Python Program to Check prime number
Code Explanation
If the Loop Completes Without Finding Divisors
Python Developer November 27, 2024 100 Python Programs for Beginner, Python No comments
If the Loop Completes Without Finding Divisors
Python Coding November 27, 2024 100 Python Programs for Beginner No comments
def is_even(number):
if number == 0:
return True
elif number == 1:
return False
else:
return is_even(number - 2)
num = int(input("Enter a number: "))
if is_even(num):
print(f"The number {num} is Even.")
else:
print(f"The number {num} is Odd.")
#source code --> clcoding.com
Python Coding November 27, 2024 100 Python Programs for Beginner, Python No comments
def grade_student(marks):
if marks >= 90:
return "A"
elif marks >= 80:
return "B"
elif marks >= 70:
return "C"
elif marks >= 60:
return "D"
elif marks >= 50:
return "E"
else:
return "F"
marks = float(input("Enter the marks of the student: "))
grade = grade_student(marks)
print(f"The grade of the student is: {grade}")
#source code --> clcoding.com
Python Developer November 27, 2024 Coursera, Data Science, Google No comments
In today’s data-driven world, the ability to analyze and interpret data has become one of the most valuable skills in any field. Whether you're looking to advance your career in data science, improve your business decision-making, or simply explore the world of data, learning how to use Python for data analysis is a powerful tool. If you want to gain a solid foundation in data analysis with Python, the Data Analysis with Python course on Coursera is the perfect place to start.
Python is widely recognized as one of the best programming languages for data analysis, thanks to its simplicity, versatility, and the vast array of libraries it offers, such as Pandas, Matplotlib, and NumPy. The Data Analysis with Python course on Coursera introduces you to the key tools and techniques necessary to extract meaningful insights from raw data using Python.
Comprehensive Learning: The course covers the complete data analysis pipeline, from data cleaning and exploration to visualization and advanced analysis techniques.
Hands-On Approach: Learn through practical, real-world exercises that help you apply your learning directly to projects.
Expert-Led: The course is taught by experts from the IBM Data Science team, providing insights from the frontlines of data science and analysis.
Career Advancement: Python skills are in high demand, and this course can boost your career by providing you with the skills needed to perform data analysis efficiently.
Flexible Learning: As with all Coursera courses, you can learn at your own pace, making it perfect for students, professionals, or anyone with a busy schedule.
Start by understanding the role of Python in data analysis and familiarize yourself with Python libraries like Pandas, NumPy, and Matplotlib.
Learn how Python can be used to handle and manipulate data, perform basic statistics, and create meaningful visualizations.
Data Cleaning and Preprocessing
In real-world data, the information you need is often messy. This section teaches you how to clean and prepare your data by handling missing values, duplicates, and errors.
You’ll learn how to reshape data and make it ready for analysis using Python’s powerful libraries.
Exploratory Data Analysis (EDA)
Understand the process of exploring data to find patterns, relationships, and insights.
Learn techniques for summarizing and visualizing data using statistical plots, histograms, scatter plots, and more.
Gain hands-on experience with Matplotlib and Seaborn for data visualization.
Data Analysis with Pandas
Dive deep into Pandas, one of the most widely-used libraries for data manipulation.
Learn how to load, inspect, and manipulate data using DataFrames and Series. Understand operations like sorting, grouping, filtering, and merging datasets.
Advanced Data Analysis Techniques
Learn more advanced topics like working with time-series data, handling categorical data, and using NumPy for mathematical computations.
Discover the power of SciPy and other Python libraries to conduct in-depth analysis on complex datasets.
Data Visualization and Reporting
Master data visualization techniques to communicate your findings effectively.
Learn how to create professional charts, graphs, and dashboards using Matplotlib and Seaborn.
Discover best practices for presenting your data to different audiences.
Capstone Project
The course concludes with a hands-on capstone project, where you’ll apply everything you’ve learned to analyze a real-world dataset and present your findings.
This project is designed to showcase your new skills and can be added to your portfolio for potential employers to see.
High Demand in the Job Market
Data analytics is one of the most sought-after skills today. By mastering Python, you’ll be able to work in industries ranging from healthcare and finance to technology and entertainment. Python's data analysis capabilities make it an essential skill for roles such as Data Analyst, Data Scientist, Business Analyst, and more.
Real-World Applications
Throughout the course, you’ll work with actual datasets, learning how to clean, manipulate, and analyze data just like professional data analysts do. This hands-on experience is key to preparing you for the challenges of real-world data analysis.
Easy-to-Learn
Python is known for being beginner-friendly, and this course breaks down complex concepts into easy-to-understand lessons. Whether you're new to programming or data analysis, this course is designed to help you learn at your own pace.
Career-Boosting Certification
Upon completing the course, you’ll receive a certificate from Coursera and IBM, a recognized leader in data science. This certificate can add significant value to your resume and LinkedIn profile, helping you stand out in the competitive job market.
Conclusion
The Data Analysis with Python course on Coursera is an excellent starting point for anyone looking to enter the world of data science and analytics. With practical, hands-on learning, you’ll gain the skills needed to clean, analyze, and visualize data, and transform raw data into actionable insights.
By mastering Python for data analysis, you’ll be well-equipped to tackle real-world challenges and enhance your career prospects in this rapidly growing field. So, whether you're starting from scratch or looking to level up your skills, this course is the perfect way to get started with data analysis.
Ready to start analyzing data? Enroll in the course today on Coursera and take your first step towards becoming a skilled data analyst.
Python Developer November 27, 2024 Coursera, Data Science, Google No comments
Python has become a cornerstone for data science and artificial intelligence (AI). For those seeking to harness the power of Python in these domains, Coursera's "Python for Applied Data Science AI" offers a perfect blend of foundational knowledge and hands-on experience. Developed by the University of Michigan, this course is part of the broader Applied Data Science with Python Specialization. Here’s an in-depth look at what the course entails and why it’s an invaluable resource for aspiring data scientists.
The "Python for Applied Data Science AI" course is tailored for beginners, providing a solid foundation in Python programming specifically geared towards data science and AI applications. The course emphasizes practical coding skills, enabling learners to solve real-world problems with Python.
1. Practical Python Applications
The course focuses on using Python for data science workflows. You’ll learn how Python integrates seamlessly with popular libraries like Pandas, NumPy, and Matplotlib to perform data manipulation, analysis, and visualization.
2. Hands-on Learning with Jupyter Notebooks
You’ll work extensively with Jupyter Notebooks, a powerful tool used by data scientists for writing and sharing live code, equations, and visualizations.
3. Introduction to Data Science Tools
Gain insights into essential tools like Python’s data structures, basic programming constructs, and libraries such as:
Pandas for data manipulation
NumPy for numerical computations
Matplotlib for creating stunning data visualizations
4. Beginner-Friendly
No prior experience with Python is required, making it ideal for those just starting their data science journey. The course also includes coding exercises and quizzes to reinforce learning.
This course is perfect for:
Aspiring data scientists looking to build their Python programming skills.
Professionals in AI who want to explore Python’s role in machine learning and analytics.
Beginners in programming who wish to enter the world of data science with Python.
1. Relevance to Industry Trends
Python’s dominance in data science and AI ensures that the skills you gain are directly applicable to the job market.
2. Flexible Learning
Coursera’s self-paced format allows you to learn at your own speed while balancing other commitments.
3. University of Michigan Expertise
The course is developed by top educators and researchers, ensuring high-quality content.
4. Gateway to Advanced Topics
This course lays the groundwork for exploring advanced topics like machine learning, deep learning, and big data analytics.
With over thousands of positive reviews, learners praise the course for its clear explanations, practical exercises, and its ability to make Python approachable even for beginners. Many report feeling confident in tackling more complex data science projects after completing the course.
Whether you’re new to programming or aiming to upskill for a career in data science or AI, "Python for Applied Data Science AI" is a fantastic starting point. Enroll today on Coursera and unlock the potential of Python in transforming data into actionable insights.
Python is the backbone of modern data science and AI, and mastering it can open doors to endless opportunities in technology and analytics. Coursera's "Python for Applied Data Science AI" course offers a comprehensive yet beginner-friendly introduction to Python’s role in solving real-world data challenges. From learning essential libraries like Pandas and NumPy to creating impactful visualizations with Matplotlib, this course equips you with the skills needed to dive deeper into data science.
By enrolling in this course, you’re not just learning a programming language—you’re stepping into the ever-growing fields of AI and data analytics. Whether you’re an aspiring data scientist, a professional looking to pivot, or simply curious about Python’s power, this course serves as the perfect launchpad.
Begin your journey today and transform your passion for data into expertise!
Python Developer November 27, 2024 Coursera, Data Science, Google No comments
Python Coding November 26, 2024 100 Python Programs for Beginner, Python No comments
Python Developer November 26, 2024 Coursera, Google, Machine Learning No comments
Machine learning (ML) is transforming industries and redefining the way businesses operate, making it one of the most sought-after skills in the modern workforce. But for many, the journey into the world of machine learning can seem overwhelming. That’s where Coursera’s Launching Machine Learning course comes in. Designed to demystify the field, this course provides a clear and structured pathway to get started with machine learning, even if you’re a beginner.
In this blog, we’ll explore what the course covers, its unique approach to teaching machine learning, and why it’s an excellent choice for anyone looking to build foundational knowledge in this exciting domain.
Machine learning is the engine behind many of today’s technological advancements, from personalized recommendations on Netflix to autonomous vehicles. Its applications span across industries, including healthcare, finance, retail, and beyond.
This introductory course is part of Google Cloud’s learning offerings on Coursera. It’s designed to equip learners with an understanding of machine learning concepts and practical experience in building simple models. Whether you’re completely new to ML or looking to strengthen your foundation, this course is structured to set you on the right path.
What Will You Learn?
1. Beginner-Friendly Approach
The course is designed with beginners in mind, using clear explanations and practical examples to make complex topics accessible. Even if you don’t have a background in programming or data science, you’ll find the content approachable and engaging.
2. Hands-On Learning
Theory alone isn’t enough to master machine learning. This course emphasizes hands-on experience, allowing you to apply what you learn to real-world datasets. This practical approach ensures that you not only understand concepts but also know how to use them in practice.
3. Introduction to Google Cloud AI
Google Cloud is a leader in AI and machine learning services. The course introduces you to tools like TensorFlow and AutoML, giving you a glimpse into the possibilities of using cloud-based platforms for ML projects.
4. Flexible and Self-Paced
As with all Coursera courses, Launching Machine Learning is self-paced, allowing you to learn at your convenience. This flexibility is perfect for professionals, students, or anyone juggling multiple commitments.
5. Certificate of Completion
Upon finishing the course, you’ll earn a shareable certificate that demonstrates your foundational knowledge of machine learning—a valuable addition to your resume or LinkedIn profile.
Machine learning is reshaping industries and creating new opportunities for professionals worldwide. Coursera’s Launching Machine Learning course is the perfect starting point for anyone looking to build a foundation in this transformative technology. With its beginner-friendly approach, hands-on projects, and insights into Google Cloud AI tools, the course equips you with the knowledge and confidence to take your first steps into the world of machine learning.
Enroll today, and start your journey into the future of technology!
Python Developer November 26, 2024 Coursera, Data Science No comments
In the age of big data, the ability to analyze and visualize data effectively is essential for driving business insights and making informed decisions. Whether you're a data analyst, business intelligence (BI) professional, or anyone involved in data-driven decision-making, the Analyzing and Visualizing Data in Looker course on Coursera provides a powerful tool to help you succeed.
Looker, a modern data platform now part of Google Cloud, is designed to help businesses analyze their data with ease and efficiency. It offers a user-friendly interface, powerful querying capabilities, and robust visualization features, making it one of the most sought-after BI tools in the market. In this blog, we’ll take a closer look at the Analyzing and Visualizing Data in Looker course, its key features, and why it’s the ideal choice for anyone looking to enhance their data analytics skills.
Looker is a business intelligence (BI) and data analytics platform that allows users to explore, analyze, and share real-time business analytics easily. It connects to a variety of data sources, enabling users to query large datasets and generate reports and visualizations to make data-driven decisions.
One of Looker’s key features is its ability to create LookML models, which define how data should be queried and structured in a consistent manner. This allows both analysts and non-technical users to work seamlessly with data, enabling efficient collaboration across teams.
Looker’s advanced visualization tools help turn raw data into actionable insights by presenting the information in an intuitive, digestible format. Whether it's a bar chart, line graph, or interactive dashboard, Looker allows users to craft compelling visualizations that tell the story behind the data.
Data visualization is a critical skill for data analysts and business leaders alike. The main goal of data visualization is to help people understand complex data more easily by presenting it in a visual format. A well-designed chart, graph, or dashboard can highlight trends, patterns, and anomalies that may be difficult to spot in raw datasets.
Effective data visualization not only simplifies the communication of complex data but also empowers teams to make quicker, data-driven decisions. It’s no surprise that organizations across industries rely on powerful visualization tools like Looker to make sense of their data and gain a competitive edge.
Learn the skills needed to do data exploration and analysis in Looker to empower others to solve business needs.
Use dimensions, measures, filters, table calculations and pivots to analyze and visualize data.
Create and share near real-time data visualizations using Looks, dashboards, and boards.
Use the Looker Integrated Development Environment (IDE) and project version control to modify LookML projects and curate Explores that empower business users to leverage data in their everyday workflows
One of the biggest advantages of this course is its hands-on approach. You’ll get the opportunity to work with real datasets, perform data analysis, and create visualizations from scratch using Looker. This practical experience is invaluable for building your confidence and expertise in data analytics.
The course covers everything from the basics of Looker to more advanced data analysis techniques. Whether you're a beginner looking to get started with Looker or an experienced analyst seeking to deepen your skills, this course offers comprehensive training that will help you build a well-rounded skill set.
Data visualization is a highly sought-after skill across industries. By mastering Looker, you’ll be equipped with one of the most powerful and widely used BI tools in the market. Looker is used by major companies like IBM, Uber, and Verizon, and having proficiency in Looker can significantly enhance your career prospects.
As Looker is now part of Google Cloud, the course also gives you an introduction to how Looker integrates with other Google Cloud services, making it easier to build end-to-end data solutions and access data at scale.
Upon completion of the course, you’ll receive a certification from Coursera that can be added to your resume or LinkedIn profile. This credential demonstrates your expertise in data analysis and visualization with Looker, adding value to your professional portfolio.
The Analyzing and Visualizing Data in Looker course is ideal for anyone who works with data and is looking to improve their ability to analyze and visualize it effectively. This includes:
Data analysis and visualization are powerful tools that help businesses make informed decisions, and Looker is one of the best platforms for creating insightful, interactive reports. The Analyzing and Visualizing Data in Looker course on Coursera provides everything you need to get up to speed with Looker, from data exploration to advanced reporting and visualization techniques. By the end of the course, you’ll have the skills and confidence to leverage Looker’s powerful features and drive meaningful business insights through data.
If you’re looking to enhance your data analytics and visualization skills and gain proficiency in one of the most popular BI tools in the industry, this course is the perfect choice. Start learning today and unlock the full potential of your data with Looker!
Python Developer November 26, 2024 Coursera, Google, Machine Learning No comments
In today’s data-driven world, organizations rely on vast amounts of data to make informed decisions, optimize operations, and drive innovation. Data engineers play a critical role in this ecosystem, ensuring that data flows seamlessly across various systems, is processed efficiently, and is made accessible for analysis. If you’re interested in pursuing a career in data engineering, there’s no better way to learn the necessary skills than with the Google cloud professional in Data Engineering on Coursera.
This comprehensive program is designed to teach you the fundamentals of data engineering on Google Cloud Platform (GCP) — one of the world’s leading cloud computing platforms. By the end of the course, you’ll be equipped with the skills to design, build, and maintain robust data systems, making you an essential asset to any organization.
Before diving into the details of the certification, it’s important to understand what data engineering is. At its core, data engineering involves the process of preparing and managing data for use by others, typically data scientists or business analysts. This includes:
As businesses generate more data than ever before, data engineers are crucial to making sure that data is available, structured, and ready for use.
Google Cloud Platform (GCP) is a powerful suite of cloud services that provides all the tools and infrastructure needed to build and scale data systems. GCP is especially well-known for its machine learning and data analytics capabilities, offering services like BigQuery, Dataflow, and Pub/Sub, which are widely used in the data engineering field.
With GCP, data engineers can:
Learning data engineering on Google Cloud gives you access to some of the most innovative and cutting-edge tools available in the cloud.
There are several reasons why this Professional Certificate is an excellent choice for aspiring data engineers:
Google Cloud is used by many organizations worldwide, and knowledge of GCP is a highly sought-after skill. By completing this certification, you’ll demonstrate your ability to work with one of the most widely used cloud platforms, making you attractive to potential employers.
Whether you’re a beginner or have some experience in data engineering, this course is designed to accommodate all levels. It starts with the basics and gradually builds your expertise, so you can confidently move to more advanced topics.
At the end of the course, you’ll earn a professional certificate from Google Cloud, which is a valuable credential that you can showcase to potential employers. It adds significant weight to your resume and proves your capability in the field of data engineering.
The program is offered entirely online, allowing you to learn at your own pace. Whether you’re working full-time or managing other commitments, you can complete the course on your schedule.
This certification is perfect for anyone looking to build or enhance their career in data engineering. Whether you’re new to the field or an experienced professional looking to specialize in cloud technologies, this program is a great fit for:
Data engineering is a rapidly growing field, and Google Cloud offers some of the best tools available for building scalable, efficient, and secure data systems. By enrolling in the Google Cloud Professional Certificate in Data Engineering on Coursera, you’ll gain the skills and knowledge necessary to thrive in this exciting field. Whether you’re just starting out or looking to level up your career, this certification will equip you with the practical, industry-relevant skills to succeed as a data engineer in today’s cloud-first world.
Start your journey toward becoming a Google Cloud Certified Data Engineer today!
Python Developer November 26, 2024 Coursera, Google, Machine Learning No comments
In the rapidly evolving field of artificial intelligence (AI) and machine learning (ML), staying ahead of the curve is essential for anyone looking to pursue a career in data science, engineering, or any related field. One powerful tool that has emerged in the AI and ML landscape is TensorFlow, an open-source library developed by Google that has revolutionized the way we build and deploy machine learning models. When combined with Google Cloud Platform (GCP), TensorFlow becomes even more powerful, offering cloud-based solutions that allow you to scale and optimize your models more efficiently. If you are looking to learn how to harness these technologies, the "Machine Learning with TensorFlow on Google Cloud Platform" specialization on Coursera is the perfect place to start.
TensorFlow is a robust framework for building machine learning models and performing complex numerical computations. Initially developed by Google Brain, it is now one of the most widely used libraries for creating deep learning models. TensorFlow offers flexibility, scalability, and high performance, making it an ideal choice for developing sophisticated AI applications such as image recognition, natural language processing, and predictive analytics.
What makes TensorFlow particularly attractive is its ability to run on multiple platforms, from mobile devices to large-scale distributed computing environments. It’s designed to be highly modular, enabling developers to use pre-built components or create custom solutions for their ML models.
Use Vertex AI AutoML and BigQuery ML to build, train, and deploy ML models
Implement machine learning models using Keras and TensorFlow 2.x
Implement machine learning in the enterprise best practices
Describe how to perform exploratory data analysis and improve data quality
Google Cloud Platform (GCP) is a suite of cloud services provided by Google, offering everything from computing power to machine learning APIs. For developers and data scientists, GCP provides a vast array of services that make deploying, training, and scaling machine learning models easier than ever before.
GCP includes services like:
When combined with TensorFlow, these services help take machine learning workflows to the next level, especially when dealing with large datasets or complex models that require heavy computation.
This Coursera specialization is a comprehensive, hands-on learning experience that takes you from beginner to advanced levels in the field of machine learning. By the end of this program, you will not only be comfortable using TensorFlow but also understand how to integrate it with the powerful cloud infrastructure provided by GCP. Here’s a breakdown of what the specialization covers:
The course starts with an introduction to TensorFlow basics, giving you a strong foundation in ML fundamentals. You’ll learn how to create and train simple models using TensorFlow, and explore the world of supervised and unsupervised learning.
You’ll dive into more advanced machine learning techniques such as Convolutional Neural Networks (CNNs), which are essential for tasks like image classification and object detection. The course provides in-depth knowledge of how deep learning works and how TensorFlow supports these complex models.
You’ll gain practical experience building real-world machine learning models with TensorFlow. The course covers the steps of setting up data pipelines, selecting models, training and tuning them, and evaluating their performance.
Once you’re comfortable building machine learning models, the specialization takes it to the next level by showing how to scale and deploy your models on GCP. You’ll learn how to use AI Platform for distributed training, handle large datasets efficiently, and deploy models to the cloud so they can be accessed by end-users globally.
The final courses focus on building an end-to-end machine learning pipeline, including data collection, model training, optimization, and deployment. By the end of the specialization, you’ll be able to seamlessly move from local model development to cloud-based deployment with TensorFlow and GCP.
This course is ideal for anyone looking to start a career in machine learning or AI, whether you are a beginner or have some experience with machine learning concepts. The specialization is especially beneficial for:
The "Machine Learning with TensorFlow on Google Cloud Platform" specialization on Coursera offers a rich learning experience, combining cutting-edge machine learning techniques with the scalability and power of Google Cloud. Whether you're a beginner or an experienced practitioner, this course will equip you with the skills to build and deploy machine learning models at scale, making you a highly valuable asset in the tech industry.
Enroll today, and take the first step toward mastering machine learning with TensorFlow and Google Cloud!
Python Coding November 25, 2024 Python No comments
Python Coding November 25, 2024 Python No comments
import psutil
# List all running processes
print(f"{'PID':<10} {'Name':<25} {'Status':<15} {'Username':<20}")
print("-" * 70)
for proc in psutil.process_iter(['pid', 'name', 'status', 'username']):
try:
pid = proc.info['pid']
name = proc.info['name'] or "N/A"
status = proc.info['status'] or "N/A"
username = proc.info['username'] or "N/A"
print(f"{pid:<10} {name:<25} {status:<15} {username:<20}")
except (psutil.NoSuchProcess, psutil.AccessDenied):
pass
#source code --> clcoding.com
Python Coding November 25, 2024 Python No comments
import plotly.graph_objects as go
stages = ['A', 'B', 'C', 'D']
values = [1000, 700, 400, 250]
fig = go.Figure(go.Funnel(
y=stages,
x=values,
textinfo="value+percent initial"
))
fig.update_layout(
title="Funnel Chart Example",
title_x=0.5
)
fig.show()
#source code --> clcoding.com
Python Coding November 25, 2024 Coursera, Data Science No comments
Focus: The course typically introduces foundational concepts of data analysis in Python, including how to represent, select, and iterate over data structures.
Introduction to basic Python data types like integers, strings, lists, dictionaries, and arrays.
Conditional logic (if, elif, else) for filtering and selecting data.
Loops (for and while) to process datasets effectively.
Iteration through lists, dictionaries, and other data structures.
This course is part of the Computational Thinking with Beginning C Programming 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
Python Coding November 24, 2024 Python Coding Challenge No comments
Python Coding November 21, 2024 Python No comments
import os # Specify the path to count files and directories PATH = r'C:\Users\CLCODING\Downloads' files = 0 dirs = 0 for root, dirnames, filenames in os.walk(PATH): dirs += len(dirnames) files += len(filenames) print('Files:', files) print('Directories:', dirs) print('Total:', files + dirs)
Program Explanation
This Python program is used to count the total number of files and folders inside a given directory (including all subfolders). import os We import the os module because it helps us interact with files and folders in the operating system. PATH = r'C:\Users\CLCODING\Downloads' This sets the location (folder path) where we want to count files and folders. The letter r before the string makes it a raw string, which prevents Python from misreading backslashes. files = 0 dirs = 0 We start with two counters: files = 0 → to count number of files dirs = 0 → to count number of folders (directories) for root, dirnames, filenames in os.walk(PATH): os.walk() goes through the folder and all of its subfolders. For each location it visits: root → the current folder path dirnames → list of all subfolders in that location filenames → list of all files in that location dirs += len(dirnames) files += len(filenames) len(dirnames) gives how many folders are in the current location. len(filenames) gives how many files are in the current location. We add them to our counters. print('Files:', files) print('Directories:', dirs) print('Total:', files + dirs) Finally, we display: total number of files total number of directories total items (files + directories) Output Example (depends on your folder): Files: 250 Directories: 40 Total: 290
Python Coding November 20, 2024 Python No comments
import cv2
import numpy as np
import pyautogui
import keyboard
screen_size = pyautogui.size()
fps = 20
fourcc = cv2.VideoWriter_fourcc(*"XVID")
output_file = "screen_recording_clcoding.mp4"
out = cv2.VideoWriter(output_file, fourcc, fps,
(screen_size.width, screen_size.height))
print("Recording... Press 'q' to stop.")
while True:
screen = pyautogui.screenshot()
frame = np.array(screen)
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
out.write(frame)
if keyboard.is_pressed('q'):
print("Recording stopped.")
break
out.release()
print(f"Video saved to {output_file}")
#source code --> clcoding.com
Python Coding November 20, 2024 AI, Python No comments
✔ 10+ hours of AI content from the fundamentals to advanced.
✔ Hands-on machine learning and deep learning projects with step-by-step coding instructions.
✔ Real-world end-to-end projects to help you build a professional AI portfolio.
✔ A private collaborative community of AI learners and professionals.
✔ Receive feedback on your projects from peers and community members.
✔ Direct access to your instructor.
✔ Lifetime access to every past and future courses and content.
Join today and enjoy a full 30-day free trial with complete access to all content. No strings attached – experience the program and decide if it's right for you. If you're not satisfied, you can cancel at any time during the trial with zero cost. We’re confident you’ll love it, but you’ve got nothing to lose with our risk-free guarantee!
The AI Learning Hub is your ongoing path to mastering AI. This syllabus outlines the key topics you’ll cover throughout the program. Each section is designed to build on the last, ensuring you develop both foundational and advanced skills through practical, hands-on learning. As part of this continuous cohort, new content will be added regularly, so you’ll always be learning the latest in AI.
This schedule is flexible and may change depending on the learning pace of everyone. But don’t worry—once the materials are published, you can go back and learn at your own speed whenever you want.
Data Types & Variables: Understand basic data types and variables.
Loops & Iterators: Learn how to iterate over data efficiently.
Functions & Lambdas: Write reusable code and anonymous functions.
Lists, Tuples, Sets, Dictionaries: Work with core Python data structures.
Conditionals: Make decisions using if, elif, and else.
Exception Handling: Handle errors gracefully.
Classes & OOP: Grasp object-oriented programming, inheritance, polymorphism, and encapsulation.
Series & DataFrames: Understand the building blocks of Pandas.
Editing & Retrieving Data: Learn data selection and modification techniques.
Importing Data: Import data from CSV, Excel, and databases.
Grouping Data: Use groupby for aggregate operations.
Merging & Joining Data: Combine datasets efficiently.
Sorting & Filtering: Organize and retrieve data.
Applying Functions to Data: Use functions to manipulate and clean data.
Basic Plotting: Create line plots, scatter plots, and histograms.
Bar Charts & Pie Charts: Display categorical data.
Time Series Plots: Visualize data over time.
Live Data Plotting: Create dynamic visualizations.
Creating Arrays: Learn about arrays and their manipulation.
Array Indexing & Slicing: Access and modify elements in arrays.
Universal Functions: Perform fast element-wise operations on arrays.
Linear Algebra & Statistics Functions: Apply matrix operations and statistical computations.
ML Life Cycle: Understand the workflow of building machine learning systems.
Key Algorithms: Explore algorithms like Linear Regression, Decision Trees, Random Forests, and K-Nearest Neighbors.
Evaluation Metrics: Learn about precision, recall, F1-scores, and the importance of model evaluation.
Overfitting & Underfitting: Learn how to handle data-related challenges.
Projects: Apply your knowledge through hands-on projects, solving real-world problems.
Neural Networks: Learn how artificial neural networks work.
Activation Functions: Explore functions like Sigmoid, ReLU, and Tanh.
Convolutional Neural Networks (CNNs): Understand image-based models and apply them to real-world data.
Recurrent Neural Networks (RNNs) & LSTMs: Work with sequential data for time series or text.
Hyperparameter Tuning & Optimization: Fine-tune models for better performance.
Projects: Implement real-world deep learning models and deploy them into production environments.
Model Deployment Strategies: Learn how to deploy models using Flask, FastAPI, and cloud platforms.
Docker & Kubernetes: Containerize your applications and deploy them at scale.
Kubeflow: Set up workflows for automating ML pipelines.
MLflow: Track experiments and manage the machine learning lifecycle.
Airflow: Manage data workflows and model pipelines.
Cloud-Based Deployment: Deploy your models on platforms like AWS, GCP, and Azure.
Monitoring & Logging: Use tools like Prometheus and Grafana to monitor model performance and ensure they remain accurate over time.
CI/CD: Automate the deployment of machine learning models using CI/CD pipelines.
Complete ML Pipelines: Learn how to build a fully functional machine learning pipeline from data collection to deployment.
Data Preprocessing: Clean, process, and prepare data for machine learning models.
Model Building & Training: Implement and train machine learning models tailored to real-world scenarios.
Model Deployment: Deploy machine learning models into production environments, integrating with APIs and cloud services.
Monitoring & Maintenance: Understand how to monitor model performance over time and retrain models as needed.
Advanced NLP & Transformers: Dive deep into cutting-edge natural language processing techniques and transformer architectures.
Generative AI Models: Explore AI models that generate text, images, and audio, including GANs and diffusion models.
Custom AI Solutions: Learn how to customize AI models for specialized tasks and industries.
Suggest a Topic: You can suggest any advanced topics or areas of interest, and we will explore them together as part of the curriculum.
Python Coding November 19, 2024 Python No comments
The Complete Python Basic to Advanced course offers a thorough journey from basic syntax to advanced concepts, including object-oriented programming, data manipulation, and real-world applications, providing a solid foundation and practical skills in Python.
Grasp Python basics: Variables, loops, data
Master OOP: Classes, inheritance, polymorphism
Implement error handling for robust programs
Optimize code for efficiency and performance
Develop problem-solving with algorithms
Write clean, structured, and organized code
Manage files and perform data manipulation
Use advanced features: Decorators, generators
Build real-world apps with Python skills
Prepare for data science and machine learning
Python basics: Syntax, data types, and variables
Control structures: Loops and conditionals
Functions and modules for code organization
OOP concepts: Classes, objects, inheritance
Advanced topics: Decorators, generators, and more
Working with databases, APIs, and file handling
Basic Computer Skills: Ability to install software, browse the internet, and navigate file systems
No Prior Coding Experience Required: Designed for beginners with no programming background needed
Eagerness to Learn: A passion for learning and exploring programming concepts is highly encouraged
Logical Thinking: Basic understanding of logic and problem-solving will be advantageous for success
Time Commitment: Set aside regular time to engage fully and complete lessons, projects, and quizzes
Python Coding November 19, 2024 Python No comments
Master OOP in Python covers object-oriented programming principles, including classes, inheritance, polymorphism and encapsulation with hands-on examples to help you build robust, reusable and efficient Python applications.
Understand classes and objects in Python
Implement inheritance for code reusability
Use polymorphism for flexible code design
Master encapsulation to protect data
Work with constructors and destructors
Apply abstraction for simplified interfaces
Handle errors in OOP effectively
Build scalable apps using OOP principles
In-depth tutorials on OOP concepts
Real-world projects to apply OOP skills
Interactive quizzes for progress tracking
Hands-on coding exercises for practice
Expert tips from OOP professionals
Community forums for peer support
Basic Python Knowledge: Familiarity with Python fundamentals, including syntax, data types, and control structures.
Understanding of Functions: Knowledge of defining and using functions in Python.
Basic Programming Concepts: Familiarity with core programming concepts like variables, loops, and conditionals.
Problem-Solving Skills: Ability to break down problems and develop logical solutions.
Eagerness to Learn OOP: A strong interest in learning and applying object-oriented programming principles.
Access to a Development Environment: A computer with Python installed and a suitable IDE or text editor for coding.
Python Coding November 16, 2024 Python Coding Challenge No comments
x = "hello" * 2
print(x)
String Multiplication:
"hello" is a string.
2 is an integer.
When you multiply a string by an integer, the string is repeated that many times.
In this case, "hello" * 2 produces "hellohello", which is the string "hello" repeated twice.
Assignment:
The result "hellohello" is assigned to the variable x.
Print Statement:
The print(x) statement outputs the value of x, which is "hellohello".
Python Coding November 14, 2024 No comments
Python Coding November 13, 2024 AI, Google No comments
Unlock the Power of AI with Google’s AI Essentials Course on Coursera
Artificial Intelligence (AI) is reshaping industries, driving innovation, and solving complex challenges around the globe. As AI becomes an essential part of the tech landscape, learning its core principles has become crucial for both beginners and professionals. Google’s AI Essentials course on Coursera is designed to introduce you to the fundamentals of AI and equip you with the knowledge and skills needed to get started.
If you’re curious about AI and want to learn how it’s used to transform real-world applications, this course offers a comprehensive, beginner-friendly introduction. Let’s dive into what makes this course special and why it’s the perfect starting point for your AI journey.
AI has rapidly expanded beyond research labs into everyday life. It powers everything from personal voice assistants and recommendation engines to complex medical diagnostics and financial forecasting. AI literacy is becoming a vital skill across industries, making it increasingly valuable for professionals in any field. Learning AI basics gives you an edge in understanding and working with the tools that are shaping the future.
Google, a global leader in AI, has crafted the AI Essentials course on Coursera to help beginners gain foundational knowledge in this field. Created with clarity and simplicity in mind, the course provides learners with an accessible introduction to AI concepts, helping you understand what AI is, its potential, and how it’s applied in the world today.
Key Highlights of the Course:
The Google AI Essentials course covers several foundational topics essential to understanding AI and how it’s changing industries. Here’s a quick look at what you’ll learn:
One of the standout features of this course is its focus on real-world applications, making it relatable for learners from any background. By the end of the course, you’ll gain insights into how AI applications solve problems across various industries:
This approach not only makes learning more engaging but also provides you with a broader understanding of how AI impacts different sectors.
Whether you're a professional looking to enhance your skillset, a student aiming to learn about AI, or just curious about technology, the Google AI Essentials course is a fantastic place to start. It’s a well-rounded introduction to AI fundamentals and applications, and it prepares you to explore further in the world of AI.
Learn more and enroll here: Coursera Google AI Essentials Course.
Artificial Intelligence is more than just a trend; it's a transformative technology that’s changing the world. Google’s AI Essentials course on Coursera offers a clear, beginner-friendly path to understanding AI’s impact, applications, and potential. By completing this course, you’ll gain a foundational knowledge that can serve as a stepping stone to advanced AI studies or applications in your own career.
Whether you’re a beginner or a professional looking to expand your skills, this course will give you the insights you need to understand AI's transformative potential. Embrace the future of technology—start your AI journey today!
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