Wednesday, 6 March 2024

Python Coding challenge - Day 143 | What is the output of the following Python Code?

 


This code defines a function named g1 that takes two parameters: x and d with a default value of an empty dictionary {}. The function updates the dictionary d by setting the key x to the value x and then returns the updated dictionary.

Here's a step-by-step explanation of the code:

def g1(x, d={}):: This line defines a function g1 with two parameters, x and d. The parameter d has a default value of an empty dictionary {}.

d[x] = x: This line updates the dictionary d by assigning the value of x to the key x. This essentially adds or updates the key-value pair in the dictionary.

return d: The function returns the updated dictionary d.

print(g1(5)): This line calls the function g1 with the argument 5. Since no value is provided for the d parameter, it uses the default empty dictionary {}. The dictionary is then updated to include the key-value pair 5: 5. The function returns the updated dictionary, and it is printed.

The output of print(g1(5)) will be:

{5: 5}

It's important to note that the default dictionary is shared across multiple calls to the function if no explicit dictionary is provided. This can lead to unexpected behavior, so caution should be exercised when using mutable default values in function parameters.






Machine Learning Engineering with Python - Second Edition: Manage the lifecycle of machine learning models using MLOps with practical examples

 


Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problems

Includes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChain

Key Features

  • This second edition delves deeper into key machine learning topics, CI/CD, and system design
  • Explore core MLOps practices, such as model management and performance monitoring
  • Build end-to-end examples of deployable ML microservices and pipelines using AWS and open-source tools

Book Description

The Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field.

The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized "model factory" for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift.

Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques.

With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.

Hard Copy : Machine Learning Engineering with Python - Second Edition: Manage the lifecycle of machine learning models using MLOps with practical examples

What you will learn

  • Plan and manage end-to-end ML development projects
  • Explore deep learning, LLMs, and LLMOps to leverage generative AI
  • Use Python to package your ML tools and scale up your solutions
  • Get to grips with Apache Spark, Kubernetes, and Ray
  • Build and run ML pipelines with Apache Airflow, ZenML, and Kubeflow
  • Detect drift and build retraining mechanisms into your solutions
  • Improve error handling with control flows and vulnerability scanning
  • Host and build ML microservices and batch processes running on AWS

Who this book is for

This book is designed for MLOps and ML engineers, data scientists, and software developers who want to build robust solutions that use machine learning to solve real-world problems. If you’re not a developer but want to manage or understand the product lifecycle of these systems, you’ll also find this book useful. It assumes a basic knowledge of machine learning concepts and intermediate programming experience in Python. With its focus on practical skills and real-world examples, this book is an essential resource for anyone looking to advance their machine learning engineering career.

Table of Contents

  1. Introduction to ML Engineering
  2. The Machine Learning Development Process
  3. From Model to Model Factory
  4. Packaging Up
  5. Deployment Patterns and Tools
  6. Scaling Up
  7. Deep Learning, Generative AI, and LLMOps
  8. Building an Example ML Microservice
  9. Building an Extract, Transform, Machine Learning Use Case

Where math doesn’t work in Python

 


1. Precision Issues:

x = 1.0

y = 1e-16

result = x + y

print(result)  

#clcoding.com 

1.0

2. Comparing Floating-Point Numbers:

a = 0.1 + 0.2

b = 0.3

print(a == b)  

#clcoding.com 

False

3. NaN (Not a Number) and Inf (Infinity):

result = float('inf') / float('inf')

print(result) 

#clcoding.com 

nan

4. Large Integers:

result = 2 ** 1000  

print(result)

#clcoding.com 

10715086071862673209484250490600018105614048117055336074437503883703510511249361224931983788156958581275946729175531468251871452856923140435984577574698574803934567774824230985421074605062371141877954182153046474983581941267398767559165543946077062914571196477686542167660429831652624386837205668069376

5. Round-off Errors:

result = 0.1 + 0.2 - 0.3

print(result)  

#clcoding.com 

5.551115123125783e-17

Tuesday, 5 March 2024

Python Coding challenge - Day 142 | What is the output of the following Python Code?

 


In Python, the expressions within curly braces {} are evaluated as set literals. However, the expressions 1 and 2 and 1 or 3 are not directly used to create sets. Instead, these expressions are evaluated as boolean logic expressions and the final results are used to create sets.

Let's break down the expressions:

s1 = {1 and 2}

s2 = {1 or 3}

result = s1 ^ s2

print(result)

1 and 2: This expression evaluates to 2 because and returns the last truthy value (2 is the last truthy value in the expression).

1 or 3: This expression evaluates to 1 because or returns the first truthy value (1 is the first truthy value in the expression).

Therefore, your sets become:

s1 = {2}

s2 = {1}

result = s1 ^ s2

print(result)

Output:

{1, 2}

In this example, the ^ (symmetric difference) operator results in a set containing elements that are unique to each set ({1, 2}).

Python & SQL Mastery: 5 Books in 1: Your Comprehensive Guide from Novice to Expert (2024 Edition) (Data Dynamics: Python & SQL Mastery)

 


Are you poised to elevate your technical expertise and stay ahead in the rapidly evolving world of data and programming?

Look no further!

Our 5 Books Series is meticulously crafted to guide you from the basics to the most advanced concepts in Python and SQL, making it a must-have for database enthusiasts, aspiring data scientists, and seasoned coders alike.

Comprehensive Learning Journey:

Mastering SQL: Dive deep into every facet of SQL, from fundamental data retrieval to complex transactions, views, and indexing.

Synergizing Code and Data: Explore the synergy between Python and SQL Server Development, mastering techniques from executing SQL queries through Python to advanced data manipulation.

Python and SQL for Data Solutions: Uncover the powerful combination of Python and SQL for data analysis, reporting, and integration, including ETL processes and machine learning applications.

Advanced Data Solutions: Delve into integrating Python and SQL for data retrieval, manipulation, and performance optimization.

Integrating Python and SQL: Master database manipulation, focusing on crafting SQL queries in Python and implementing security best practices.

Empower Your Career: Gain the skills that are highly sought after in today's job market. From database management to advanced analytics, this series prepares you for a multitude of roles in tech, data analysis, and beyond.

Practical, Real-World Application: Each book is packed with practical examples, real-world case studies, and hands-on projects. This approach not only reinforces learning but also prepares you to apply your knowledge effectively in professional settings.

Expert Insight and Future Trends: Learn from experts with years of experience in the field. The series not only teaches you current best practices but also explores emerging trends, ensuring you stay at the forefront of technology.

For Beginners and Experts Alike: Whether you're just starting out or looking to deepen your existing knowledge, our series provides a clear, structured path to mastering both Python and SQL.

Embark on this comprehensive journey to mastering Python and SQL. With our series, you'll transform your career, opening doors to new opportunities and achieving data excellence.

Hard Copy: Python & SQL Mastery: 5 Books in 1: Your Comprehensive Guide from Novice to Expert (2024 Edition) (Data Dynamics: Python & SQL Mastery)

Finance with Rust: The 2024 Quantitative Finance Guide to - Financial Engineering, Machine Learning, Algorithmic Trading, Data Visualization & More

 


Reactive Publishing

"Finance with Rust" is a pioneering guide that introduces financial professionals and software developers to the transformative power of Rust in the financial industry. With its emphasis on speed, safety, and concurrency, Rust presents an unprecedented opportunity to enhance financial systems and applications.

Written by an accomplished software developer and entrepreneur, this book bridges the gap between complex financial processes and cutting-edge technology. It offers a comprehensive exploration of Rust's application in finance, from developing faster algorithms to ensuring data security and system reliability.

Within these pages, you'll discover:

An introduction to Rust for those new to the language, focusing on its relevance and benefits in financial applications.

Step-by-step guides on using Rust to build scalable and secure financial models, algorithms, and infrastructure.

Case studies demonstrating the successful integration of Rust in financial systems, highlighting its impact on performance and security.

Practical insights into leveraging Rust for financial innovation, including blockchain technology, cryptocurrency platforms, and more.

"Finance with Rust" empowers you to stay ahead in the fast-evolving world of financial technology. Whether you're aiming to optimize financial operations, develop high-performance trading systems, or innovate with blockchain and crypto technologies, this book is your essential roadmap to success.

Hard Copy: Finance with Rust: The 2024 Quantitative Finance Guide to - Financial Engineering, Machine Learning, Algorithmic Trading, Data Visualization & More

PYTHON PROGRAMMING FOR BEGINNERS: Mastering Python With No Prior Experience: The Ultimate Guide to Conquer Your Coding Fear From Crash and Land Your First Job in Tech

 


Learn Python Programming Fast - A Beginner's Guide to Mastering Python from Home

Grab the Bonus Chapter Inside with 50 Coding Journal

Python is the most in-demand programming language in 2024. As a beginner, learning Python can open up high-paying remote and freelance job opportunities in fields like data science, web development, AI, and more.

This hands-on Python Programming is designed specifically for beginners with no prior coding experience. It provides a foundations-first introduction to Python programming concepts using simplified explanations, practical examples, and step-by-step tutorials.

Programming is best learned by doing, and thus, this book incorporates numerous practical exercises and real-world projects.

This is not Hype; you will learn something new in this Python Programming for Beginners.

What You Will Learn in this Python Programming for Beginners Book:

Python Installation - How to download Python and set up your coding environment

Python Syntax - Key programming constructs like variables, data types, functions, conditionals and loops

Core Programming Techniques - Best practices for writing clean, efficient Python code

Built-in Data Structures - Hands-on projects using Python lists, tuples, dictionaries and more

Object-Oriented Programming - How to work with classes, objects and inheritance in Python

Python for Web Development - Build a web app and API with Python frameworks like Django and Flask

Python for Data Analysis - Use Python for data science and work with Jupyter Notebooks

Python for Machine Learning - Implement machine learning algorithms for prediction and classification

Bonus: Python Coding Interview Questions - Practice questions and answers to prepare for the interview

This beginner-friendly guide will give you a solid foundation in Python to build real-world apps and land your first Python developer job.

Hard Copy: PYTHON PROGRAMMING FOR BEGINNERS: Mastering Python With No Prior Experience: The Ultimate Guide to Conquer Your Coding Fear From Crash and Land Your First Job in Tech

Econometric Python: Harnessing Data Science for Economic Analysis: The Science of Pythonomics in 2024

 


Reactive Publishing

In the rapidly evolving landscape of economics, "Econometric Python" emerges as a groundbreaking guide, perfectly blending the intricate world of econometrics with the dynamic capabilities of Python. This book is crafted for economists, data scientists, researchers, and students who aspire to revolutionize their approach to economic data analysis.

At its center, "Econometric Python" serves as a beacon for those navigating the complexities of econometric models, offering a unique perspective on applying Python's powerful data science tools in economic research. The book starts with a fundamental introduction to Python, focusing on aspects most relevant to econometric analysis. This makes it an invaluable resource for both Python novices and seasoned programmers.

As the narrative unfolds, readers are led through a series of progressively complex econometric techniques, all demonstrated with Python's state-of-the-art libraries such as pandas, NumPy, and statsmodels. Each chapter is meticulously designed to balance theory and practice, providing in-depth explanations of econometric concepts, followed by practical coding examples.

Key features of "Econometric Python" include:

Comprehensive Coverage: From basic economic concepts to advanced econometric models, the book covers a wide array of topics, ensuring a thorough understanding of both theoretical and practical aspects.

Hands-On Approach: With real-world datasets and step-by-step coding tutorials, readers gain hands-on experience in applying econometric theories using Python.

Latest Trends and Techniques: Stay abreast of the latest developments in both econometrics and Python programming, including machine learning applications in economic data analysis.

Expert Insights: The authors, renowned in the fields of economics and data science, provide valuable insights and tips, enhancing the learning experience.

"Econometric Python" is more than just a textbook; it's a journey into the future of economic analysis. By the end of this book, readers will not only be proficient in using Python for econometric analysis but will also be equipped with the skills to contribute innovatively to the field of economics. Whether it's for academic purposes, professional development, or personal interest, this book is an indispensable asset for anyone looking to merge the power of data science with economic analysis.

Hard Copy: Econometric Python: Harnessing Data Science for Economic Analysis: The Science of Pythonomics in 2024

Python Data Science 2024: Explore Data, Build Skills, and Make Data-Driven Decisions in 30 Days (Machine Learning and Data Analysis for Beginners)

 


Data Science Crash Course for Beginners with Python...

Uncover the energy of records in 30 days with Python Data Science 2024!

Are you searching for a hands-on strategy to study Python coding and Python for Data Analysis fast?

This beginner-friendly route offers you the abilities and self-belief to discover data, construct sensible abilities, and begin making data-driven selections inside a month.

On the program:

Deep mastering

Neural Networks and Deep Learning

Deep Learning Parameters and Hyper-parameters

Deep Neural Networks Layers

Deep Learning Activation Functions

Convolutional Neural Network

Python Data Structures

Best practices in Python and Zen of Python

Installing Python

Python

These are some of the subjects included in this book:

Fundamentals of deep learning

Fundamentals of probability

Fundamentals of statistics

Fundamentals of linear algebra

Introduction to desktop gaining knowledge of and deep learning

Fundamentals of computer learning

Deep gaining knowledge of parameters and hyper-parameters

Deep neural networks layers

Deep getting to know activation functions

Convolutional neural network

Deep mastering in exercise (in jupyter notebooks)

Python information structures

Best practices in python and zen of Python

Installing Python

At the cease of this course, you may be in a position to:

Confidently deal with real-world datasets.

Wrangle, analyze, and visualize facts the usage of Python.

Turn records into actionable insights and knowledgeable decisions.

Speak the language of data-driven professionals.

Lay the basis for in addition studying in statistics science and computing device learning.

Hard Copy: Python Data Science 2024: Explore Data, Build Skills, and Make Data-Driven Decisions in 30 Days (Machine Learning and Data Analysis for Beginners)



Sunday, 3 March 2024

Slicing in Python

 

Example 1: Slicing a List

# Slicing a list

numbers = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

# Get elements from index 2 to 5 (exclusive)

subset = numbers[2:5]

print(subset)  # Output: [2, 3, 4]

#clcoding.com

[2, 3, 4]

Example 2: Omitting Start and End Indices

# Omitting start and end indices

subset = numbers[:7]  # From the beginning to index 6

print(subset)  # Output: [0, 1, 2, 3, 4, 5, 6]

subset = numbers[3:]  # From index 3 to the end

print(subset)  # Output: [3, 4, 5, 6, 7, 8, 9]

#clcoding.com

[0, 1, 2, 3, 4, 5, 6]

[3, 4, 5, 6, 7, 8, 9]

Example 3: Using Negative Indices

# Using negative indices

subset = numbers[-4:-1]  

print(subset)  

#clcoding.com

[6, 7, 8]

Example 4: Slicing a String

# Slicing a string

text = "Hello, Python!"

# Get the substring "Python"

substring = text[7:13]

print(substring)  # Output: Python

#clcoding.com

Python

Example 5: Step in Slicing

# Step in slicing

even_numbers = numbers[2:10:2]  

print(even_numbers)  

#clcoding.com

[2, 4, 6, 8]

Example 6: Slicing with Stride

# Slicing with stride

reverse_numbers = numbers[::-1] 

print(reverse_numbers)  

#clcoding.com

[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]

Example 7: Slicing a Tuple

# Slicing a tuple

my_tuple = (1, 2, 3, 4, 5)

# Get a sub-tuple from index 1 to 3

subset_tuple = my_tuple[1:4]

print(subset_tuple)  # Output: (2, 3, 4)

#clcoding.com

(2, 3, 4)

Example 8: Modifying a List with Slicing

# Modifying a list with slicing

letters = ['a', 'b', 'c', 'd', 'e']

# Replace elements from index 1 to 3

letters[1:4] = ['x', 'y', 'z']

print(letters)  

#clcoding.com

['a', 'x', 'y', 'z', 'e']

Saturday, 2 March 2024

Python Coding challenge - Day 141 | What is the output of the following Python Code?

 


The above code defines a string variable my_string with the value "hello, world!" and then extracts a substring from index 2 to 6 (7 is exclusive) using slicing. Finally, it prints the extracted substring. Here's the breakdown:

my_string = "hello, world!"

substring = my_string[2:7]

print(substring)

Output:

llo, 

In Python, string indexing starts from 0, so my_string[2] is the third character, which is "l", and my_string[7] is the eighth character, which is the space after the comma. Therefore, the substring "llo, " is extracted and printed.

Data Analysis with Python

 


What you'll learn

Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data

Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy

Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines

Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making

Join Free: Data Analysis with Python

There are 6 modules in this course
Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the basics of data analysis with Python to building and evaluating data models.  

Topics covered include:  
- collecting and importing data 
- cleaning, preparing & formatting data 
- data frame manipulation 
- summarizing data 
- building machine learning regression models 
- model refinement 
- creating data pipelines 

You will learn how to import data from multiple sources, clean and wrangle data, perform exploratory data analysis (EDA), and create meaningful data visualizations. You will then predict future trends from data by developing linear, multiple, polynomial regression models & pipelines and learn how to evaluate them.  

In addition to video lectures you will learn and practice using hands-on labs and projects. You will work with several open source Python libraries, including Pandas and Numpy to load, manipulate, analyze, and visualize cool datasets. You will also work with scipy and scikit-learn, to build machine learning models and make predictions.  


Get Started with Python by Google

 


What you'll learn

Explain how Python is used by data professionals 

Explore basic Python building blocks, including syntax and semantics

Understand loops, control statements, and string manipulation

Use data structures to store and organize data 

Join Free : Get Started with Python

There are 5 modules in this course

This is the second of seven courses in the Google Advanced Data Analytics Certificate. The Python programming language is a powerful tool for data analysis. In this course, you’ll learn the basic concepts of Python programming and how data professionals use Python on the job. You'll explore concepts such as object-oriented programming, variables, data types, functions, conditional statements, loops, and data structures. 

Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. 

Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate.    

By the end of this course, you will:

-Define what a programming language is and why Python is used by data scientists

-Create Python scripts to display data and perform operations

-Control the flow of programs using conditions and functions

-Utilize different types of loops when performing repeated operations

-Identify data types such as integers, floats, strings, and booleans

-Manipulate data structures such as , lists, tuples, dictionaries, and sets

-Import and use Python libraries such as NumPy and pandas

The zip function in Python

 


Example 1: Basic Usage of zip

# Basic usage of zip

names = ["Alice", "Bob", "Charlie"]

ages = [25, 30, 35]

# Combining lists using zip

combined_data = zip(names, ages)

# Displaying the result

for name, age in combined_data:

    print(f"Name: {name}, Age: {age}")

    #clcoding.com

Name: Alice, Age: 25

Name: Bob, Age: 30

Name: Charlie, Age: 35

Example 2: Different Lengths in Input Iterables

# Zip with different lengths in input iterables

names = ["Alice", "Bob", "Charlie"]

ages = [25, 30]

# Using zip with different lengths will stop at the shortest iterable

combined_data = zip(names, ages)

# Displaying the result

for name, age in combined_data:

    print(f"Name: {name}, Age: {age}")

    

#clcoding.com

Name: Alice, Age: 25

Name: Bob, Age: 30

Example 3: Unzipping with zip

# Unzipping with zip

names = ["Alice", "Bob", "Charlie"]

ages = [25, 30, 35]

# Combining lists using zip

combined_data = zip(names, ages)

# Unzipping the result

unzipped_names, unzipped_ages = zip(*combined_data)

# Displaying the unzipped data

print("Unzipped Names:", unzipped_names)

print("Unzipped Ages:", unzipped_ages)

#clcoding.com

Unzipped Names: ('Alice', 'Bob', 'Charlie')

Unzipped Ages: (25, 30, 35)

Example 4: Using zip with Dictionaries

# Using zip with dictionaries

keys = ["name", "age", "city"]

values = ["Alice", 25, "New York"]

# Creating a dictionary using zip

person_dict = dict(zip(keys, values))

# Displaying the dictionary

print(person_dict)

#clcoding.com

{'name': 'Alice', 'age': 25, 'city': 'New York'}

Example 5: Transposing a Matrix with zip

# Transposing a matrix using zip

matrix = [

    [1, 2, 3],

    [4, 5, 6],

    [7, 8, 9]

]

# Using zip to transpose the matrix

transposed_matrix = list(zip(*matrix))

# Displaying the transposed matrix

for row in transposed_matrix:

    print(row)

#clcoding.com

(1, 4, 7)

(2, 5, 8)

(3, 6, 9)

Example 6: Using zip with enumerate

# Using zip with enumerate

names = ["Alice", "Bob", "Charlie"]

# Combining with enumerate to get both index and value

indexed_names = list(zip(range(len(names)), names))

# Displaying the result

for index, name in indexed_names:

    print(f"Index: {index}, Name: {name}")

#clcoding.com

Index: 0, Name: Alice

Index: 1, Name: Bob

Index: 2, Name: Charlie

Python Coding challenge - Day 140 | What is the output of the following Python Code?

 


Let's break down the code:

x = 5

y = 2

x *= -y

print(x, y)

Here's what happens step by step:


x is initially assigned the value 5.

y is initially assigned the value 2.

x *= -y is equivalent to x = x * -y, which means multiplying the current value of x by -y and assigning the result back to x.

Therefore, x becomes 5 * -2, resulting in x being updated to -10.

The print(x, y) statement prints the current values of x and y.

So, the output of this code will be:

-10 2

Thursday, 29 February 2024

Python Coding challenge - Day 139 | What is the output of the following Python Code?

 


Let's break down the code step by step:

Function Definition:

def custom_function(b):

This line defines a function named custom_function that takes a parameter b.

Conditional Statements:

if b < 0:

    return 20

This block checks if the value of b is less than 0. If it is, the function returns the integer 20.

if b == 0:

    return 20.0

This block checks if the value of b is equal to 0. If it is, the function returns the floating-point number 20.0.

if b > 0:

    return '20'

This block checks if the value of b is greater than 0. If it is, the function returns the string '20'.

Function Call:

print(custom_function(-3))

This line calls the custom_function with the argument -3 and prints the result.

Output Explanation:

The argument is -3, which is less than 0. Therefore, the first condition is true.

The function returns the integer 20.

The print statement then outputs 20.

So, the output of the provided code will be:

20

This is because the function returns the integer 20 when the input is less than 0.

Probabilistic Graphical Models 3: Learning

 


Build your subject-matter expertise

This course is part of the Probabilistic Graphical Models 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: Probabilistic Graphical Models 3: Learning

There are 8 modules in this course

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. 

This course is the third in a sequence of three. Following the first course, which focused on representation, and the second, which focused on inference, this course addresses the question of learning: how a PGM can be learned from a data set of examples. The course discusses the key problems of parameter estimation in both directed and undirected models, as well as the structure learning task for directed models. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of two commonly used learning algorithms are implemented and applied to a real-world problem.

Probabilistic Graphical Models 2: Inference

 


Build your subject-matter expertise

This course is part of the Probabilistic Graphical Models 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: Probabilistic Graphical Models 2: Inference

There are 7 modules in this course

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. 

This course is the second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem.

Probabilistic Graphical Models 1: Representation

 


Build your subject-matter expertise

This course is part of the Probabilistic Graphical Models 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: Probabilistic Graphical Models 1: Representation

There are 7 modules in this course

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. 

This course is the first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly.

Evaluations of AI Applications in Healthcare

 


What you'll learn

Principles and practical considerations for integrating AI into clinical workflows

Best practices of AI applications to promote fair and equitable healthcare solutions

Challenges of regulation of AI applications and which components of a model can be regulated

What standard evaluation metrics do and do not provide

Join Free: Evaluations of AI Applications in Healthcare

There are 7 modules in this course

With artificial intelligence applications proliferating throughout the healthcare system, stakeholders are faced with both opportunities and challenges of these evolving technologies. This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare solutions.

In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

Fundamentals of Machine Learning for Healthcare

 


What you'll learn

Define important relationships between the fields of machine learning, biostatistics, and traditional computer programming.

Learn about advanced neural network architectures for tasks ranging from text classification to object detection and segmentation.

Learn important approaches for leveraging data to train, validate, and test machine learning models.

Understand how dynamic medical practice and discontinuous timelines impact clinical machine learning application development and deployment.

Join Free: Fundamentals of Machine Learning for Healthcare

There are 8 modules in this course

Machine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. But we will never realize the potential of these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles. 

This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. We will explore machine learning approaches, medical use cases, metrics unique to healthcare, as well as best practices for designing, building, and evaluating machine learning applications in healthcare.

The course will empower those with non-engineering backgrounds in healthcare, health policy, pharmaceutical development, as well as data science with the knowledge to critically evaluate and use these technologies.

Co-author: Geoffrey Angus
 
Contributing Editors:
Mars Huang
Jin Long
Shannon Crawford
Oge Marques


In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

Tuesday, 27 February 2024

Python Data Science Handbook: Essential Tools for Working with Data

 


Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all—IPython, NumPy, pandas, Matplotlib, Scikit-Learn, and other related tools.

Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

With this handbook, you'll learn how:

IPython and Jupyter provide computational environments for scientists using Python

NumPy includes the ndarray for efficient storage and manipulation of dense data arrays

Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data

Matplotlib includes capabilities for a flexible range of data visualizations

Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms

Join Free: Python Data Science Handbook: Essential Tools for Working with Data

Foundations of Data Science with Python (Chapman & Hall/CRC The Python Series)

 


Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality.

This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics. However, it is also intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science.

Key Features:

Applies a modern, computational approach to working with data

Uses real data sets to conduct statistical tests that address a diverse set of contemporary issues

Teaches the fundamentals of some of the most important tools in the Python data-science stack

Provides a basic, but rigorous, introduction to Probability and its application to Statistics

Offers an accompanying website that provides a unique set of online, interactive tools to help the reader learn the material

Hard Copy: Foundations of Data Science with Python (Chapman & Hall/CRC The Python Series)

Python for Data Analysis: From Basics to Advanced Data Science Techniques

 


Unlock the power of Python to analyze data, uncover insights, and drive decision-making with "Python for Data Analysis: From Basics to Advanced Data Science Techniques" Whether you're new to data analysis or looking to enhance your skills, this book offers a comprehensive journey through the tools, techniques, and concepts that make Python the go-to choice for data professionals.

Inside, you'll discover:

Foundational Python: Start from the basics of Python programming, including setting up your environment, understanding Python syntax, and exploring core concepts.

Mastering Pandas for Data Manipulation: Dive deep into Pandas for data cleaning, preparation, and manipulation, empowering you to handle and explore real-world datasets with ease.

Data Visualization Techniques: Learn to communicate your findings visually with Matplotlib and Seaborn, creating compelling and informative plots that bring your data to life.

Machine Learning Integration: Step into the world of machine learning with Scikit-Learn to apply predictive models to your data, from basic classification to complex regression tasks.

Advanced Data Analysis: Explore advanced topics, including working with big data using Dask, natural language processing (NLP), and an introduction to deep learning with TensorFlow and Keras.

Practical Projects and Case Studies: Apply what you've learned with hands-on projects and case studies that simulate real-world data analysis scenarios, enhancing your problem-solving skills and practical knowledge.

Future of Data Analysis: Look ahead to the emerging trends in data analysis and the ethical considerations of working with data, preparing you for the future of the field.

"Python for Data Analysis: From Basics to Advanced Data Science Techniques" is more than just a book; it's a comprehensive guide to becoming proficient in data analysis using Python. With clear explanations, practical examples, and step-by-step instructions, this book will equip you with the knowledge and skills you need to navigate the data landscape confidently and become an invaluable asset in your organization or field.

Hard Copy: Python for Data Analysis: From Basics to Advanced Data Science Techniques

Python for Data Science: A Hands-On Introduction

 

A hands-on, real-world introduction to data analysis with the Python programming language, loaded with wide-ranging examples.

Python is an ideal choice for accessing, manipulating, and gaining insights from data of all kinds. Python for Data Science introduces you to the Pythonic world of data analysis with a learn-by-doing approach rooted in practical examples and hands-on activities. You’ll learn how to write Python code to obtain, transform, and analyze data, practicing state-of-the-art data processing techniques for use cases in business management, marketing, and decision support.

You will discover Python’s rich set of built-in data structures for basic operations, as well as its robust ecosystem of open-source libraries for data science, including NumPy, pandas, scikit-learn, matplotlib, and more. Examples show how to load data in various formats, how to streamline, group, and aggregate data sets, and how to create charts, maps, and other visualizations. Later chapters go in-depth with demonstrations of real-world data applications, including using location data to power a taxi service, market basket analysis to identify items commonly purchased together, and machine learning to predict stock prices.

Hard Copy: Python for Data Science: A Hands-On Introduction


Data Engineering with AWS: Acquire the skills to design and build AWS-based data transformation pipelines like a pro 2nd ed. Edition

 


Looking to revolutionize your data transformation game with AWS? Look no further! From strong foundations to hands-on building of data engineering pipelines, our expert-led manual has got you covered.

Key Features

Delve into robust AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines

Stay up to date with a comprehensive revised chapter on Data Governance

Build modern data platforms with a new section covering transactional data lakes and data mesh

Book Description

This book, authored by a seasoned Senior Data Architect with 25 years of experience, aims to help you achieve proficiency in using the AWS ecosystem for data engineering. This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability.

You'll begin by reviewing the key concepts and essential AWS tools in a data engineer's toolkit and getting acquainted with modern data management approaches. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how that transformed data is used by various data consumers. You’ll learn how to ensure strong data governance, and about populating data marts and data warehouses along with how a data lakehouse fits into the picture. After that, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. Then, you'll explore how the power of machine learning and artificial intelligence can be used to draw new insights from data. In the final chapters, you'll discover transactional data lakes, data meshes, and how to build a cutting-edge data platform on AWS.

By the end of this AWS book, you'll be able to execute data engineering tasks and implement a data pipeline on AWS like a pro!

What you will learn

Seamlessly ingest streaming data with Amazon Kinesis Data Firehose

Optimize, denormalize, and join datasets with AWS Glue Studio

Use Amazon S3 events to trigger a Lambda process to transform a file

Load data into a Redshift data warehouse and run queries with ease

Visualize and explore data using Amazon QuickSight

Extract sentiment data from a dataset using Amazon Comprehend

Build transactional data lakes using Apache Iceberg with Amazon Athena

Learn how a data mesh approach can be implemented on AWS

Who this book is for

This book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts, while gaining practical experience with common data engineering services on AWS, will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book, but it’s not a prerequisite. Familiarity with the AWS console and core services will also help you follow along.

Table of Contents

An Introduction to Data Engineering

Data Management Architectures for Analytics

The AWS Data Engineer’s Toolkit

Data Governance, Security, and Cataloging

Architecting Data Engineering Pipelines

Ingesting Batch and Streaming Data

Transforming Data to Optimize for Analytics

Identifying and Enabling Data Consumers

A Deeper Dive into Data Marts and Amazon Redshift

Orchestrating the Data Pipeline

Hard Copy: Data Engineering with AWS: Acquire the skills to design and build AWS-based data transformation pipelines like a pro 2nd ed. Edition



Monday, 26 February 2024

Python Coding challenge - Day 138 | What is the output of the following Python Code?

 


The above code deletes elements from index 2 to index 3 (not including index 4) in the list num and then prints the updated list. Let's break it down:

num = [10, 20, 30, 40, 50]

This line initializes a list named num with the elements 10, 20, 30, 40, and 50.

del(num[2:4])

This line uses the del statement to delete elements from index 2 up to (but not including) index 4 in the list. So, it removes the elements at index 2 and 3 (30 and 40) from the list.

After this operation, the list num becomes [10, 20, 50].

print(num)

Finally, the code prints the updated list, which is [10, 20, 50].

So, the output of the code will be:

[10, 20, 50]

IBM Data Analytics with Excel and R Professional Certificate

 


What you'll learn

Master the most up-to-date practical skills and knowledge data analysts use in their daily roles

Learn how to perform data analysis, including data preparation, statistical analysis, and predictive modeling using R, R Studio, and Jupyter

Utilize Excel spreadsheets to perform a variety of data analysis tasks like data wrangling, using pivot tables, data mining, & creating charts

Communicate your data findings using various data visualization techniques including, charts, plots & interactive dashboards with Cognos and R Shiny

Join Free: IBM Data Analytics with Excel and R Professional Certificate

Professional Certificate - 9 course series

Prepare for the in-demand field of data analytics. In this program, you’ll learn high valued skills like Excel, Cognos Analytics, and R programming language to get job-ready in less than 3 months.

Data analytics is a strategy-based science where data is analyzed to find trends, answer questions, shape business processes, and aid decision-making. This Professional Certificate focuses on data analysis using Microsoft Excel and R programming language. If you’re interested in using Python, please explore the IBM Data Analyst PC. 

This program will teach you the foundational data skills employers are seeking for entry level data analytics roles and will provide a portfolio of projects and a Professional Certificate from IBM to showcase your expertise to potential employers.

You’ll learn the latest skills and tools used by professional data analysts and upon successful completion of this program, you will be able to work with Excel spreadsheets, Jupyter Notebooks, and R Studio to analyze data and create visualizations. You will also use the R programming language to complete the entire data analysis process,  including data preparation, statistical analysis, data visualization, predictive modeling and creating interactive dashboards. Lastly, you’ll learn how to communicate your data findings and prepare a summary report.

This program is ACE® and FIBAA recommended—when you complete, you can earn up to 15 college credits and 4 ECTS credits.

Applied Learning Project

You will complete hands-on labs to build your portfolio and  gain practical experience with Excel, Cognos Analytics, SQL, and the R programing language and related libraries for data science, including Tidyverse, Tidymodels, R Shiny, ggplot2, Leaflet, and rvest.

Projects include:

Analyzing fleet vehicle inventory data using pivot tables.

Using key performance indicator (KPI) data from car sales to create an interactive dashboard.

Identifying patterns in countries’ COVID-19 testing data rates using R.

Using SQL with the RODBC R package to analyze foreign grain markets.

Creating linear and polynomial regression models and comparing them with weather station data to predict precipitation.

Using the R Shiny package to create a dashboard that examines trends in census data.

Using hypothesis testing and predictive modeling skills to build an interactive dashboard with the R Shiny package and a dynamic Leaflet map widget to investigate how weather affects bike-sharing demand.

Predict Sales Revenue with scikit-learn

 


What you'll learn

Build simple linear regression models in Python

Apply scikit-learn and statsmodels to regression problems

Employ explorartory data analysis (EDA) with seaborn and pandas

Explain linear regression to both technical and non-technical audiences

Join Free: Predict Sales Revenue with scikit-learn

About this Guided Project

In this 2-hour long project-based course, you will build and evaluate a simple linear regression model using Python. You will employ the scikit-learn module for calculating the linear regression, while using pandas for data management, and seaborn for plotting. You will be working with the very popular Advertising data set to predict sales revenue based on advertising spending through mediums such as TV, radio, and newspaper. 

By the end of this course, you will be able to:

- Explain the core ideas of linear regression to technical and non-technical audiences
- Build a simple linear regression model in Python with scikit-learn
- Employ Exploratory Data Analysis (EDA) to small data sets with seaborn and pandas
- Evaluate a simple linear regression model using appropriate metrics

This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Jupyter and Python 3.7 with all the necessary libraries pre-installed.

Notes:

- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
- This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Generative AI: Enhance your Data Analytics Career

 


What you'll learn

Describe how you can use Generative AI tools and techniques in the context of data analytics across industries

Implement various data analytic processes such as data preparation, analysis, visualization and storytelling using Generative AI tools

Evaluate real-world case studies showcasing the successful application of Generative AI in deriving meaningful insights 

 Analyze the ethical considerations and challenges associated with using Generative AI in data analytics

Join Free: Generative AI: Enhance your Data Analytics Career

There are 3 modules in this course

This comprehensive course unravels the potential of generative AI in data analytics. The course will provide an in-depth knowledge of the fundamental concepts, models, tools, and generative AI applications regarding the data analytics landscape. 

In this course, you will examine real-world applications and use generative AI to gain data insights using techniques such as prompts, visualization, storytelling, querying and so on. In addition, you will understand the ethical implications, considerations, and challenges of using generative AI in data analytics across different industries.

You will acquire practical experience through hands-on labs where you will leverage generative AI models and tools such as ChatGPT, ChatCSV, Mostly.AI, SQLthroughAI and more.

Finally, you will apply the concepts learned throughout the course to a data analytics project. Also, you will have an opportunity to test your knowledge with practice and graded quizzes and earn a certificate. 

This course is suitable for both practicing data analysts as well as learners aspiring to start a career in data analytics. It requires some basic knowledge of data analytics, prompt engineering, Python programming and generative artificial intelligence.

Data Analyst Career Guide and Interview Preparation

 


What you'll learn

Describe the role of a data analyst and some career path options as well as the prospective opportunities in the field.

Explain how to build a foundation for a job search, including researching job listings, writing a resume, and making a portfolio of work.

Summarize what a candidate can expect during a typical job interview cycle, different types of interviews, and how to prepare for interviews.

Explain how to give an effective interview, including techniques for answering questions and how to make a professional personal presentation.

Join Free: Data Analyst Career Guide and Interview Preparation

There are 4 modules in this course

Data analytics professionals are in high demand around the world, and the trend shows no sign of slowing. There are lots of great jobs available, but lots of great candidates too. How can you get the edge in such a competitive field?

This course will prepare you to enter the job market as a great candidate for a data analyst position. It provides practical techniques for creating essential job-seeking materials such as a resume and a portfolio, as well as auxiliary tools like a cover letter and an elevator pitch. You will learn how to find and assess prospective job positions, apply to them, and lay the groundwork for interviewing. 

The course doesn’t stop there, however. You will also get inside tips and steps you can use to perform professionally and effectively at interviews. You will learn how to approach a take-home challenges and get to practice completing them. Additionally, it provides information about the regular functions and tasks of data analysts, as well as the opportunities of the profession and some options for career development.

You will get guidance from a number of experts in the data industry through the course. They will discuss their own career paths and talk about what they have learned about networking, interviewing, solving coding problems, and fielding other questions you may encounter as a candidate. Let seasoned data analysis professionals share their experience to help you get ahead and land the job you want.

Machine Learning With Big Data

 


Build your subject-matter expertise

This course is part of the Big Data 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: Machine Learning With Big Data

There are 7 modules in this course

Want to make sense of the volumes of data you have collected?  Need to incorporate data-driven decisions into your process?  This course provides an overview of machine learning techniques to explore, analyze, and leverage data.  You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.

At the end of the course, you will be able to:

Design an approach to leverage data using the steps in the machine learning process.
Apply machine learning techniques to explore and prepare data for modeling.
Identify the type of machine learning problem in order to apply the appropriate set of techniques.
Construct models that learn from data using widely available open source tools.
Analyze big data problems using scalable machine learning algorithms on Spark.

Sunday, 25 February 2024

Python Coding challenge - Day 137 | What is the output of the following Python Code?

 


a = [1, 2, 3, 4]

b = [1, 2, 5]

print(a < b)

Two lists, a and b, are defined.

a is [1, 2, 3, 4]

b is [1, 2, 5]

The code uses the less-than (<) operator to compare the two lists a and b. This comparison is performed element-wise.

The first elements of both lists are equal (1 == 1).

The second elements are equal (2 == 2).

The third elements are different (3 in a and 5 in b).

The less-than comparison stops at the first differing element. Since 3 is less than 5, the entire comparison evaluates to True.

The result of the comparison is printed using print(a < b), and it will output True.

So, the output of the code is:

True

This is because, in lexicographical order, the list a is considered less than the list b due to the first differing element at index 2.

Saturday, 24 February 2024

3D contour plot using Python

 


import numpy as np

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Create a meshgrid of x and y values
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)

# Define a function to calculate the z values (height) based on x and y
def f(x, y):
    return np.sin(np.sqrt(x**2 + y**2))

# Calculate the z values for the meshgrid
Z = f(X, Y)

# Create a three-dimensional contour plot
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111, projection='3d')
contour = ax.contour3D(X, Y, Z, 50, cmap='viridis')

# Add labels and a colorbar
ax.set_xlabel('X-axis')
ax.set_ylabel('Y-axis')
ax.set_zlabel('Z-axis')
fig.colorbar(contour, ax=ax, label='Z values')

# Show the plot
plt.show()

#clcoding.com

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Person climbing a staircase. Learn Data Science from Scratch: online program with 21 courses