the complex number 4 + 3j is 5.0.

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Applied Data Science with Python Specialization

 

What you'll learn

Conduct an inferential statistical analysis

Discern whether a data visualization is good or bad

Enhance a data analysis with applied machine learning

Analyze the connectivity of a social network

Join Free: Applied Data Science with Python Specialization

Specialization - 5 course series

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.

Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization.  After completing those, courses 4 and 5 can be taken in any order.  All 5 are required to earn a certificate.

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Introduction to AI in the Data Center

 

What you'll learn

What is AI and AI use cases, Machine Learning, Deep Leaning, and how training and inference happen in a Deep Learning Workflow.

The history and architecture of GPUs,  how they differ from CPUs, and how they are revolutionizing AI.    

Become familiar with deep learning frameworks, AI software stack, and considerations when deploying AI workloads on a data center on prem or cloud.

Requirements for multi-system AI clusters and considerations for infrustructure planning, including servers, networking, storage and tools. 

Join Free: Introduction to AI in the Data Center

There are 4 modules in this course

Welcome to the Introduction to AI in the Data Center Course!

As you know, Artificial Intelligence, or AI, is transforming society in many ways. 

From speech recognition to improved supply chain management, AI technology provides enterprises with the compute power, tools, and algorithms their teams need to do their life’s work. 

But how does AI work in a Data Center? What hardware and software infrastructure are needed? 

These are some of the questions that this course will help you address. 

This course will cover an introduction to concepts and terminology that will help you start the journey to AI and GPU computing in the data center. 

You will learn about:

* AI and AI use cases, Machine Learning, Deep Learning, and how training and inference happen in a Deep Learning Workflow. 

* The history and architecture of GPUs,  how they differ from CPUs, and how they are revolutionizing AI.

* Deep learning frameworks, AI software stack, and considerations when deploying AI workloads on a data center on prem, in the cloud, on a hybrid model, or on a multi-cloud environment. ​ 

* Requirements for multi-system AI clusters​​, considerations for infrastructure planning, including servers, networking, and storage and tools for cluster management, monitoring and orchestration. 

This course is part of the preparation material for the NVIDIA Certified Associate - ”AI in the Data Center” certification. 

This certification will take your expertise to the next level and support your professional development.

Who should take this course?

* IT Professionals

* System and Network Administrators

* DevOps

* Data Center Professionals

No prior experience required.

This is an introduction course to AI and GPU computing in the data center. 

To learn more about NVIDIA’s certification program, visit: 

https://academy.nvidia.com/en/nvidia-certified-associate-data-center/

So let's get started!

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IBM DevOps and Software Engineering Professional Certificate

 

What you'll learn

Develop  a DevOps mindset, practice Agile philosophy & Scrum methodology -  essential to succeed in the era of Cloud Native Software Engineering

Create applications using Python  language, using various programming constructs and logic, including functions, REST APIs, and  libraries

Build applications composed of microservices and deploy using containers (e.g. Docker, Kubernetes, and OpenShift) & serverless technologies

Employ tools for automation, continuous integration (CI) and continuous deployment (CD) including Chef, Puppet, GitHub Actions, Tekton and  Travis. 

Join Free:IBM DevOps and Software Engineering Professional Certificate

Professional Certificate - 14 course series

DevOps professionals are in high demand! According to a recent GitLab report,  DevOps skills are expected to grow 122% over the next five years,  making it one of the fastest growing skills in the workforce. 

This certificate will equip you with the key concepts and technical know-how to build your skills and knowledge of DevOps practices, tools and technologies and prepare you for an entry-level role in Software Engineering. 

The courses in this program will help you develop skill sets in a variety of DevOps philosophies and methodologies including Agile Development, Scrum Methodology, Cloud Native Architecture, Behavior and Test-Driven Development, and Zero Downtime Deployments.

You will learn to program with the Python language and Linux shell scripts,  create projects in GitHub, containerize and orchestrate your applications using Docker, Kubernetes & OpenShift,  compose applications with microservices, employ serverless technologies,  perform continuous integration and delivery (CI/CD), develop testcases,  ensure your code is secure, and monitor & troubleshoot your cloud deployments.

Guided by experts at IBM, you will be prepared for success. Labs and projects in this certificate program are designed to equip job-ready hands-on skills that will help you launch a new career in a highly in-demand field. 

This professional certificate is suitable for both - those who have none or some programming experience, as well as those with and without college degrees.

Applied Learning Project

Throughout the courses in this Professional Certificate,  you will develop a portfolio of projects to demonstrate your proficiency using various popular tools and technologies in DevOps and Cloud Native Software Engineering. 

You will: 

Create applications using Python programming language, using different programming constructs and logic, including functions, REST APIs, and various Python libraries.

Develop Linux Shell Scripts using Bash and automate repetitive tasks

Create projects on GitHub and work with Git commands

Build  and deploy applications composed of several microservices and deploy  them to cloud using containerization tools (such as Docker, Kubernetes,  and OpenShift); and serverless technologies

Employ various tools for automation, continuous integration (CI) and  continuous deployment (CD) of software including Chef, Puppet, GitHub  Actions, Tekton and Travis.

Secure and Monitor your applications and cloud deployments using tools like sysdig and Prometheus.

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CertNexus Certified Artificial Intelligence Practitioner Professional Certificate

 

What you'll learn

Learn about the business problems that AI/ML can solve as well as the specific AI/ML technologies that can solve them.  

Learn important tasks that make up the workflow, including data analysis and model training and about how machine learning tasks can be automated. 

Use ML algorithms to solve the two most common supervised problems regression and classification, and a common unsupervised problem: clustering.

Explore advanced algorithms used in both machine learning and deep learning. Build multiple models to solve business problems within a workflow.

Join Free:CertNexus Certified Artificial Intelligence Practitioner Professional Certificate

Professional Certificate - 5 course series

The Certified Artificial Intelligence Practitioner™ (CAIP) specialization prepares learners to earn an industry validated certification which will differentiate themselves from other job candidates and demnstrate proficiency in the concepts of Artificial intelligence (AI) and machine learning (ML) found in CAIP. 

AI and ML have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This specialization shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. 

The specialization is designed for data science practitioners entering the field of artificial intelligence and will prepare learners for the CAIP certification exam. 

Applied Learning Project

At the conclusion of each course, learners will have the opportunity to complete a project which can be added to their portfolio of work.  Projects include: 

Create an AI project outline

Follow a machine learning workflow to predict demand 

Build a regression, classification, or clustering model

Build a convolutional neural network (CNN)

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Artificial Intelligence (AI) Education for Teachers

 

What you'll learn

Compare AI with human intelligence, broadly understand how it has evolved since the 1950s, and identify industry applications

Identify and use creative and critical thinking, design thinking, data fluency, and computational thinking as they relate to AI applications

Explain how the development and use of AI requires ethical considerations focusing on fairness, transparency, privacy protection and compliance

Describe how thinking skills embedded in Australian curricula can be used to solve problems where AI has the potential to be part of the solution

Join Free:Artificial Intelligence (AI) Education for Teachers

There are 6 modules in this course

Today’s learners need to know what artificial intelligence (AI) is, how it works, how to use it in their everyday lives, and how it could potentially be used in their future. Using AI requires skills and values which extend far beyond simply having knowledge about coding and technology.

This course is designed by teachers, for teachers, and will bridge the gap between commonly held beliefs about AI, and what it really is. AI can be embedded into all areas of the school curriculum and this course will show you how. 

This course will appeal to teachers who want to increase their general understanding of AI, including why it is important for learners; and/or to those who want to embed AI into their teaching practice and their students’ learning. There is also a unique opportunity to implement a Capstone Project for students alongside this professional learning course.

Macquarie School of Education at Macquarie University and IBM Australia have collaborated to create this course which is aligned to AITSL ‘Proficient Level’ Australian Professional Standards at AQF Level 8.

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Web Design for Everybody: Basics of Web Development & Coding Specialization

 

What you'll learn

Add interacitivity to web pages with Javascript

Describe the basics of Cascading Style Sheets (CSS3)

Use the Document Object Model (DOM) to modify pages

Apply responsive design to enable page to be viewed by various devices

Specialization - 5 course series

This Specialization covers the basics of how web pages are created – from writing syntactically correct HTML and CSS to adding JavaScript to create an interactive experience. While building your skills in these topics you will create websites that work seamlessly on mobile, tablet, and large screen browsers. During the capstone you will develop a professional-quality web portfolio demonstrating your growth as a web developer and your knowledge of accessible web design. This will include your ability to design and implement a responsive site that utilizes tools to create a site that is accessible to a wide audience, including those with visual, audial, physical, and cognitive impairments.

Join : Web Design for Everybody: Basics of Web Development & Coding Specialization

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Difference between Lists and sets in Python

 Lists and sets in Python are both used for storing collections of elements, but they have several differences based on their characteristics and use cases. Here are some key differences between lists and sets in Python:

Ordering:

Lists: Maintain the order of elements. The order in which elements are added is preserved, and you can access elements by their index.

Sets: Do not maintain any specific order. The elements are unordered, and you cannot access them by index.

Uniqueness:

Lists: Allow duplicate elements. You can have the same value multiple times in a list.

Sets: Enforce uniqueness. Each element in a set must be unique; duplicates are automatically removed.

Declaration:

Lists: Created using square brackets [].

Sets: Created using curly braces {} or the set() constructor.

Mutability:

Lists: Mutable, meaning you can change the elements after the list is created. You can add, remove, or modify elements.

Sets: Mutable, but individual elements cannot be modified once the set is created. You can add and remove elements, but you can't change them.

Indexing:

Lists: Allow indexing and slicing. You can access elements by their position in the list.

Sets: Do not support indexing or slicing. Elements cannot be accessed by position.

Here's a brief example illustrating some of these differences:

# Lists

my_list = [1, 2, 3, 3, 4, 5]

print(my_list)        # Output: [1, 2, 3, 3, 4, 5]

print(my_list[2])     # Output: 3

# Sets

my_set = {1, 2, 3, 3, 4, 5}

print(my_set)         # Output: {1, 2, 3, 4, 5}

# print(my_set[2])    # Raises TypeError, sets do not support indexing

In the above example, the list allows duplicates and supports indexing, while the set automatically removes duplicates and does not support indexing. Choose between lists and sets based on your specific requirements for ordering, uniqueness, and mutability.

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Python Coding challenge - Day 110 | What is the output of the following Python Code?

 

aList = ["Clcoding", [4, 8, 12, 16]]   

print(aList[1][3])

Solution and Explanation:

The given code is a Python script that defines a list called aList containing two elements: the string "Clcoding" and another list [4, 8, 12, 16]. The script then prints the value at the index 3 of the second element (the inner list) of aList.

Let's break it down:

aList[1] refers to the second element of aList, which is [4, 8, 12, 16].

aList[1][3] accesses the element at index 3 of the inner list, which is 16.

Therefore, the output of the code will be:

16

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Avatar Logo in Python using Turtle

 

from turtle import *

speed(0)

bgcolor('black')

color('orange')

hideturtle()

n=1

p=True

while True:

    circle(n)

    if p:

        n=n-1

    else:

        n=n+1

    if n==0 or n==60:

        p=not p

    left(1)

    forward(3)

#clcoding.com    

Explanation:

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Amazing Spiral in Python (turtle library)

 

import turtle

#clcoding.com

t = turtle.Turtle()

s = turtle.Screen()

s.bgcolor("black")

t.width(2)

t.speed(15)

col = ('white','pink','cyan')

for i in range (300):

    t.pencolor(col[i%3])

    t.forward(i*4)

    t.right(121)

#clcoding.com  

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How much do you know about string in python?

 

s = 'Clcoding'

print(s.startswith('CL'))

# In[13]:

s = 'Clcoding'

print(s.isalpha( ))

# In[14]:

s = 'Clcoding'

print(s.isdigit( ))

# In[15]:

s = 'Clcoding'

print(s.isalnum( ))

# In[16]:

s = 'Clcoding'

print(s.islower( ))

# In[17]:

s = 'Clcoding'

print(s.isupper( ))

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Introduction to Probability for Data Science (Free PDF)

 

This introductory textbook in undergraduate probability emphasizes the inseparability between data (computing) and probability (theory) in our time. It examines the motivation, intuition, and implication of the probabilistic tools used in science and engineering:

Motivation: In the ocean of mathematical definitions, theorems, and equations, why should we spend our time on this particular topic but not another?

Intuition: When going through the deviations, is there a geometric interpretation or physics beyond those equations?

Implication: After we have learned a topic, what new problems can we solve?

Download : Introduction to Probability for Data Science

Hard Copy : Introduction to Probability for Data Science

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How much do you know about Containership and Inheritance in python?

a. Inheritance is the ability of a class to inherit properties and behavior

from a parent class by extending it.

Answer

True

b. Containership is the ability of a class to contain objects of different

classes as member data.

Answer

True

c. We can derive a class from a base class even if the base class's source

code is not available.

Answer

True

d. Multiple inheritance is different from multiple levels of inheritance.

Answer

True

e. An object of a derived class cannot access members of base class if the

member names begin with.

Answer

True

f. Creating a derived class from a base class requires fundamental changes

to the base class.

Answer

False

g. If a base class contains a member function func( ), and a derived class

does not contain a function with this name, an object of the derived class

cannot access func( ).

Answer

False

h. If no constructors are specified for a derived class, objects of the derived

class will use the constructors in the base class.

Answer

False

i. If a base class and a derived class each include a member function with

the same name, the member function of the derived class will be called

by an object of the derived class.

Answer

True

j. A class D can be derived from a class C, which is derived from a class

B, which is derived from a class A.

Answer

True

k. It is illegal to make objects of one class members of another class.

Answer

False

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Day 173 : Convert Decimal to Fraction in Python

 

from fractions import Fraction

# Convert decimal to fraction

decimal_number = input("Enter a Decimal Number:")

fraction_result = Fraction(decimal_number).limit_denominator()

print(f"Decimal: {decimal_number}")

print(f"Fraction: {fraction_result}")

#clcoding.com for free code visit

Explanation : 

Let's break down the code step by step:

from fractions import Fraction

This line imports the Fraction class from the fractions module. The Fraction class is used to represent and perform operations with rational numbers.

# Convert decimal to fraction

decimal_number = input("Enter a Decimal Number:")

This line prompts the user to enter a decimal number by using the input function. The entered value is stored in the variable decimal_number.

fraction_result = Fraction(decimal_number).limit_denominator()

Here, the entered decimal_number is converted to a Fraction object using the Fraction class. The limit_denominator() method is then called to limit the denominator of the fraction to a reasonable size.

print(f"Decimal: {decimal_number}")

print(f"Fraction: {fraction_result}")

These lines print the original decimal number entered by the user and the corresponding fraction.

#clcoding.com for free code visit

This line is a comment and is not executed as part of the program. It's just a comment providing a website link.

So, when you run this code, it will prompt you to enter a decimal number, convert it to a fraction, and then print both the original decimal and the corresponding fraction. The limit_denominator() method is used to present the fraction in a simplified form with a limited denominator.

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Python Programming Language

 An extremely handy programmer’s standard library reference that is as durable as it is portable. This 6 page laminated guide includes essential script modules used by developers of all skill levels to simplify the process of programming in Python. This guide is all script and is organized to find needed script quickly. As with QuickStudy reference on any subject, with continued reference, the format lends itself to memorization. Beginning students or seasoned programmers will find this tool a perfect go-to for the at-a-glance script answer and memory jog you might need. At this price and for the bank of script included it’s an easy add to your programmer’s toolbox.

6 page laminated guide includes:

General Functionality

Date/Time Processing

System and Computer Controls

OS Module

Classes of the OS Module

Pathlib Module

Threading Module

Debugging Functionality

PDB Module

Debugging for the PDB Module

Mathematic and Numeric Operations

Math Module

Random Module

Iterable and Iterator Operations

Collections Module

Classes of the Collections Module

Itertools Module

Web and Data Transfer Operations

HTML Parser Module

HTML Module

Audio Manipulation

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Python Standard Library

 

An extremely handy programmer’s standard library reference that is as durable as it is portable. This 6 page laminated guide includes essential script modules used by developers of all skill levels to simplify the process of programming in Python. This guide is all script and is organized to find needed script quickly. As with QuickStudy reference on any subject, with continued reference, the format lends itself to memorization. Beginning students or seasoned programmers will find this tool a perfect go-to for the at-a-glance script answer and memory jog you might need. At this price and for the bank of script included it’s an easy add to your programmer’s toolbox.

6 page laminated guide includes:

General Functionality

Date/Time Processing

System and Computer Controls

OS Module

Classes of the OS Module

Pathlib Module

Threading Module

Debugging Functionality

PDB Module

Debugging for the PDB Module

Mathematic and Numeric Operations

Math Module

Random Module

Iterable and Iterator Operations

Collections Module

Classes of the Collections Module

Itertools Module

Web and Data Transfer Operations

HTML Parser Module

HTML Module

Audio Manipulation

Buy : Python Standard Library

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Python Coding challenge - Day 109 | What is the output of the following Python Code?

 

p = 'Love for Coding'

print(p[4], p[5])

Solution and Explanation:

This code prints the characters at positions 4 and 5 in the string p. In Python, string indexing starts at 0. Therefore:

p[4] refers to the fifth character in the string, which is the space (' ').

p[5] refers to the sixth character in the string, which is the letter 'f'.

So, when you run the code, it will output:

  f

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How much do you know about Exception Handling in python?

 

a. The exception handling mechanism is supposed to handle compile time

errors.

Answer

False

b. It is necessary to declare the exception class within the class in which an

exception is going to be thrown.

Answer

False

c. Every raised exception must be caught.

Answer

True

d. For one try block there can be multiple except blocks.

Answer

True

e. When an exception is raised, an exception class's constructor gets called.

Answer

True

f. try blocks cannot be nested.

Answer

False

g. Proper destruction of an object is guaranteed by exception handling

mechanism.

Answer

False

h. All exceptions occur at runtime.

Answer

True

i. Exceptions offer an object-oriented way of handling runtime errors.

Answer

True

j. If an exception occurs, then the program terminates abruptly without

getting any chance to recover from the exception.

Answer

False

k. No matter whether an exception occurs or not, the statements in the

finally clause (if present) will get executed.

Answer

True

l. A program can contain multiple finally clauses.

Answer

False

m. finally clause is used to perform cleanup operations like closing the

network/database connections.

Answer

True

n. While raising a user-defined exception, multiple values can be set in the

exception object.

Answer

True

o. In one function/method, there can be only one try block.

Answer

False

p. An exception must be caught in the same function/method in which it is

raised.

Answer

False

q. All values set up in the exception object are available in the except block

that catches the exception.

Answer

True

r. If our program does not catch an exception then Python runtime catches

it.

Answer

True

s. It is possible to create user-defined exceptions.

Answer

True

t. All types of exceptions can be caught using the Exception class.

Answer

True

u. For every try block there must be a corresponding finally block.

Answer

False

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Python Coding challenge - Day 108 | What is the output of the following Python Code?

 

Code :

s = set('CLC') 

t = list(s)  

t = t[::-1]  

print(t)

Solution and Explanation

Step 1: Define the set and convert it to a list:

s = set('CLC')

t = list(s)

We define a set s containing the characters C, L, and C (duplicates are ignored in sets).

Then, we convert the set s to a list t using the list() function. This creates a list containing the unique elements of the set in an arbitrary order.

Step 2: Reverse the list:

t = t[::-1]

We use the slicing operator [::-1] to reverse the order of elements in the list t.

Step 3: Print the reversed list:

print(t)

Finally, we print the reversed list t.

Output:

['L', 'C']

As you can see, the output shows the characters from the original set in reverse order (L and C).

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Structuring Machine Learning Projects

 

Build your subject-matter expertise

This course is part of the Deep Learning Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free:Structuring Machine Learning Projects

There are 2 modules in this course

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. 

By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning.

This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience.

 

The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

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Machine Learning: Theory and Hands-on Practice with Python Specialization

 

What you'll learn

Explore several classic Supervised and Unsupervised Learning algorithms and introductory Deep Learning topics.  

Build and evaluate Machine Learning models utilizing popular Python libraries and compare each algorithm’s strengths and weaknesses.

Explain which Machine Learning models would be best to apply to a Machine Learning task based on the data’s properties.

Improve model performance by tuning hyperparameters and applying various techniques such as sampling and regularization.

Join Free:Machine Learning: Theory and Hands-on Practice with Python Specialization

Specialization - 3 course series

In the Machine Learning specialization, we will cover Supervised Learning, Unsupervised Learning, and the basics of Deep Learning. You will apply ML algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Starting with supervised learning, we will cover linear and logistic regression, KNN, Decision trees, ensembling methods such as Random Forest and Boosting, and kernel methods such as SVM. Then we turn our attention to unsupervised methods, including dimensionality reduction techniques (e.g., PCA), clustering, and recommender systems. We finish with an introduction to deep learning basics, including choosing model architectures, building/training neural networks with libraries like Keras, and hands-on examples of CNNs and RNNs. 

This specialization can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: 

MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder 

MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

Applied Learning Project

In this specialization, you will build a movie recommendation system, identify cancer types based on RNA sequences, utilize CNNs for digital pathology, practice NLP techniques on disaster tweets, and even generate your images of dogs with GANs. You will complete a final supervised, unsupervised, and deep learning project to demonstrate course mastery.

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Exploratory Data Analysis for Machine Learning

 

Build your subject-matter expertise

This course is available as part of multiple programs,

When you enroll in this course, you'll also be asked to select a specific program.

Learn new concepts from industry experts

Gain a foundational understanding of a subject or tool

Develop job-relevant skills with hands-on projects

Earn a shareable career certificate

Join Free:Exploratory Data Analysis for Machine Learning

There are 5 modules in this course

This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing.

By the end of this course you should be able to:

Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud 

Describe and use common feature selection and feature engineering techniques

Handle categorical and ordinal features, as well as missing values

Use a variety of techniques for detecting and dealing with outliers

Articulate why feature scaling is important and use a variety of scaling techniques

 

Who should take this course?

This course targets aspiring data scientists interested in acquiring hands-on experience  with Machine Learning and Artificial Intelligence in a business setting.

 

What skills should you have?

To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.

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Investment Management with Python and Machine Learning Specialization

 

What you'll learn

Write custom Python code and use existing Python libraries to build and analyse efficient portfolio strategies.

Write custom Python code and use existing Python libraries to estimate risk and return parameters, and build better diversified portfolios.

Learn the principles of supervised and unsupervised machine learning techniques to financial data sets 

Gain an understanding of advanced data analytics methodologies, and quantitative modelling applied to alternative data in investment decisions     

Join Free:Investment Management with Python and Machine Learning Specialization

Specialization - 4 course series

The Data Science and Machine Learning for Asset Management Specialization has been designed to deliver a broad and comprehensive introduction to modern methods in Investment Management, with a particular emphasis on the use of data science and machine learning techniques to improve investment decisions.By the end of this specialization, you will have acquired the tools required for making sound investment decisions, with an emphasis not only on the foundational theory and underlying concepts, but also on practical applications and implementation. Instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language through a series of dedicated lab sessions.

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Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

 

What you'll learn

Learn the skills needed to be successful in a machine learning engineering role

Prepare for the Google Cloud Professional Machine Learning Engineer certification exam

Understand how to design, build, productionalize ML models to solve business challenges using Google Cloud technologies

Understand the purpose of the Professional Machine Learning Engineer certification and its relationship to other Google Cloud certifications

Join Free:Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

Professional Certificate - 9 course series

87% of Google Cloud certified users feel more confident in their cloud skills. This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized

 Google Cloud Professional Machine Learning Engineer

 certification.

Here's what you have to do

1) Complete the Preparing for Google Cloud Machine Learning Engineer Professional Certificate

2) Review other recommended resources for the Google Cloud Professional Machine Learnin Engineer 

exam

3) Review the Professional Machine Learning Engineer exam guide

4) Complete Professional Machine Learning Engineer sample questions

5)Register for the Google Cloud certification exam (remotely or at a test center)

Applied Learning Project

This professional certificate incorporates hands-on labs using Qwiklabs platform.These hands on components will let you apply the skills you learn. Projects incorporate Google Cloud Platform products used within Qwiklabs. You will gain practical hands-on experience with the concepts explained throughout the modules.

Applied Learning Project

This specialization incorporates hands-on labs using Google's Qwiklabs platform.

These hands on components will let you apply the skills you learn in the video lectures. Projects will incorporate topics such as Google Cloud Platform products, which are used and configured within Qwiklabs. You can expect to gain practical hands-on experience with the concepts explained throughout the modules.

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Python for Excel: A Modern Environment for Automation and Data Analysis (Free PDF)

 

While Excel remains ubiquitous in the business world, recent Microsoft feedback forums are full of requests to include Python as an Excel scripting language. In fact, it's the top feature requested. What makes this combination so compelling? In this hands-on guide, Felix Zumstein--creator of xlwings, a popular open source package for automating Excel with Python--shows experienced Excel users how to integrate these two worlds efficiently.

Excel has added quite a few new capabilities over the past couple of years, but its automation language, VBA, stopped evolving a long time ago. Many Excel power users have already adopted Python for daily automation tasks. This guide gets you started.

Use Python without extensive programming knowledge

Get started with modern tools, including Jupyter notebooks and Visual Studio code

Use pandas to acquire, clean, and analyze data and replace typical Excel calculations

Automate tedious tasks like consolidation of Excel workbooks and production of Excel reports

Use xlwings to build interactive Excel tools that use Python as a calculation engine

Connect Excel to databases and CSV files and fetch data from the internet using Python code

Use Python as a single tool to replace VBA, Power Query, and Power Pivot

PDF Download : Python for Excel: A Modern Environment for Automation and Data Analysis

Buy : Python for Excel: A Modern Environment for Automation and Data Analysis

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Rewrite the following code snippet in 1 line

 

Rewrite the following code snippet in 1 line:

x = 3

y = 3.0

if x == y :

    print('x and y are equal')

else :

    print('x and y are not equal')

Answer: 

print('x and y are equal' if x == y else 'x and y are not equal')

The condition x == y is evaluated.

If the condition is True, the expression before the if (i.e., 'x and y are equal') is executed.

If the condition is False, the expression after the else (i.e., 'x and y are not equal') is executed.

So, in a single line, this code snippet prints either 'x and y are equal' or 'x and y are not equal' based on the equality of x and y.

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