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Programmation Python

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📖 30 days roadmap to learn Python for Data Analysis 😄👇 Days 1-5: Introduction to Python 1. Day 1: Install Python and a code editor (e.g., Anaconda, Jupyter Notebook). 2. Day 2-5: Learn Python basics (variables, data types, and basic operations). Days 6-10: Control Flow and Functions 6. Day 6-8: Study control flow (if statements, loops). 9. Day 9-10: Learn about functions and modules in Python. Days 11-15: Data Structures 11. Day 11-12: Explore lists, tuples, and dictionaries. 13. Day 13-15: Study sets and string manipulation. Days 16-20: Libraries for Data Analysis 16. Day 16-17: Get familiar with NumPy for numerical operations. 18. Day 18-19: Dive into Pandas for data manipulation. 20. Day 20: Basic data visualization with Matplotlib. Days 21-25: Data Cleaning and Analysis 21. Day 21-22: Data cleaning and preprocessing using Pandas. 23. Day 23-25: Exploratory data analysis (EDA) techniques. Days 26-30: Advanced Topics 26. Day 26-27: Introduction to data visualization with Seaborn. 27. Day 28-29: Introduction to machine learning with Scikit-Learn. 30. Day 30: Create a small data analysis project. Use platforms like Kaggle to find datasets for projects & GeekforGeeks to practice coding problems. ENJOY LEARNING 👍👍

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🔰 Windows vs Linux
🔰 Windows vs Linux

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🎉🥳Happy New Year 2025 to Everyone! 🎉 Dear subscribers, thank you for your amazing support throughout 2024. We're starting
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🔰 The Complete React Native + Hooks Course 🌟 4.6 - 44567 votes 💰 Original Price: $74.99 📖 Understand React Native with Ho
🔰 The Complete React Native + Hooks Course 🌟 4.6 - 44567 votes 💰 Original Price: $74.99
📖 Understand React Native with Hooks, Context, and React Navigation.
🔊 Taught By: Stephen Grider 📤 Download Full Course 📤 Download All Courses

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🧠 Face Recognition with Machine Learning + Deploy Flask App 🌟 4.4 - 459 votes 💰 Original Price: $69.99 📖 Create an Face R
🧠 Face Recognition with Machine Learning + Deploy Flask App 🌟 4.4 - 459 votes 💰 Original Price: $69.99
📖 Create an Face Recognition project from scratch with Python, OpenCV , Machine Learning Algorithms, Flask, Heroku Deploy
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How to get job as python fresher? 1. Get Your Python Fundamentals Strong You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview. 2. Learn Python Frameworks As a beginner, you’re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers. 3. Build Some Relevant Projects You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once you’ll learn several Python web frameworks and other trending technologies. 4. Get Exposure to Trending Technologies Using Python. Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity. 5. Do an Internship & Grow Your Network. You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.

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🔅 Machine Learning and AI Foundations: Advanced Decision Trees with KNIME 🌐 Author: Keith McCormick 🔰 Level: Advanced ⏰ Du
🔅 Machine Learning and AI Foundations: Advanced Decision Trees with KNIME 🌐 Author: Keith McCormick 🔰 Level: AdvancedDuration: 1h 33m
🌀 Learn to go beyond the basic decision tree algorithms in KNIME by accessing WEKA, R, and Python-based decision tree and rule induction algorithms from within the KNIME platform.
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⌨️ Encrypt PDF files using Python
⌨️ Encrypt PDF files using Python

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⌨️ Python Roadmap
⌨️ Python Roadmap

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20 - Adding a Checkbox Input to Change Out Bar Chart Marker Colors

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19 - Understanding the Shiny Reactivity Model (How Does Shiny Render Things?)

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18 - Quick Formatting Adjustments

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17 - Rendering Shiny Inputs Within Text

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16 - Filtering City Data with Select Inputs (UI.Input_Selectize)

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15 - Creating an Additional Visualization (Sales Over Time by City) — Continued

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14 - What are Reactive.Calcs and How Do We Use Them Properly? (DataFrame Best Practices)

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13 - Creating an Additional Visualization (Sales Over Time by City)

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12 - Adding Shiny Components (Inputs, Outputs, & Display Messages)

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11 - Getting Started with Code (Part 2)

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47 - Final words! Like & Subscribe pretty please!!