If you want to learn Python for data analysis prioritise:
- NumPy (maths)
- Pandas (data wrangling)
- Matplotlib (Data visualisation)
- Seaborn (built on top of matplotlib, has higher level interface capabilities)
- OS (Operating System Interaction for working with files and folders)
Master the above and you'll be able to defend yourself against any data requests that come your way.
#python
- NumPy (maths)
- Pandas (data wrangling)
- Matplotlib (Data visualisation)
- Seaborn (built on top of matplotlib, has higher level interface capabilities)
- OS (Operating System Interaction for working with files and folders)
Master the above and you'll be able to defend yourself against any data requests that come your way.
#python
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Python Physics-Based Simulation App Roadmap
Stage 1 - Python Basics (OOP, numpy)
Stage 2 - Physics Concepts (Gravity, Forces)
Stage 3 - Rendering (Pygame, Pyglet)
Stage 4 - Physics (Collisions, Rigid Bodies)
Stage 5 - Fluid Dynamics (Custom Algorithms)
Stage 6 - Interaction (Tkinter, PyQt)
Stage 7 - Optimization (Multithreading)
Stage 8 - Export (JSON Formats)
🏆 – Python Physics-Based Simulation App
Stage 1 - Python Basics (OOP, numpy)
Stage 2 - Physics Concepts (Gravity, Forces)
Stage 3 - Rendering (Pygame, Pyglet)
Stage 4 - Physics (Collisions, Rigid Bodies)
Stage 5 - Fluid Dynamics (Custom Algorithms)
Stage 6 - Interaction (Tkinter, PyQt)
Stage 7 - Optimization (Multithreading)
Stage 8 - Export (JSON Formats)
🏆 – Python Physics-Based Simulation App
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PYTHON FOR EVERYTHING:
Python + Flask = Web Development
Python + Django = Full-Stack Web Applications
Python + NumPy = Scientific Computing
Python + Pandas = Data Analysis
Python + TensorFlow = Machine Learning
Python + Keras = Deep Learning
Python + OpenCV = Computer Vision
Python + Matplotlib = Data Visualization
Python + Scrapy = Web Scraping
Python + PyTorch = Neural Networks
Python + SQLAlchemy = Database Management
Python + Selenium = Automated Testing
Python + Flask = Web Development
Python + Django = Full-Stack Web Applications
Python + NumPy = Scientific Computing
Python + Pandas = Data Analysis
Python + TensorFlow = Machine Learning
Python + Keras = Deep Learning
Python + OpenCV = Computer Vision
Python + Matplotlib = Data Visualization
Python + Scrapy = Web Scraping
Python + PyTorch = Neural Networks
Python + SQLAlchemy = Database Management
Python + Selenium = Automated Testing
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Python Libraries & Frameworks
👇👇
https://www.linkedin.com/posts/sqlspecialist_leverage-your-skills-from-google-ibm-and-activity-7268558128181379072--JuI
👇👇
https://www.linkedin.com/posts/sqlspecialist_leverage-your-skills-from-google-ibm-and-activity-7268558128181379072--JuI
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Cheat-Sheets For Pandas 🐼
Don't Forget to give reactions❤️
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🧱 Best Resource to learn Python
➼ Freecodecamp Python Course with FREE Certificate
➼ Python course for beginners by Microsoft
➼ Python course by Google
#python
➼ Freecodecamp Python Course with FREE Certificate
➼ Python course for beginners by Microsoft
➼ Python course by Google
#python
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Complete Roadmap to learn Generative AI in 2 months 👇👇
Weeks 1-2: Foundations
1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI.
2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning.
Weeks 3-4: Machine Learning Basics
1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics.
2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics.
Weeks 5-6: Deep Learning
1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes.
2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
Weeks 7-8: Generative Models
1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models.
Additional Tips:
- Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings.
- Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others.
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn Generative AI 👇👇
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Deep Learning Nanodegree Program with Real-world Projects
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
Weeks 1-2: Foundations
1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI.
2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning.
Weeks 3-4: Machine Learning Basics
1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics.
2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics.
Weeks 5-6: Deep Learning
1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes.
2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
Weeks 7-8: Generative Models
1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models.
Additional Tips:
- Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings.
- Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others.
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn Generative AI 👇👇
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Deep Learning Nanodegree Program with Real-world Projects
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
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Python Tip 🚀
Normally we use Square brackets to access a dictionary value using it's key.
Normally we use Square brackets to access a dictionary value using it's key.
To perform the above operation we can also make use of the python get method, which returns None if the input key is not part of the given dictionary.
This will save you from run time error (KeyError) if the key is not found and also you don't need to do extra coding to deal with unidentified keys.
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