Google Cybersecurity Professional Certificate

 

What you'll learn

Understand the importance of cybersecurity practices and their impact for organizations.

Identify common risks, threats, and vulnerabilities, as well as techniques to mitigate them.

Protect networks, devices, people, and data from unauthorized access and cyberattacks using Security Information and Event Management (SIEM) tools.

Gain hands-on experience with Python, Linux, and SQL.

Prepare for a career in cybersecurity

Receive professional-level training from Google

Demonstrate your proficiency in portfolio-ready projects

Earn an employer-recognized certificate from Google

Qualify for in-demand job titles: cybersecurity analyst, security analyst, security operations center (SOC) analyst

Professional Certificate - 8 course series

Prepare for a new career in the high-growth field of cybersecurity, no degree or experience required. Get professional training designed and delivered by subject matter experts at Google and have the opportunity to connect with top employers.

Organizations must continuously protect themselves and the people they serve from cyber-related threats, like fraud and phishing. They rely on cybersecurity to maintain the confidentiality, integrity, and availability of their internal systems and information. Cybersecurity analysts use a collection of methods and technologies to safeguard against threats and unauthorized access — and to create and implement solutions should a threat get through.

During the 8 courses in this certificate program, you’ll learn from cybersecurity experts at Google and gain in-demand skills that prepare you for entry-level roles like cybersecurity analyst, security operations center (SOC) analyst, and more. At under 10 hours per week, you can complete the certificate in less than 6 months. 

Upon completion of the certificate, you can directly apply for jobs with Google and over 150 U.S. employers, including American Express, Deloitte, Colgate-Palmolive, Mandiant (now part of Google Cloud), T-Mobile, and Walmart.

The Google Cybersecurity Certificate helps prepare you for the CompTIA Security+ exam, the industry leading certification for cybersecurity roles. You’ll earn a dual credential when you complete both.

Applied Learning Project

This program includes 170 hours of instruction and hundreds of practice-based assessments and activities that simulate real-world cybersecurity scenarios that are critical for success in the workplace. Through a mix of videos, assessments, and hands-on labs, you’ll become familiar with the cybersecurity tools, platforms, and skills required for an entry-level job.

Skills you’ll gain will include: Python, Linux, SQL, Security Information and Event Management (SIEM) tools, Intrusion Detection Systems (IDS), communication, collaboration, analysis, problem solving and more! 

Additionally, each course includes portfolio activities through which you’ll showcase examples of cybersecurity skills that you can share with potential employers. Acquire concrete skills that top employers are hiring for right now.

Join Free - Google Cybersecurity Professional Certificate

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Free Courses from Cisco

 

Free Courses from Cisco

1. Cybersecurity Operations Fundamentals Specialization

2. Introduction to Network Automation

3. Security Operations Center (SOC)

4. Network Automation Engineering Fundamentals Specialization

5. Network Security

6. DevOps for Network Automation (NetDevOps)

7. Ansible for Network Automation

8. Using APIs for Network Automation

9. Data Security

10. Introducing Model-Driven Programmability

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Machine Learning with Apache Spark (Free Course)

 

What you'll learn

Describe ML, explain its role in data engineering, summarize generative AI, discuss Spark's uses, and analyze ML pipelines and model persistence. 

Evaluate ML models, distinguish between regression, classification, and clustering models, and compare data engineering pipelines with ML pipelines. 

Construct the data analysis processes using Spark SQL, and perform regression, classification, and clustering using SparkML. 

Demonstrate connecting to Spark clusters, build ML pipelines, perform feature extraction and transformation, and model persistence. 

There are 4 modules in this course

Explore the exciting world of machine learning with this IBM course. 

Start by learning ML fundamentals before unlocking the power of Apache Spark to build and deploy ML models for data engineering applications. Dive into supervised and unsupervised learning techniques and discover the revolutionary possibilities of Generative AI through instructional readings and videos.

Gain hands-on experience with Spark structured streaming, develop an understanding of data engineering and ML pipelines, and become proficient in evaluating ML models using SparkML.  

In practical labs, you'll utilize SparkML for regression, classification, and clustering, enabling you to construct prediction and classification models. Connect to Spark clusters, analyze SparkSQL datasets, perform ETL activities, and create ML models using Spark ML and sci-kit learn. Finally, demonstrate your acquired skills through a final assignment. 

This intermediate course is suitable for aspiring and experienced data engineers, as well as working professionals in data analysis and machine learning. Prior knowledge in Big Data, Hadoop, Spark, Python, and ETL is highly recommended for this course.

Free Course - Machine Learning with Apache Spark

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Data Science Coding Challenge: Loan Default Prediction (Free Project)

 

Objectives

Load, clean, analyze, process, and visualize data using Python and Jupyter Notebooks

Produce an end-to-end machine learning prediction model using Python and Jupyter Notebooks

Skills you’ll demonstrate

Data Science

Data Analysis

Python Programming

Machine Learning

About this Project

In this coding challenge, you'll compete with other learners to achieve the highest prediction accuracy on a machine learning problem. You'll use Python and a Jupyter Notebook to work with a real-world dataset and build a prediction or classification model.

Important Information:

How to register?

To participate, you’ll need to complete simple steps. First, click the “Start Project” button to register.

Next, you’ll need to create a Coursera Skills Profile, which only takes a few minutes. We’ll send you a profile link the week of the challenge.

When does the challenge start?

The coding challenge begins Tuesday, August 29th, at 8 AM (PST) and closes Thursday, August 31st, at 11:59 PM (PST). If you’re registered, you’ll receive a reminder email on the challenge start date. 

Please note this is a timed competition. Once the challenge is unlocked, you’ll have 72 hours to complete it. You can submit as many times as you would like within this timeframe.

What will the winners receive?

Participants will be evaluated based on their model’s prediction accuracy. The top 20% of participants will receive an achievement badge on their Coursera Skills Profile, highlighting their performance to recruiters.  The top 100 performers will get complimentary access to select Data Science courses.

All participants can showcase their projects to potential employers on their Coursera Skills Profile.

Winners will be notified by email the week of September 10th.

Good luck, and have fun!

Project plan

This project requires you to independently complete the following steps:

•Importing and preprocessing data

•Analyze the data

•Build machine learning models

•Evaluate machine learning models

Join Free -  Data Science Coding Challenge: Loan Default Prediction

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Machine Learning with Python

 

What you'll learn

Describe the various types of Machine Learning algorithms and when to use them  

Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression  

Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees 

Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics  

 There are 6 modules in this course

Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.  

This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more.  

You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN. 

With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms.  

By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency.

Join free - Machine Learning with Python

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Python Coding challenge - Day 70 | What is the output of the following Python code?

 

a == c: This checks if the values of a and c are equal. The values of both lists are [1, 2, 3], so a == c is True.

a is c: This checks if a and c refer to the exact same object. As mentioned before, although the values of the lists are the same, a and c are two different list objects. Therefore, a is c is False.

So, the output of this code will be: True, False

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result = max(-0.0, 0.0) print(result)

result = max(-0.0, 0.0) 
print(result)

The correct explanation is that in Python, -0.0 and 0.0 are considered equal, and the max() function does not distinguish between them based on sign. When you use max(-0.0, 0.0), the result will be the number with the higher magnitude, regardless of its sign. In this case, both -0.0 and 0.0 have the same magnitude, so the result will be the one that appears first in the arguments, which is -0.0:

result = max(-0.0, 0.0)

print(result)

Output:

-0.0

So, max(-0.0, 0.0) returns -0.0 due to the way Python handles the comparison of floating-point numbers.

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round(3 / 2) round(5 / 2)

 The round() function is used to round a number to the nearest integer. Let's calculate the results:

round(3 / 2) is equal to round(1.5), and when rounded to the nearest integer, it becomes 2.

round(5 / 2) is equal to round(2.5), and when rounded to the nearest integer, it becomes 2.

So, the results are:

round(3 / 2) equals 2.

round(5 / 2) equals 2.

Why does round(5 / 2) return 2 instead of 3? The issue here is that Python’s round method implements banker’s rounding, where all half values will be rounded to the closest even number. 

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The most difficult Python questions:

• What is the Global Interpreter Lock (GIL)? Why is it important?

• Define self in Python?

• What is pickling and unpickling in Python?

• How do you reverse a list in Python?

• What does break and continue do in Python?

• Can break and continue be used together?

• Explain generators vs iterators.

• How do you access a module written in Python from C?

• What is the difference between a List and a Tuple?

• What is __init__() in Python?

• What is the difference between a mutable data type and an immutable data type?

• Explain List, Dictionary, and Tuple comprehension with an example.

• What is monkey patching in Python?

• What is the Python “with” statement designed for?

• Why use else in try/except construct in Python?

• What are the advantages of NumPy over regular Python lists?

• What is the difference between merge, join and concatenate?

• How do you identify and deal with missing values?

• Which all Python libraries have you used for visualization?

• What is the most difficult Python question you have ever encountered?

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Python: Lists vs. Tuples vs. Sets vs. Dictionaries

 lists, tuples, sets, and dictionaries in Python based on various characteristics:

Mutability:

Lists: Mutable. You can modify, add, or remove elements after creation.

Tuples: Immutable. Once created, elements cannot be changed.

Sets: Mutable. You can add or remove elements, but each element must be unique.

Dictionaries: Mutable. You can add, modify, or remove key-value pairs.

Ordering:

Lists: Ordered. Elements are stored in a specific order and can be accessed by index.

Tuples: Ordered. Similar to lists, elements have a specific order.

Sets: Unordered. Elements have no specific order, and you cannot access them by index.

Dictionaries: Prior to Python 3.7, dictionaries were unordered. From Python 3.7 onwards, dictionaries maintain insertion order.

Duplicates:

Lists: Can contain duplicate elements.

Tuples: Can contain duplicate elements.

Sets: Cannot contain duplicate elements.

Dictionaries: Keys must be unique.

Syntax:

Lists: Defined using square brackets [].

Tuples: Defined using parentheses ().

Sets: Defined using curly braces {}.

Dictionaries: Defined using curly braces {} with key-value pairs separated by colons.

Use Cases:

Lists: Use when you need a mutable, ordered collection with the possibility of duplicate elements.

Tuples: Use when you need an immutable, ordered collection. Suitable for situations where data should not be changed.

Sets: Use when you need an unordered collection of unique elements and order doesn't matter.

Dictionaries: Use when you need a mutable collection of key-value pairs, providing fast lookup based on keys.

Example:

my_list = [1, 2, 3]

my_tuple = (4, 5, 6)

my_set = {7, 8, 9}

my_dict = {'a': 10, 'b': 11, 'c': 12}

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IBM Full Stack Software Developer Professional Certificate

 

Prepare for a career as a full stack developer. Gain the in-demand skills and hands-on experience to get job-ready in less than 4 months. No prior experience required.

What you'll learn

Master the most up-to-date practical skills and tools that full stack developers use in their daily roles

Learn how to deploy and scale applications using Cloud Native methodologies and tools such as Containers, Kubernetes, Microservices, and Serverless

Develop software with front-end development languages and tools such as HTML, CSS, JavaScript, React, and Bootstrap

Build your GitHub portfolio by applying your skills to multiple labs and hands-on projects, including a capstone

Professional Certificate - 12 course series

Prepare for a career in the high-growth field of software development. In this program, you’ll learn in-demand skills and tools used by professionals for front-end, back-end, and cloud native application development to get job-ready in less than 4 months, with no prior experience needed. 

Full stack refers to the end-to-end computer system application, including the front end and back end coding. This Professional Certificate covers development for both of these scenarios. Cloud native development refers to developing a program designed to work on cloud architecture. The flexibility and adaptability that full stack and cloud native developers provide make them highly sought after in this digital world. 

You’ll  learn how to build, deploy, test, run, and manage full stack cloud native applications. Technologies covered includes Cloud foundations, GitHub, Node.js, React, CI/CD, Containers, Docker, Kubernetes, OpenShift, Istio, Databases, NoSQL, Django ORM, Bootstrap, Application Security, Microservices, Serverless computing, and more. 

After completing the program you will have developed several applications using front-end and back-end technologies and deployed them on a cloud platform using Cloud Native methodologies. You will publish these projects through your GitHub repository to share your portfolio with your peers and prospective employers.

This program is ACE® recommended—when you complete, you can earn up to 18 college credits.

Applied Learning Project

Throughout the courses in the Professional Certificate, you will develop a portfolio of hands-on projects involving various popular technologies and programming languages in Full Stack Cloud Application Development. These projects include creating:

HTML pages on Cloud Object Storage

An interest rate calculator using HTML, CSS, and JavaScript

An AI program deployed on Cloud Foundry using DevOps principles and CI/CD toolchains with a NoSQL database

A Node.js back-end application and a React front-end application

A containerized guestbook app packaged with Docker deployed with Kubernetes and managed with OpenShift

A Python app bundled as a package

A database-powered application using Django ORM and Bootstrap

An app built using Microservices & Serverless

A scalable, Cloud Native Full Stack application using the technologies learned in previous courses

You will publish these projects through your GitHub repository to share your skills with your peers and prospective employers.

Join - IBM Full Stack Software Developer Professional Certificate

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Object-Oriented Python: Inheritance and Encapsulation

 

What you'll learn

How to architect larger programs using object-oriented principles

Re-use parts of classes using inheritance

Encapsulate relevant information and methods in a class

There are 4 modules in this course

Code and run your first python program in minutes without installing anything!

This course is designed for learners with limited coding experience, providing a solid foundation of not just python, but core Computer Science topics that can be transferred to other languages. The modules in this course cover inheritance, encapsulation, polymorphism, and other object-related topics. Completion of the prior 3 courses in this specialization is recommended.

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 small, approachable coding exercises that take minutes instead of hours.

Join free - Object-Oriented Python: Inheritance and Encapsulation

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Python Coding challenge - Day 69 | What is the output of the following Python code?

 

The above code of the list my_list using slicing and assigns a new set of values [7, 8, 9] to the sliced portion. Here's a step-by-step breakdown:

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

This initializes a list with the values [1, 2, 3, 4, 5].

my_list[1:3] = [7, 8, 9]

This slices the elements at index 1 and 2 (exclusive) of my_list and replaces them with the values [7, 8, 9]. After this line executes, my_list becomes [1, 7, 8, 9, 4, 5].

print(my_list)

This prints the modified list:

[1, 7, 8, 9, 4, 5]

So, the final output is [1, 7, 8, 9, 4, 5].

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Python Coding challenge - Day 68 | What is the output of the following Python code?

 

The expression 3 * 2 ** 3 involves exponentiation and multiplication. The exponentiation operator ** has higher precedence than multiplication.

Let's break it down step by step:

3. 2∗∗32∗∗3 is 2×2×22×2×2, which equals 88.

4. 3×83×8 is 2424.

So, the output of the code: print(3 * 2 ** 3)

24

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Do you know the reason ?

 

The all function returns True if all elements of the iterable are true (or if the iterable is empty). If any element is false, it returns False.

In the case of an empty iterable, such as an empty list ([]), the all function returns True because there are no elements that are false.

So, when you execute print(all([])), it will output: True

The any function returns True if at least one element of the iterable is true. If the iterable is empty, it returns False because there are no elements to evaluate.

In the case of an empty iterable, such as an empty list ([]), the any function returns False.

So, when you execute print(any([])), it will output: False

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A simple Python code for checking whether a given number is prime or not

 

def is_prime(number):

    # Check if the number is less than 2

    if number < 2:

        return False

    

    # Check for factors from 2 to the square root of the number

    for i in range(2, int(number**0.5) + 1):

        if number % i == 0:

            # If a factor is found, the number is not prime

            return False

    

    # If no factors are found, the number is prime

    return True

# Example usage

num = int(input("Enter a number: "))

if is_prime(num):

    print(f"{num} is a prime number.")

else:

    print(f"{num} is not a prime number.")

Explanation:

The is_prime function takes an integer number as an argument and returns True if the number is prime, and False otherwise.

The function first checks if the number is less than 2. If it is, the number is not prime, as prime numbers are defined as greater than 1.

It then uses a for loop to iterate over the range from 2 to the square root of the number (rounded up to the nearest integer). This is an optimization, as factors of a number larger than its square root must pair with factors smaller than its square root.

Inside the loop, it checks if the number is divisible evenly by the current value of i. If it is, then the number is not prime, and the function returns False.

If no factors are found in the loop, the function returns True, indicating that the number is prime.

Finally, an example usage of the function is shown, where the user is prompted to enter a number, and the program prints whether it is prime or not based on the result of the is_prime function.

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Count number of files and directories

 

import os

# Path IN which we have to count files and directories

PATH = 'E:\elements'   # Give your path here

fileCount = 0

dirCount = 0

for root, dirs, files in os.walk(PATH):

    print('Looking in:',root)

    for directories in dirs:

        dirCount += 1

    for Files in files:

        fileCount += 1

#clcoding.com

print('Number of files',fileCount)

print('Number of Directories',dirCount)

print('Total:',(dirCount + fileCount))

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Introduction to Python

 

What you'll learn

Uses of Python

Python variables and input

Python Decisions and Looping

About this Guided Project

Learning Python gives the programmer a wide variety of career paths to choose from. Python is an open-source (free) programming language that is used in web programming, data science, artificial intelligence, and many scientific applications. Learning Python allows the programmer to focus on solving problems, rather than focusing on syntax. Its relative size and simplified syntax give it an edge over languages like Java and C++, yet the abundance of libraries gives it the power needed to accomplish great things.

In this tutorial you will create a guessing game application that pits the computer against the user. You will create variables, decision constructs, and loops in python to create the game.

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

• Task 1: How Python is Used

• Task 2: Python Input and variables

• Task3: Python Decisions

• Challenge Task: Python Input and Decisions

• Challenge Solution: Python Input and Decisions

• Task 4: Python Loops

• Task 5: Python Functions

• Challenge Task: Python While Loops and Functions

• Challenge Solution: Python While Loops and Functions

Join - Introduction to Python

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Understanding Basic SQL Syntax

 

What you'll learn

Identify and use correct syntax when writing SQL retrieval queries.

Learn, practice, and apply job-ready skills in less than 2 hours

Receive training from industry experts

Gain hands-on experience solving real-world job tasks

Build confidence using the latest tools and technologies

About this Guided Project

In this project you will learn to identify and use correct syntax when writing SQL retrieval queries. Through hands-on activities in SQLiteStudio, you will gain experience with the SQL syntax used to display specific columns, filter for specific rows, and determine the sequence of those columns and rows in query output. Familiarity with SQL syntax is a marketable skill for both Information Technology professionals and non-IT super users.

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

•Describe basic SQL syntax rules along with requirements for the names of tables, and the naming and data type requirements for table columns (fields).

•Use the SELECT command and FROM clause to control the columns that are displayed and the order in which they display in query output.

•Limit the rows retrieved by an SQL query by applying the WHERE clause with one or more conditions.

•Use the ORDER BY clause to modify the sort order of the rows returned from an SQL retrieval query.

•Code an SQL retrieval query that uses the JOIN command to retrieve data rows from tables that are related via a common column.

Join - Understanding Basic SQL Syntax

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Python Coding challenge - Day 67 | What is the output of the following Python code?

 

In Python, the True Boolean value is equivalent to the integer 1, and False is equivalent to 0. Therefore, when you multiply True by any number, it's like multiplying 1 by that number.

In this code: print(True * 10)

It will output 10 because True is equivalent to 1, and 1 * 10 equals 10.

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Successful Algorithmic Trading halls-moore (PDF)

 

Introduction to the Book

1.1 Introduction to QuantStart

QuantStart was founded by Michael Halls-Moore, in 2010, to help junior quantitative analysts

(QAs) find jobs in the tough economic climate. Since then the site has evolved to become a

substantial resource for quantitative finance. The site now concentrates on algorithmic trading,

but also discusses quantitative development, in both Python and C++.

Since March 2010, QuantStart has helped over 200,000 visitors improve their quantitative

finance skills. You can always contact QuantStart by sending an email to mike@quantstart.com.

1.2 What is this Book?

Successful Algorithmic Trading has been written to teach retail discretionary traders and trading

professionals, with basic programming skills, how to create fully automated profitable and robust

algorithmic trading systems using the Python programming language. The book describes the

nature of an algorithmic trading system, how to obtain and organise financial data, the concept of backtesting and how to implement an execution system. The book is designed to be

extremely practical, with liberal examples of Python code throughout the book to demonstrate

the principles and practice of algorithmic trading.

1.3 Who is this Book For?

This book has been written for both retail traders and professional quants who have some basic

exposure to programming and wish to learn how to apply modern languages and libraries to

algorithmic trading. It is designed for those who enjoy self-study and can learn by example. The

book is aimed at individuals interested in actual programming and implementation, as I believe

that real success in algorithmic trading comes from fully understanding the implementation

details.

Professional quantitative traders will also find the content useful. Exposure to new libraries

and implementation methods may lead to more optimal execution or more accurate backtesting.

1.4 What are the Prerequisites?

The book is relatively self-contained, but does assume a familiarity with the basics of trading in

a discretionary setting. The book does not require an extensive programming background, but

basic familiarity with a programming language is assumed. You should be aware of elementary

programming concepts such as variable declaration, flow-control (if-else) and looping (for/while).

Some of the trading strategies make use of statistical machine learning techniques. In addition, the portfolio/strategy optimisation sections make extensive use of search and optimization.

PDF Link - successful algorithmic trading halls-moore

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Understanding Deep Learning (PDF Book)

 

Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.

Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion models

Short, focused chapters progress in complexity, easing students into difficult concepts

Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models

Streamlined presentation separates critical ideas from background context and extraneous detail

Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible

Programming exercises offered in accompanying Python Notebooks

Buy - Understanding Deep Learning

PDF Link - Understanding Deep Learning (PDF )

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Understanding Machine Learning: From Theory to Algorithms (PDF Book)

 

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

Buy - Understanding Machine Learning: From Theory to Algorithms 

PDF Link - Understanding Machine Learning: From Theory to Algorithms

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Happy Diwali using Python Turtle

 

import turtle

s = turtle.Screen()

t = turtle.Turtle()

def move_to(x,y):

    t.penup()

    t.goto(x,y)

    t.pendown()

def draw_rectangle(a,b):

    t.begin_fill()

    t.forward(a)

    t.left(90)

    t.forward(b)

    t.left(90)

    t.forward(a)

    t.left(90)

    t.forward(b)

    t.end_fill()

    t.speed(10)

t.color("red")

move_to(-500,200)

draw_rectangle(10,100)

move_to(-490,250)

t.left(90)

draw_rectangle(80,10)

move_to(-410,200)

t.left(90)

draw_rectangle(10,100)

move_to(-380,200)

t.left(60)

t.color("yellow")

draw_rectangle(10,122)

move_to(-275,198)

t.left(145)

draw_rectangle(10,110)

move_to(-350,230)

t.left(335)

draw_rectangle(10,63)

move_to(-240,198)

t.left(270)

t.color("green")

draw_rectangle(100,10)

move_to(-240,288)

draw_rectangle(50,10)

move_to(-190,288)

draw_rectangle(40,10)

move_to(-190,248)

draw_rectangle(50,10)

move_to(-160,198)

t.color("violet")

draw_rectangle(100,10)

move_to(-160,288)

draw_rectangle(50,10)

move_to(-110,288)

draw_rectangle(40,10)

move_to(-110,248)

draw_rectangle(50,10)

move_to(-80,198)

t.left(320)

t.color("orange")

draw_rectangle(120,10)

move_to(-100,295)

draw_rectangle(68,10)

move_to(-500,80)

t.left(40)

t.color("blue")

draw_rectangle(100,10)

t.left(180)

move_to(-490,70)

draw_rectangle(50,10)

t.left(90)

t.fd(50)

t.right(90)

draw_rectangle(80,10)

t.left(90)

t.fd(80)

t.left(270)

draw_rectangle(50,10)

move_to(-400,80)

t.left(180)

t.color("pink")

draw_rectangle(100,10)

move_to(-220,70)

t.left(60)

t.color("brown")

draw_rectangle(100,10)

t.left(300)

move_to(-370,70)

t.right(152)

draw_rectangle(100,10)

t.left(152)

move_to(-290,10)

t.right(45)

draw_rectangle(40,10)

t.right(3)

draw_rectangle(40,10)

move_to(-200,-15)

t.left(200)

t.color("darkgreen")

draw_rectangle(10,110)

move_to(-95,-20)

t.left(145)

draw_rectangle(10,114)

move_to(-175,25)

t.left(333)

draw_rectangle(10,63)

move_to(-70,79)

t.left(90)

t.color("skyblue")

draw_rectangle(101,10)

move_to(-60,-22)

t.left(180)

draw_rectangle(80,10)

move_to(50,-22)

t.left(180)

t.color("black")

draw_rectangle(100,10)

move_to(300,150)

t.color("red")

angle=0

for i in range(20):

    t.fd(50)

    move_to(300,150)

    angle+=18

    t.left(angle)

move_to(450,150)

t.color("blue")

angle=0

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(450,150)

move_to(375,300)

t.color("green")

angle=0

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(375,300)

move_to(375,-300)

t.color("black")

angle=0

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(375,-300)

move_to(150,-150)

t.color("violet")

angle=0

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(150,-150)

move_to(450,-150)

t.color("brown")

angle=0

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(450,-150)

move_to(-200,-300)

t.color("green")

angle=0

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(-200,-300)

move_to(125,0)

t.color("yellow")

angle=0

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(125,0)

t.color("pink")

angle=0

move_to(-100,-200)

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(-100,-200)

t.color("lightgreen")

angle=0

move_to(300,0)

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(300,0)

t.color("skyblue")

angle=0

move_to(-500,-240)

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(-500,-240)

t.color("orange")

angle=0

move_to(-350,-170)

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(-350,-170)

t.color("black")

angle=0

move_to(500,20)

for i in range(20):

    t.fd(50)

    angle+=18

    t.left(angle)

    move_to(500,20)

    #clcoding.com

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Mastering Data Analysis with Pandas

 

What you'll learn

Master data analysis and manipulation in Pandas and Python

Define and manipulate Pandas Series

Master Pandas Attributes, methods and math operations

Project Link - Mastering Data Analysis with Pandas

About this Guided Project

In this structured series of hands-on guided projects, we will master the fundamentals of data analysis and manipulation with Pandas and Python. Pandas is a super powerful, fast, flexible and easy to use open-source data analysis and manipulation tool. This guided project is the first of a series of multiple guided projects (learning path) that is designed for anyone who wants to master data analysis with pandas.

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

•Introduction and Pandas Series Fundamentals

•Define a Pandas Series with Custom Index

•Define a Pandas Series from a Dictionary 

•Pandas Attributes

•Pandas Methods

•Import One Dimensional CSV Data 

•Pandas Built-in Functions

•Sorting Pandas Series

•Perform Math Operations on Pandas Series

•Check if a Given Element Exists in a Pandas Series

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Python Coding challenge - Day 66 | What is the output of the following Python code?

 

In Python, when you multiply a boolean value by an integer, the boolean value is implicitly converted to an integer. In this case, False is equivalent to 0, so False * 10 will result in 0.

If you run the following code: print(False * 10)

The output will be: 0

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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (Free PDF)

 

Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.

With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started.

Use Scikit-learn to track an example ML project end to end

Explore several models, including support vector machines, decision trees, random forests, and ensemble methods

Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection

Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers

Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning 

Buy - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

PDF Link - 

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Computers, Waves, Simulations: A Practical Introduction to Numerical Methods using Python (Free Course)

 

What you'll learn

How to solve a partial differential equation using the finite-difference, the pseudospectral, or the linear (spectral) finite-element method.

Understanding the limits of explicit space-time simulations due to the stability criterion and spatial and temporal sampling requirements.

Strategies how to plan and setup sophisticated simulation tasks.

Strategies how to avoid errors in simulation results. 

There are 9 modules in this course

Interested in learning how to solve partial differential equations with numerical methods and how to turn them into python codes? This course provides you with a basic introduction how to apply methods like the finite-difference method, the pseudospectral method, the linear and spectral element method to the 1D (or 2D) scalar wave equation. The mathematical derivation of the computational algorithm is accompanied by python codes embedded in Jupyter notebooks. In a unique setup you can see how the mathematical equations are transformed to a computer code and the results visualized. The emphasis is on illustrating the fundamental mathematical ingredients of the various numerical methods (e.g., Taylor series, Fourier series, differentiation, function interpolation, numerical integration) and how they compare. You will be provided with strategies how to ensure your solutions are correct, for example benchmarking with analytical solutions or convergence tests. The mathematical aspects are complemented by a basic introduction to wave physics, discretization, meshes, parallel programming, computing models. 

The course targets anyone who aims at developing or using numerical methods applied to partial differential equations and is seeking a practical introduction at a basic level. The methodologies discussed are widely used in natural sciences,  engineering, as well as economics and other fields. 

Join - Computers, Waves, Simulations: A Practical Introduction to Numerical Methods using Python

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20 extremely useful single-line Python codes

 

#!/usr/bin/env python

# coding: utf-8

# # 20 extremely useful single-line Python codes

# #  1.  Swap Variables:

# In[ ]:

a, b = b, a

# # 2. Find Maximum Element in a List:

# In[ ]:

max_element = max(lst)

# # 3. Find Minimum Element in a List:

# In[ ]:

min_element = min(lst)

# # 4. List Comprehension:

# In[ ]:

squared_numbers = [x**2 for x in range(10)]

# # 5. Filter List Elements:

# In[ ]:

even_numbers = list(filter(lambda x: x % 2 == 0, lst))

# # 6. Map Function:

# In[ ]:

doubled_numbers = list(map(lambda x: x * 2, lst))

# # 7. Sum of List Elements:

# In[ ]:

total = sum(lst)

# # 8. Check if All Elements in a List are True:

# In[ ]:

all_true = all(lst)

# # 9. Check if Any Element in a List is True:

# In[ ]:

any_true = any(lst)

# # 10. Count Occurrences of an Element in a List:

# In[ ]:

count = lst.count(element)

# # 11. Reverse a String:

# In[ ]:

reversed_str = my_str[::-1]

# # 12. Read a File into a List of Lines:

# In[ ]:

lines = [line.strip() for line in open('file.txt')]

# # 13. Inline If-Else:

# In[ ]:

result = "even" if x % 2 == 0 else "odd"

# # 14. Flatten a Nested List:

# In[ ]:

flat_list = [item for sublist in nested_list for item in sublist]

# # 15. Find the Factorial of a Number:

# In[ ]:

factorial = 1 if n == 0 else functools.reduce(lambda x, y: x * y, range(1, n+1))

# # 16. List Unique Elements:

# In[ ]:

unique_elements = list(set(lst))

# # 17. Execute a Function for Each Element in a List:

# In[ ]:

result = list(map(lambda x: my_function(x), lst))

# # 18. Calculate the Average of a List:

# In[ ]:

average = sum(lst) / len(lst) if len(lst) > 0 else 0

# # 19. Convert a String to a List of Characters:

# In[ ]:

char_list = list("hello")

# # 20. Find Common Elements Between Two Lists:

# In[ ]:

common_elements = list(set(lst1) & set(lst2))

# In[ ]:

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