Showing posts with label Python. Show all posts
Showing posts with label Python. Show all posts

Thursday 29 February 2024

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


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]

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

Wednesday 21 February 2024

Word cloud using Python Libraries

 



from wordcloud import WordCloud

import matplotlib.pyplot as plt

# Read text from a file
with open('cl.txt', 'r', encoding='utf-8') as file:
    text = file.read()

# Generate word cloud
wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)

# Display the generated word cloud using matplotlib
plt.figure(figsize=(10, 5))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()

#clcoding.com

Tuesday 20 February 2024

Cybersecurity Attack and Defense Fundamentals Specialization

 


What you'll learn

Information security threats, vulnerabilities, and attacks.

Network security assessment techniques and tools.

Computer forensics fundaments, digital evidence, and forensic investigation phases.

Join Free: Cybersecurity Attack and Defense Fundamentals Specialization

Specialization - 3 course series

This Specialization can be taken by students, IT professionals, IT managers, career changers, and anyone who seeks a cybersecurity career or aspires to advance their current role. This course is ideal for those entering the cybersecurity workforce, providing foundational, hands-on skills to solve the most common security issues organizations face today.


This 3-course Specialization will help you gain core cybersecurity skills needed to protect critical data, networks, and digital assets. You will learn to build the foundation that enables individuals to grow their skills in specialized domains like penetration testing, security consulting, auditing, and system and network administration. 

Applied Learning Project

Learn to troubleshoots  network security problems, monitor alerts, and follow policies, procedures, and standards to protect information assets. You will gain practical skills cybersecurity professionals need in Information Security, Network Security, Computer Forensics, Risk Management, Incident Handling, and the industry best practices.

Cybersecurity: Developing a Program for Your Business Specialization

 


Advance your subject-matter expertise

Learn in-demand skills from university and industry experts

Master a subject or tool with hands-on projects

Develop a deep understanding of key concepts

Earn a career certificate from University System of Georgia

Join Free: Cybersecurity: Developing a Program for Your Business Specialization

Specialization - 4 course series

Cybersecurity is an essential business skill for the evolving workplace. For-profit companies, government agencies, and not-for-profit organizations all need technologically proficient, business-savvy information technology security professionals. In this Specialization, you will learn about  a variety of processes for protecting business assets through policy, education and training, and technology best practices. You’ll develop an awareness of the risks and cyber threats or attacks associated with modern information usage, and explore key technical and managerial topics required for a balanced approach to information protection. Topics will include mobility, the Internet of Things, the human factor,  governance and management practices.

Enterprise and Infrastructure Security

 


Build your subject-matter expertise

This course is part of the Introduction to Cyber Security 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: Enterprise and Infrastructure Security

There are 4 modules in this course

This course introduces a series of advanced and current topics in cyber security, many of which are especially relevant in modern enterprise and infrastructure settings. The basics of enterprise compliance frameworks are provided with introduction to NIST and PCI. Hybrid cloud architectures are shown to provide an opportunity to fix many of the security weaknesses in modern perimeter local area networks.

Emerging security issues in blockchain, blinding algorithms, Internet of Things (IoT), and critical infrastructure protection are also described for learners in the context of cyber risk. Mobile security and cloud security hyper-resilience approaches are also introduced. The course completes with some practical advice for learners on how to plan careers in cyber security.

Introduction to Python for Cybersecurity

 


Build your subject-matter expertise

This course is part of the Python for Cybersecurity 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: Introduction to Python for Cybersecurity

There are 3 modules in this course

This course it the first part of the Python for Cybersecurity Specialization. Learners will get an introduction and overview of the course format and learning objectives.

Monday 19 February 2024

Python for Finance: Beta and Capital Asset Pricing Model


 What you'll learn

Understand the theory and intuition behind the Capital Asset Pricing Model (CAPM)

Calculate Beta and expected returns of securities in python

Perform interactive data visualization using Plotly Express

Join Free: Python for Finance: Beta and Capital Asset Pricing Model

About this Guided Project

In this project, we will use Python to perform stocks analysis such as calculating stock beta and expected returns using the Capital Asset Pricing Model (CAPM). CAPM is one of the most important models in Finance and it describes the relationship between the expected return and risk of securities. We will analyze the performance of several companies such as Facebook, Netflix, Twitter and AT&T over the past 7 years. This project is crucial for investors who want to properly manage their portfolios, calculate expected returns, risks, visualize datasets, find useful patterns, and gain valuable insights. This project could be practically used for analyzing company stocks, indices or  currencies and performance of portfolio.

Note: 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.

Sunday 18 February 2024

Introduction to Python for Civil Engineers: a Beginner’s Guide

 


This book serves as a means to bridge the gap between civil engineering and programming skills, with a specific focus on Python. Python is highly regarded among users due to its user-friendly nature, making it applicable in a wide range of subjects such as:

• Data mining

• Big data problems

• Artificial intelligence

• Machine learning

• Engineering calculations

To master Python and acquire comprehensive knowledge, it is crucial to embark on your journey with a well-scripted book. Our aim in writing this book was to provide abundant examples that cater specifically to individuals with basic knowledge in civil engineering.

Now, why do civil engineers need to acquire knowledge about Python? The answer is simple: depending on the field a civil engineering graduate chooses to pursue, having Python skills can be pivotal. For instance, structural health monitoring is currently a trending topic, and effectively interpreting collocated data requires proficiency in data manipulation, visualization, and optimization techniques. Therefore, possessing these capabilities greatly increases your chances of securing your dream job.

"Introduction to Python for Civil Engineers: A Beginner's Guide" offers simple and thorough explanations of the basics, accompanied by numerous examples. After covering the fundamentals, the book delves into the useful and essential features of popular libraries including Numpy, Pandas, Matplotlib, and Scipy. Abundant examples and mini-projects are provided to enhance your understanding of these concepts. Additionally, the book includes four real-world projects with step-by-step solutions, guiding you through your very first hands-on training experiences.

So, what are you waiting for? Start your learning journey today!


Hard Copy: Introduction to Python for Civil Engineers: a Beginner’s Guide




The Amazing Technique of Returning Results in Python Functions

 


1. Single Return Value:

def add_numbers(a, b):
    result = a + b
    return result
sum_result = add_numbers(3, 4)
print(sum_result)  # Output: 7
#clcoding.com
7

2. Multiple Return Values:

def operate_numbers(a, b):
    addition = a + b
    subtraction = a - b
    multiplication = a * b
    return addition, subtraction, multiplication
result_tuple = operate_numbers(5, 3)
print(result_tuple)
# Output: (8, 2, 15)
# Unpack the tuple
add_result, sub_result, mul_result = operate_numbers(5, 3)
print(add_result, sub_result, mul_result)
# Output: 8 2 15
#clcoding.com
(8, 2, 15)
8 2 15

3. Returning a Dictionary :

def get_person_info(name, age):
    person_info = {'Name': name, 'Age': age}
    return person_info
info_dict = get_person_info('John', 30)
print(info_dict)
# Output: {'Name': 'John', 'Age': 30}
#clcoding.com
{'Name': 'John', 'Age': 30}

4. Returning None:

def simple_function():
    print("This function does something")
result = simple_function()
print(result)  # Output: None
#clcoding.com
This function does something
None

5. Returning Early:

def divide(a, b):
    if b == 0:
        print("Cannot divide by zero.")
        return  # Exit the function early
    result = a / b
    return result
result = divide(8, 2)
print(result)  # Output: 4.0
#clcoding.com
4.0

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