Financial Machine Learning (Foundations and Trends(r) in Finance)

  

Financial Machine Learning surveys the nascent literature on machine learning in the study of financial markets. The authors highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.

This survey is organized as follows. Section 2 analyzes the theoretical benefits of highly parameterized machine learning models in financial economics. Section 3 surveys the variety of machine learning methods employed in the empirical analysis of asset return predictability. Section 4 focuses on machine learning analyses of factor pricing models and the resulting empirical conclusions for risk-return tradeoffs. Section 5 presents the role of machine learning in identifying optimal portfolios and stochastic discount factors. Section 6 offers brief conclusions and directions for future work.

PDF: Financial Machine Learning (Foundations and Trends(r) in Finance)

Hard Copy: Financial Machine Learning (Foundations and Trends(r) in Finance)

Free Courses Machine learning for Finance 

Fundamentals of Machine Learning in Finance https://www.clcoding.com/2024/02/fundamentals-of-machine-learning-in.html

Python and Machine Learning for Asset Management 

https://www.clcoding.com/2024/02/python-and-machine-learning-for-asset_19.html

Guided Tour of Machine Learning in Finance https://www.clcoding.com/2024/02/guided-tour-of-machine-learning-in.html

Python and Machine-Learning for Asset Management with Alternative Data Sets https://www.clcoding.com/2024/02/python-and-machine-learning-for-asset.html

Python for Finance: Beta and Capital Asset Pricing Model https://www.clcoding.com/2024/02/python-for-finance-beta-and-capital.html

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Python Coding challenge - Day 136 | What is the output of the following Python Code?

 

The code creates a list data with elements [1, 2, 3, 4] and then creates a copy of this list called backup_data using the copy() method. After that, it modifies the fourth element of the original data list by setting it to 7. Finally, it prints the backup_data list.

Let's analyze the code step by step:

data = [1, 2, 3, 4]: Initializes a list named data with elements [1, 2, 3, 4].

backup_data = data.copy(): Creates a shallow copy of the data list and assigns it to backup_data. Both lists will initially contain the same elements.

data[3] = 7: Modifies the fourth element of the data list, changing it from 4 to 7.

print(backup_data): Prints the backup_data list. Since it's a copy made before the modification, it will not reflect the change made to the data list.

So, when you run this code, the output will be:

[1, 2, 3, 4]

This is because the modification of the data list does not affect the backup_data list, as it was created as a separate copy.

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10-question multiple-choice quiz on Pandas

 1. What is Pandas?

a. A data visualization library

b. A web development framework

c. A data manipulation library

d. A machine learning framework

2.  What is the primary data structure in Pandas for one-dimensional labeled data?

a. Series

b. DataFrame

c. Array

d. List

3. How do you read a CSV file into a Pandas DataFrame?

a. pd.load_csv()

b. pd.read_csv()

c. pd.read_data()

d. pd.import_csv()

4. How do you select a specific column in a Pandas DataFrame?

a. df.column('ColumnName')

b. df.select('ColumnName')

c. df['ColumnName']

d. df.get('ColumnName')

5. What is the purpose of the head() method in Pandas?

a. It gives the first few rows of the DataFrame

b. It returns the last rows of the DataFrame

c. It displays a summary statistics of the DataFrame

d. It provides information about the columns in the DataFrame

6. How do you handle missing values in a Pandas DataFrame?

a. Use the fillna() method

b. Use the remove_na() method

c. Use the drop_na() method

d. Pandas automatically handles missing values

7. What function is used to group data in Pandas based on one or more columns?

a. groupby()

b. aggregate()

c. sort()

d. combine()

8. How do you merge two DataFrames in Pandas based on a common column?

a. df.merge()

b. df.join()

c. df.concat()

d. df.combine()

9. What does the describe() method in Pandas provide?

a. Descriptive statistics of the DataFrame

b. A list of unique values in each column

c. Information about data types in the DataFrame

d. A summary of missing values in the DataFrame

10. What is the purpose of the to_csv() method in Pandas?

a. It saves the DataFrame to a CSV file

b. It converts the DataFrame to a Series

c. It exports the DataFrame to an Excel file

d. It prints the DataFrame to the console

Answer:

1. c, 

2. a, 

3. b, 

4. c, 

5. a, 

6. a, 

7. a, 

8. a, 

9. a, 

10. a

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Python Coding challenge - Day 135 | What is the output of the following Python Code?

 

Let's break down the code:

from random import *

x = [0, 2, 4]

print(sample(x, 2))

from random import *: This line imports all functions from the random module. This means you can use functions from the random module without prefixing them with random..

x = [0, 2, 4]: A list named x is defined with elements 0, 2, and 4.

sample(x, 2): The sample function is called with two arguments - the population (which is the list x), and the number of elements to be randomly chosen (2 in this case). The sample function returns a new list containing unique elements randomly chosen from the population.

print(...): The result of the sample function is printed.

So, when you run this code, it will output a list containing 2 randomly selected elements from the list x. The output will vary each time you run the code due to the random selection. For example, it might output [2, 4] or [0, 4], etc.

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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

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Python Coding challenge - Day 134 | What is the output of the following Python Code?

 

The above code is a list comprehension in Python. It creates a new list where each element is the cube of the corresponding element in the original list a.

Here's the breakdown of the code:

a = [-2, -1, 0, 1, 2]: Initializes a list a with the values -2, -1, 0, 1, and 2.

print([i**3 for i in a]): Uses a list comprehension to create a new list by cubing each element in the original list a. The expression i**3 calculates the cube of each element i. The resulting list is then printed.

Output:

[-8, -1, 0, 1, 8]

So, the printed list is [-8, -1, 0, 1, 8], which represents the cubes of the elements in the original list a.

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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.

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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.

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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.

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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.

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Security Analyst Fundamentals Specialization

 

What you'll learn

Develop knowledge in digital forensics, incident response and penetration testing.

Advance your knowledge of cybersecurity analyst tools including data and endpoint protection; SIEM; and systems and network fundamentals.  

Get hands-on experience to develop skills  via industry specific and open source Security tools.

Apply your skills to investigate a real-world security breach identifying the attack, vulnerabilities, costs and prevention recommendations.

Join Free: Security Analyst Fundamentals Specialization

Specialization - 3 course series

There are a growing number of exciting, well-paying jobs in today’s security industry that do not require a traditional college degree. Forbes estimates that there will be as many as 3.5 million unfilled positions in the industry worldwide by 2021! One position with a severe shortage of skills is as a cybersecurity analyst.

Throughout this specialization, you will learn concepts around digital forensics, penetration testing and incident response.  You will learn about threat intelligence and tools to gather data to prevent an attack or in the event your organization is attacked.  You will have the opportunity to review some of the largest breach cases and try your hand at reporting on a real world breach.  

The content creators and instructors are architects , Security Operation Center (SOC) analysts, and distinguished engineers who work with cybersecurity in their day to day lives at IBM with a worldwide perspective. They will share their skills which they need to secure IBM and its clients security systems.

The completion of this specialization also makes you eligible to earn the System Analyst Fundamentals IBM digital badge. More information about the badge can be found here:

https://www.youracclaim.com/org/ibm/badge/security-analyst-fundamentals         

Applied Learning Project

Throughout the program, you will use virtual labs and internet sites that will provide you with practical skills with applicability to real jobs that employers value, including:

Tools: e.g. Wireshark, IBM QRadar, IBM MaaS360, IBM Guardium, IBM Resilient, i2 Enterprise Insight 

 Labs: SecurityLearningAcademy.com

Libraries: Python

Projects: Investigate a real-world security breach identifying the attack, vulnerabilities, costs and prevention recommendations.

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Advanced Django: Advanced Django Rest Framework

 

What you'll learn

Optimize the Django Rest Framework

Integrate with ReactJS

Join Free: Advanced Django: Advanced Django Rest Framework

There are 4 modules in this course

Code and run Django websites without installing anything!

This course is designed for learners who are familiar with Python and basic Django skills (similar to those covered in the Django for Everybody specialization). The modules in this course cover testing, performance considerations such as caching and throttling, use of 3rd party libraries, and integrating frontends within the context of the Django REST framework.

To allow for a truly hands-on, self-paced learning experience, this course is video-free. Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You’ll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to slowly building features, resulting in large coding projects at the end of the course.

Course Learning Objectives: 

Write and run tests on Django applications

Optimize code performance using caching, throttling, and filtering

Use a 3rd Party library

Integrate with common Frontends

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Select Topics in Python Specialization

 

What you'll learn

Create websites with Django

Create charts and plots with Matplotlib and Jupyter notebooks

Create a chatbot with the NLTK library

Join Free: Select Topics in Python Specialization

Specialization - 4 course series

This specialization is intended for people who are interested in furthering their Python skills. It is assumed that students are familiar with Python and have taken the Programming in Python: A Hands-On Tutorial.

These four courses cover a wide range of topics. Learn how to create and manage Python package. Use Jupyter notebooks to visualize data with Matplotlib. The third course focuses on the basics of the Django web framework. Finally, learn how to leverage Python for natural langauge processing.

Applied Learning Project

Learners create a variety of projects from their own Python packages, as well as use third-party package management tools. They also transform data into different charts and plots. In the Django course, learners build three simple websites. Finally, natural language processing powers a chatbot that learners build.

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Web Applications and Command-Line Tools for Data Engineering

 

What you'll learn

Construct Python Microservices with FastAPI

Build a Command-Line Tool in Python using Click

Compare multiple ways to set up and use a Jupyter notebook

Join Free: Web Applications and Command-Line Tools for Data Engineering

There are 4 modules in this course

In this fourth course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will build upon the data engineering concepts introduced in the first three courses to apply Python, Bash and SQL techniques in tackling real-world problems. First, we will dive deeper into leveraging Jupyter notebooks to create and deploy models for machine learning tasks. Then, we will explore how to use Python microservices to break up your data warehouse into small, portable solutions that can scale. Finally, you will build a powerful command-line tool to automate testing and quality control for publishing and sharing your tool with a data registry.

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Database Engineer Capstone

 

What you'll learn

Build a MySQL database solution.

Deploy level-up ideas to enhance the scope of a database project.

Join Free: Database Engineer Capstone

There are 4 modules in this course

In this course you’ll complete a capstone project in which you’ll create a database and client for Little Lemon restaurant.

To complete this course, you will need database engineering experience.  

The Capstone project enables you to demonstrate multiple skills from the Certificate by solving an authentic real-world problem. Each module includes a brief recap of, and links to, content that you have covered in previous courses in this program. 

In this course, you will demonstrate your new skillset by designing and composing a database solution, combining all the skills and technologies you've learned throughout this program to solve the problem at hand. 

By the end of this course, you’ll have proven your ability to:

-Set up a database project,

-Add sales reports,

-Create a table booking system,

-Work with data analytics and visualization,

-And create a database client.

You’ll also demonstrate your ability with the following tools and software:

-Git,

-MySQL Workbench,

-Tableau,

-And Python.

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Web Application Technologies and Django

 

What you'll learn

Explain the basics of HTTP and how the request-response cycle works

Install and deploy a simple DJango application

Build simple web pages in HTML and style them using CSS

Explain the basic operations in SQL

Join Free: Web Application Technologies and Django

There are 5 modules in this course

In this course, you'll explore the basic structure of a web application, and how a web browser interacts with a web server. You'll be introduced to the Hypertext Transfer Protocol (HTTP) request/response cycle, including GET/POST/Redirect. You'll also gain an introductory understanding of Hypertext Markup Language (HTML), as well as the overall structure of a Django application.  We will explore the Model-View-Controller (MVC) pattern for web applications and how it relates to Django.  You will learn how to deploy a Django application using a service like PythonAnywhere so that it is available over the Internet. 

This is the first course in the Django for Everybody specialization. It is recommended that you complete the Python for Everybody specialization or an equivalent learning experience before beginning this series.

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Python Coding challenge - Day 133 | What is the output of the following Python Code?

 

The above code assigns the string '2\t4' to the variable x and then prints the value of x. The string '2\t4' contains the characters '2', '\', 't', and '4'.

When you print the value of x, it will display:

2\t4

The '\t' in the string represents the escape sequence for a tab character, so when you print it, you'll see a tab between '2' and '4' in the output.

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Fundamentals of Machine Learning in Finance

 

Build your subject-matter expertise

This course is part of the Machine Learning and Reinforcement Learning in Finance 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: Fundamentals of Machine Learning in Finance

There are 4 modules in this course

The course aims at helping students to be able to solve practical ML-amenable problems that they may encounter in real life that include: (1) understanding where the problem one faces lands on a general landscape of available ML methods, (2) understanding which particular ML approach(es) would be most appropriate for resolving the problem, and (3) ability to successfully implement a solution, and assess its performance.  

A learner with some or no previous knowledge of Machine Learning (ML)  will get to know main algorithms of Supervised and Unsupervised Learning, and Reinforcement Learning, and will be able to use ML open source Python packages to design, test, and implement ML algorithms in Finance.

Fundamentals of Machine Learning in Finance will provide more at-depth view of supervised, unsupervised, and reinforcement learning, and end up in a project on using unsupervised learning for implementing a simple portfolio trading strategy.

The course is designed for three categories of students:

Practitioners working at financial institutions such as banks, asset management firms or hedge funds

Individuals interested in applications of ML for personal day trading

Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance  

Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.

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Python and Machine Learning for Asset Management

 

What you'll learn

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

Understand the basis of logistical regression and ML algorithms for classifying variables into one of two outcomes    

Utilize powerful Python libraries to implement machine learning algorithms in case studies    

Learn about factor models and regime switching models and their use in investment management    \

Join Free: Python and Machine Learning for Asset Management

There are 5 modules in this course

This course will enable you mastering machine-learning approaches in the area of investment management. It has been designed by two thought leaders in their field, Lionel Martellini from EDHEC-Risk Institute and John Mulvey from Princeton University. Starting from the basics, they will help you build practical skills to understand data science so you can make the best portfolio decisions.

The course will start with an introduction to the fundamentals of machine learning, followed by an in-depth discussion of the application of these techniques to portfolio management decisions, including the design of more robust factor models, the construction of portfolios with improved diversification benefits, and the implementation of more efficient risk management models. 

We have designed a 3-step learning process: first, we will introduce a meaningful investment problem and see how this problem can be addressed using statistical techniques. Then, we will see how this new insight from Machine learning can complete and improve the relevance of the analysis.

You will have the opportunity to capitalize on videos and recommended readings to level up your financial expertise, and to use the quizzes and Jupiter notebooks to ensure grasp of concept.

At the end of this course, you will master the various machine learning techniques in investment management.

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Guided Tour of Machine Learning in Finance

 

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Python and Machine-Learning for Asset Management with Alternative Data Sets

 

What you'll learn

Learn what alternative data is and how it is used in financial market applications. 

Become immersed in current academic and practitioner state-of-the-art research pertaining to alternative data applications.

Perform data analysis of real-world alternative datasets using Python.

Gain an understanding and hands-on experience in data analytics, visualization and quantitative modeling applied to alternative data in finance

Join Free: Python and Machine-Learning for Asset Management with Alternative Data Sets

There are 4 modules in this course

Over-utilization of market and accounting data over the last few decades has led to portfolio crowding, mediocre performance and systemic risks, incentivizing financial institutions which are looking for an edge to quickly adopt alternative data as a substitute to traditional data. This course introduces the core concepts around alternative data, the most recent research in this area, as well as practical portfolio examples and actual applications. The approach of this course is somewhat unique because while the theory covered is still a main component, practical lab sessions and examples of working with alternative datasets are also key. This course is fo you if you are aiming at carreers prospects as a data scientist in financial markets, are looking to enhance your analytics skillsets to the financial markets, or if you are interested in cutting-edge technology and research as  they apply to big data. The required background is: Python programming, Investment theory , and Statistics. This course will enable you to learn new data and research techniques applied to the financial markets while strengthening data science and python skills.

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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.

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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

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Python Coding challenge - Day 132 | What is the output of the following Python Code?

 

Let's break down the expression:

a = True

b = False

print(a == b or not b)

a == b: This checks if the value of a is equal to the value of b. In this case, True == False evaluates to False.

not b: This negates the value of b. Since b is False, not b evaluates to True.

a == b or not b: The or operator returns True if at least one of the conditions is True. In this case, False or True evaluates to True.

So, the output of the print statement will be True.

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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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Box and Whisker plot using Python

 

#!/usr/bin/env python

# coding: utf-8

# # Box and whisker plot using Python

# # 1. Matplotlib:

# In[1]:

import matplotlib.pyplot as plt

# Sample data

data = [7, 2, 15, 9, 12, 4, 11, 8, 13, 6]

# Create boxplot

plt.boxplot(data)

# Customize labels and title

plt.xlabel("Data")

plt.ylabel("Value")

plt.title("Boxplot with Matplotlib")

plt.show()

# # 2. Pandas:

# In[2]:

import pandas as pd

import matplotlib.pyplot as plt

# Sample DataFrame

data = pd.DataFrame({"values": [7, 2, 15, 9, 12, 4, 11, 8, 13, 6]})

# Create boxplot

data.plot.box()

# Customize labels and title

plt.xlabel("Data")

plt.ylabel("Value")

plt.title("Boxplot with Pandas")

plt.show()

# # 3. Seaborn:

# In[3]:

import seaborn as sns

# Sample data (same as before)

data = [7, 2, 15, 9, 12, 4, 11, 8, 13, 6]

# Create boxplot

sns.boxplot(data=data)

# Customize with hue (category) plot

data = {"category": ["A", "B", "A", "A", "B", "A", "A", "B", "B", "A"], "values": data}

sns.boxplot(x="category", y="values", data=data)

plt.show()

# In[ ]:

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Python Coding challenge - Day 131 | What is the output of the following Python Code?

 

Let's break down the code step by step:

x = 'Monday'

In this line, a variable x is assigned the value 'Monday'. The variable x now holds the string 'Monday'.

print('Mon' in x)

This line uses the print function to output the result of the expression 'Mon' in x. The in keyword is used here to check if the substring 'Mon' is present in the string x.

Here's how it works:

'Mon' is a string representing the substring we are looking for.

x is the string 'Monday' that we are searching within.

The expression 'Mon' in x evaluates to True if the substring 'Mon' is found anywhere within the string 'Monday', and False otherwise.

In this case, since 'Mon' is a part of 'Monday', the result of the expression is True. Therefore, the print function will output True to the console.

So, when you run this code, the output will be:

True

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Computer Architecture

 

There are 21 modules in this course

In this course, you will learn to design the computer architecture of complex modern microprocessors.

Join Free: Computer Architecture

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What will be the output after the following statements? x = 'Python Pi Py Pip' print(x.count('p'))

 

x = 'Python Pi Py Pip'

print(x.count('p'))

The code defines a string variable named x and assigns the value 'Python Pi Py Pip' to it. Then, it uses the count() method to count the number of occurrences of the letter 'p' in the string. The count() method returns an integer value, which is the number of times the specified substring is found within the string.

In this case, the string 'Python Pi Py Pip' contains one lowercase letter 'p'. Therefore, the output of the code is:

1

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Bayesian Analysis with Python - Third Edition: A practical guide to probabilistic modeling

 

Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these libraries

Key Features:

• Conduct Bayesian data analysis with step-by-step guidance
• Gain insight into a modern, practical, and computational approach to Bayesian statistical modeling
• Enhance your learning with best practices through sample problems and practice exercises
• Purchase of the print or Kindle book includes a free PDF eBook.

Book Description:

The third edition of Bayesian Analysis with Python serves as an introduction to the main concepts of applied Bayesian modeling using PyMC, a state-of-the-art probabilistic programming library, and other libraries that support and facilitate modeling like ArviZ, for exploratory analysis of Bayesian models; Bambi, for flexible and easy hierarchical linear modeling; PreliZ, for prior elicitation; PyMC-BART, for flexible non-parametric regression; and Kulprit, for variable selection.

In this updated edition, a brief and conceptual introduction to probability theory enhances your learning journey by introducing new topics like Bayesian additive regression trees (BART), featuring updated examples. Refined explanations, informed by feedback and experience from previous editions, underscore the book's emphasis on Bayesian statistics. You will explore various models, including hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes, and BART, using synthetic and real datasets.

By the end of this book, you will possess a functional understanding of probabilistic modeling, enabling you to design and implement Bayesian models for your data science challenges. You'll be well-prepared to delve into more advanced material or specialized statistical modeling if the need arises.

What You Will Learn:

• Build probabilistic models using PyMC and Bambi
• Analyze and interpret probabilistic models with ArviZ
• Acquire the skills to sanity-check models and modify them if necessary
• Build better models with prior and posterior predictive checks
• Learn the advantages and caveats of hierarchical models
• Compare models and choose between alternative ones
• Interpret results and apply your knowledge to real-world problems
• Explore common models from a unified probabilistic perspective
• Apply the Bayesian framework's flexibility for probabilistic thinking

Who this book is for:

If you are a student, data scientist, researcher, or developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory, so no previous statistical knowledge is required, although some experience in using Python and scientific libraries like NumPy is expected.

Hard Copy:  Bayesian Analysis with Python - Third Edition: A practical guide to probabilistic modeling

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The Power of Statistics

 

What you'll learn

Explore and summarize a dataset 

Use probability distributions to model data

Conduct a hypothesis test to identify insights about data

Perform statistical analyses using Python 

Join Free: The Power of Statistics

There are 6 modules in this course

This is the fourth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll discover how data professionals use statistics to analyze data and gain important insights. You'll explore key concepts such as descriptive and inferential statistics, probability, sampling, confidence intervals, and hypothesis testing. You'll also learn how to use Python for statistical analysis and practice communicating your findings like a data professional. 

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:

-Describe the use of statistics in data science 

-Use descriptive statistics to summarize and explore data

-Calculate probability using basic rules

-Model data with probability distributions

-Describe the applications of different sampling methods 

-Calculate sampling distributions 

-Construct and interpret confidence intervals

-Conduct hypothesis tests

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Automate Cybersecurity Tasks with Python

 

What you'll learn

Explain how the Python programming language is used in cybersecurity

Create new, user-defined Python functions

Use regular expressions to extract information from text

Practice debugging code

Join Free: Automate Cybersecurity Tasks with Python

There are 4 modules in this course

This is the seventh course in the Google Cybersecurity Certificate. These courses will equip you with the skills you need to apply for an entry-level cybersecurity job. You’ll build on your understanding of the topics that were introduced in the sixth Google Cybersecurity Certificate course.

In this course, you will be introduced to the Python programming language and apply it in a cybersecurity setting to automate tasks. You'll start by focusing on foundational Python programming concepts, including data types, variables, conditional statements, and iterative statements. You'll also learn to work with Python effectively by developing functions, using libraries and modules, and making your code readable. In addition, you'll work with string and list data, and learn how to import, parse and debug files.  

Google employees who currently work in cybersecurity will guide you through videos, provide hands-on activities and examples that simulate common cybersecurity tasks, and help you build your skills to prepare for jobs. 

Learners who complete this certificate will be equipped to apply for entry-level cybersecurity roles. No previous experience is necessary.

By the end of this course, you will: 

- Explain how the Python programming language is used in cybersecurity.

- Write conditional and iterative statements in Python.

- Create new, user-defined Python functions.

- Use Python to work with strings and lists.

- Use regular expressions to extract information from text.

- Use Python to open and read the contents of a file.

- Identify best practices to improve code readability.

- Practice debugging code.

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Introduction to Git and GitHub

 

What you'll learn

Understand why version control is a fundamental tool for coding and collaboration

Install and run Git on your local machine 

Use and interact with GitHub 

Collaborate with others through remote repositories

Join Free: Introduction to Git and GitHub

There are 4 modules in this course

In this course, you’ll learn how to keep track of the different versions of your code and configuration files using a popular version control system (VCS) called Git. We'll also go through how to set up an account with a service called GitHub so that you can create your very own remote repositories to store your code and configuration.

Throughout this course, you'll learn about Git's core functionality so you can understand how and why it’s used in organizations. We’ll look into both basic and more advanced features, like branches and merging. We'll demonstrate how having a working knowledge of a VCS like Git can be a lifesaver in emergency situations or when debugging. And then we'll explore how to use a VCS to work with others through remote repositories, like the ones provided by GitHub. By the end of this course, you'll be able to store your code's history in Git and collaborate with others in GitHub, where you’ll also start creating your own portfolio! In order to follow along and complete the assessments, you’ll need a computer where you can install Git or ask your administrator to install it for you.

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The Nuts and Bolts of Machine Learning

 

What you'll learn

Identify characteristics of the different types of machine learning 

Prepare data for machine learning models 

Build and evaluate supervised and unsupervised learning models using Python

Demonstrate proper model and metric selection for a machine learning algorithm

Join Free: The Nuts and Bolts of Machine Learning

There are 5 modules in this course

This is the sixth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll learn about machine learning, which uses algorithms and statistics to teach computer systems to discover patterns in data. Data professionals use machine learning to help analyze large amounts of data, solve complex problems, and make accurate predictions. You’ll focus on the two main types of machine learning: supervised and unsupervised. You'll learn how to apply different machine learning models to business problems and become familiar with specific models such as Naive Bayes, decision tree, random forest, and more.  

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:

-Apply feature engineering techniques using Python

-Construct a Naive Bayes model

-Describe how unsupervised learning differs from supervised learning

-Code a K-means algorithm in Python 

-Evaluate and optimize the results of K-means model

-Explore decision tree models, how they work, and their advantages over other types of supervised machine learning

-Characterize bagging in machine learning, specifically for random forest models 

-Distinguish boosting in machine learning, specifically for XGBoost models 

-Explain tuning model parameters and how they affect performance and evaluation metrics

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Regression Analysis: Simplify Complex Data Relationships

 

What you'll learn

Investigate relationships in datasets

Identify regression model assumptions 

Perform linear and logistic regression using Python

Practice model evaluation and interpretation

Join Free: Regression Analysis: Simplify Complex Data Relationships

There are 6 modules in this course

This is the fifth of seven courses in the Google Advanced Data Analytics Certificate. Data professionals use regression analysis to discover the relationships between different variables in a dataset and identify key factors that affect business performance. In this course, you’ll practice modeling variable relationships. You'll learn about different methods of data modeling and how to use them to approach business problems. You’ll also explore methods such as linear regression, analysis of variance (ANOVA), and logistic regression.  

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:

-Explore the use of predictive models to describe variable relationships, with an emphasis on correlation

-Determine how multiple regression builds upon simple linear regression at every step of the modeling process

-Run and interpret one-way and two-way ANOVA tests

-Construct different types of logistic regressions including binomial, multinomial, ordinal, and Poisson log-linear regression models

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