Plots using Python

 

1. Line Plot:

#clcoding.com

import matplotlib.pyplot as plt

​

# Sample data

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

y = [2, 4, 6, 8, 10]

​

# Create a line plot

plt.plot(x, y)

plt.xlabel('X-axis')

plt.ylabel('Y-axis')

plt.title('Line Plot Example')

plt.show()

​

#clcoding.com

2. Bar Plot:

import matplotlib.pyplot as plt

​

# Sample data

categories = ['A', 'B', 'C', 'D']

values = [10, 20, 15, 25]

​

# Create a bar plot

plt.bar(categories, values)

plt.xlabel('Categories')

plt.ylabel('Values')

plt.title('Bar Plot Example')

plt.show()

​

3. Histogram:

import matplotlib.pyplot as plt

import numpy as np

​

# Generate random data

data = np.random.randn(1000)

​

# Create a histogram

plt.hist(data, bins=30)

plt.xlabel('Values')

plt.ylabel('Frequency')

plt.title('Histogram Example')

plt.show()

​

4. Scatter Plot:

import matplotlib.pyplot as plt

import numpy as np

​

# Generate random data

x = np.random.randn(100)

y = 2 * x + np.random.randn(100)

​

# Create a scatter plot

plt.scatter(x, y)

plt.xlabel('X-axis')

plt.ylabel('Y-axis')

plt.title('Scatter Plot Example')

plt.show()

​

5. Box Plot:

import seaborn as sns

import numpy as np

​

# Generate random data

data = np.random.randn(100)

​

# Create a box plot

sns.boxplot(data=data)

plt.title('Box Plot Example')

plt.show()

​

6. Violin Plot:

import seaborn as sns

import numpy as np

​

# Generate random data

data = np.random.randn(100)

​

# Create a violin plot

sns.violinplot(data=data)

plt.title('Violin Plot Example')

plt.show()

​

7. Heatmap:

#clcoding.com

import seaborn as sns

import numpy as np

​

# Generate random data

data = np.random.rand(10, 10)

#clcoding.com

# Create a heatmap

sns.heatmap(data)

plt.title('Heatmap Example')

plt.show()

​

8. Area Plot:

import matplotlib.pyplot as plt

​

# Sample data #clcoding.com

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

y1 = [2, 4, 6, 8, 10]

y2 = [1, 3, 5, 7, 9]

​

# Create an area plot

plt.fill_between(x, y1, color="skyblue", alpha=0.4)

plt.fill_between(x, y2, color="salmon", alpha=0.4)

plt.xlabel('X-axis')

plt.ylabel('Y-axis')

plt.title('Area Plot Example')

plt.show()

9. Pie Chart:

import matplotlib.pyplot as plt

​

# Sample data

sizes = [30, 20, 25, 15, 10]

labels = ['A', 'B', 'C', 'D', 'E']

​

# Create a pie chart

plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)

plt.title('Pie Chart Example')

plt.show()

​

10. Polar Plot:

g

import matplotlib.pyplot as plt

import numpy as np

​

# Sample data

theta = np.linspace(0, 2*np.pi, 100)

r = np.sin(3*theta)

​

# Create a polar plot #clcoding.com

plt.polar(theta, r)

plt.title('Polar Plot Example')

plt.show()

​

11. 3D Plot:

import matplotlib.pyplot as plt

import numpy as np

​

# Sample data

x = np.linspace(-5, 5, 100)

y = np.linspace(-5, 5, 100)

X, Y = np.meshgrid(x, y)

Z = np.sin(np.sqrt(X**2 + Y**2))

​

# Create a 3D surface plot

fig = plt.figure()

ax = fig.add_subplot(111, projection='3d')

ax.plot_surface(X, Y, Z)

ax.set_title('3D Plot Example')

plt.show()

​

12. Violin Swarm Plot:

#clcoding.com

import seaborn as sns

import numpy as np

​

# Generate random data

data = np.random.randn(100)

#clcoding.com

# Create a violin swarm plot

sns.violinplot(data=data, inner=None, color='lightgray')

sns.swarmplot(data=data, color='blue', alpha=0.5)

plt.title('Violin Swarm Plot Example')

plt.show()

​

13. Pair Plot:

import seaborn as sns

import pandas as pd

​

# Load sample dataset

iris = sns.load_dataset('iris')

​

# Create a pair plot

sns.pairplot(iris)

plt.title('Pair Plot Example')

plt.show()

​

​

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Cybersecurity using Python

 

1. Hashing Passwords:

import hashlib

​

def hash_password(password):

    hashed_password = hashlib.sha256(password.encode()).hexdigest()

    return hashed_password

​

# Example

password = "my_secure_password"

hashed_password = hash_password(password)

print("Hashed Password:", hashed_password)

​

#clcoding.com 

Hashed Password: 2c9a8d02fc17ae77e926d38fe83c3529d6638d1d636379503f0c6400e063445f

2. Generating Random Passwords:

import random

import string

​

def generate_random_password(length=12):

    characters = string.ascii_letters + string.digits + string.punctuation

    password = ''.join(random.choice(characters) for _ in range(length))

    return password

​

# Example

random_password = generate_random_password()

print("Random Password:", random_password)

​

#clcoding.com 

Random Password: zH7~ANoO:7#S

3. Network Scanning with Scapy:

from scapy.all import IP, ICMP, sr1

​

def ping(host):

    packet = IP(dst=host)/ICMP()

    response = sr1(packet, timeout=2, verbose=0)

    if response:

        return f"{host} is online"

    else:

        return f"{host} is offline"

​

# Example

host_to_scan = "example.com"

result = ping(host_to_scan)

print(result)

​

#clcoding.com

4. Web Scraping for Security Research:

import requests

from bs4 import BeautifulSoup

​

def scrape_security_news():

    url = "https://example-security-news.com"

    response = requests.get(url)

    soup = BeautifulSoup(response.text, 'html.parser')

    headlines = soup.find_all('h2', class_='security-headline')

    return [headline.text for headline in headlines]

​

# Example

security_headlines = scrape_security_news()

print("Security Headlines:", security_headlines)

​

#clcoding.com

5. Password Cracking Simulation:

import hashlib

​

def simulate_password_cracking(hashed_password, password_list):

    for password in password_list:

        if hashlib.sha256(password.encode()).hexdigest() == hashed_password:

            return f"Password cracked: {password}"

    return "Password not found"

​

# Example

hashed_password_to_crack = "d033e22ae348aeb5660fc2140aec35850c4da997"

common_passwords = ["password", "123456", "qwerty", "admin"]

​

result = simulate_password_cracking(hashed_password_to_crack, common_passwords)

print(result)

​

#clcoding.com

6. Secure File Handling:

import os

def secure_file_deletion(file_path):

    with open(file_path, 'w') as file:

        file.write(os.urandom(1024))  

        # Overwrite the file with random data

    os.remove(file_path)

    print(f"{file_path} securely deleted")

# Example

file_path_to_delete = "example.txt"

secure_file_deletion(file_path_to_delete)

#clcoding.com

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Interpretable Machine Learning with Python - Second Edition: Build explainable, fair, and robust high-performance models with hands-on, real-world examples

 

A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models.

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

Key Features

Interpret real-world data, including cardiovascular disease data and the COMPAS recidivism scores

Build your interpretability toolkit with global, local, model-agnostic, and model-specific methods

Analyze and extract insights from complex models from CNNs to BERT to time series models

Book Description

Interpretable Machine Learning with Python, Second Edition, brings to light the key concepts of interpreting machine learning models by analyzing real-world data, providing you with a wide range of skills and tools to decipher the results of even the most complex models.

Build your interpretability toolkit with several use cases, from flight delay prediction to waste classification to COMPAS risk assessment scores. This book is full of useful techniques, introducing them to the right use case. Learn traditional methods, such as feature importance and partial dependence plots to integrated gradients for NLP interpretations and gradient-based attribution methods, such as saliency maps.

In addition to the step-by-step code, you'll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability.

By the end of the book, you'll be confident in tackling interpretability challenges with black-box models using tabular, language, image, and time series data.

What you will learn

Progress from basic to advanced techniques, such as causal inference and quantifying uncertainty

Build your skillset from analyzing linear and logistic models to complex ones, such as CatBoost, CNNs, and NLP transformers

Use monotonic and interaction constraints to make fairer and safer models

Understand how to mitigate the influence of bias in datasets

Leverage sensitivity analysis factor prioritization and factor fixing for any model

Discover how to make models more reliable with adversarial robustness

Who this book is for

This book is for data scientists, machine learning developers, machine learning engineers, MLOps engineers, and data stewards who have an increasingly critical responsibility to explain how the artificial intelligence systems they develop work, their impact on decision making, and how they identify and manage bias. It's also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a good grasp of the Python programming language is needed to implement the examples.

Table of Contents

Interpretation, Interpretability and Explainability; and why does it all matter?

Key Concepts of Interpretability

Interpretation Challenges

Global Model-agnostic Interpretation Methods

Local Model-agnostic Interpretation Methods

Anchors and Counterfactual Explanations

Visualizing Convolutional Neural Networks

Interpreting NLP Transformers

Interpretation Methods for Multivariate Forecasting and Sensitivity Analysis

Feature Selection and Engineering for Interpretability

Bias Mitigation and Causal Inference Methods

Monotonic Constraints and Model Tuning for Interpretability

Adversarial Robustness

What's Next for Machine Learning Interpretability?

Hard Copy: Interpretable Machine Learning with Python - Second Edition: Build explainable, fair, and robust high-performance models with hands-on, real-world examples

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Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT and other LLMs

 

Get to grips with the LangChain framework from theory to deployment and develop production-ready applications.

Code examples regularly updated on GitHub to keep you abreast of the latest LangChain developments.

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

Key Features

Learn how to leverage LLMs' capabilities and work around their inherent weaknesses

Delve into the realm of LLMs with LangChain and go on an in-depth exploration of their fundamentals, ethical dimensions, and application challenges

Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality

Book Description

ChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Bard. It also demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis - illustrating the expansive utility of LLMs in real-world applications.

Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.

What you will learn

Understand LLMs, their strengths and limitations

Grasp generative AI fundamentals and industry trends

Create LLM apps with LangChain like question-answering systems and chatbots

Understand transformer models and attention mechanisms

Automate data analysis and visualization using pandas and Python

Grasp prompt engineering to improve performance

Fine-tune LLMs and get to know the tools to unleash their power

Deploy LLMs as a service with LangChain and apply evaluation strategies

Privately interact with documents using open-source LLMs to prevent data leaks

Who this book is for

The book is for developers, researchers, and anyone interested in learning more about LLMs. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs and are looking to stay ahead of the curve in the LLMs and LangChain arena.

Basic knowledge of Python is a prerequisite, while some prior exposure to machine learning will help you follow along more easily.

Table of Contents

What Is Generative AI?

LangChain for LLM Apps

Getting Started with LangChain

Building Capable Assistants

Building a Chatbot like ChatGPT

Developing Software with Generative AI

LLMs for Data Science

Customizing LLMs and Their Output

Generative AI in Production

The Future of Generative Models

Hard Copy: Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT and other LLMs

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Data Science Fundamentals with Python and SQL Specialization

 

What you'll learn

Working knowledge of Data Science Tools such as Jupyter Notebooks, R Studio, GitHub, Watson Studio

Python programming basics including data structures, logic, working with files, invoking APIs, and libraries such as Pandas and Numpy

Statistical Analysis techniques including  Descriptive Statistics, Data Visualization, Probability Distribution, Hypothesis Testing and Regression

Relational Database fundamentals including SQL query language, Select statements, sorting & filtering, database functions, accessing multiple tables

Join Free: Data Science Fundamentals with Python and SQL Specialization

Specialization - 5 course series

Data Science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Specialization from IBM will help anyone interested in pursuing a career in data science by teaching them fundamental skills to get started in this in-demand field.

The specialization consists of 5 self-paced online courses that will provide you with the foundational skills required for Data Science, including open source tools and libraries, Python, Statistical Analysis, SQL, and relational databases. You’ll learn these data science pre-requisites through hands-on practice using real data science tools and real-world data sets.

Upon successfully completing these courses, you will have the practical knowledge and experience to delve deeper in Data Science and work on more advanced Data Science projects. 

No prior knowledge of computer science or programming languages required. 

This program is ACE® recommended—when you complete, you can earn up to 8 college credits.  

Applied Learning Project

All courses in the specialization contain multiple hands-on labs and assignments to help you gain practical experience and skills with a variety of data sets. Build your data science portfolio from the artifacts you produce throughout this program. Course-culminating projects include:

Extracting and graphing financial data with the Pandas data analysis Python library

Generating visualizations and conducting statistical tests to provide insight on housing trends using census data

Using SQL to query census, crime, and demographic data sets to identify causes that impact enrollment, safety, health, and environment ratings in schools

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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

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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)

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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

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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

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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

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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)

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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.  

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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

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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.

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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.

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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.

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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.

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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.

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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

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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)

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