macos

OSX (macOS) inside a Docker container.

Creator: Dockur
Stars ⭐️: 5.2k
Forked By: 185
https://github.com/dockur/macos

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Hands On Python Data Science - Data Science Bootcamp

Master Python for Data Science with Real-World Applications: Dive Deep into Data Analysis, Machine Learning

Rating ⭐️: 4.3 out 5
Students 👨‍🎓 : 4865
Duration ⏰ : 5.5 hours on-demand video
Created by 👨‍🏫: Sayman Creative Institute

🔗 COURSE LINK

⚠️ Its free for first 1000 enrollments only!


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Data Science for Value-Chain Management

How can you leverage data science to optimize operations and boost profitability?

Value Chain Management (VCM) refers to organizing activities that add value to the goods or services to achieve a competitive advantage in the marketplace.

This method helps organizations to effectively respond to market trends and improve efficiency to boost profitability.

We quickly delve into the fundamental components of Value Chain Management.

We will then explore four examples of data science applications to support strategic primary activities.

The value chain framework was originally introduced in Michael Porter's book “Competitive Advantage: Creating and Sustaining Superior Performance”.

This revolutionized how businesses perceive their operations by dissecting any business into a series of interconnected activities that contribute to creating and delivering value to customers.

🌳 What is a Decision Tree? 🌳

Imagine you're trying to figure out what to eat for dinner. 🍕🥗🍔 A decision tree is like a flowchart that helps you make choices based on yes/no questions:

Are you in the mood for something light?
Yes ➡️ Salad 🥗
No ➡️ Are you craving something cheesy?
Yes ➡️ Pizza 🍕
No ➡️ Burger 🍔

That's the essence of how decision trees work in machine learning!

🤖 In Machine Learning Terms:

Nodes: Questions (e.g., Is the price > $50?)
Branches: Possible answers (e.g., Yes/No)
Leaves: Final decisions or predictions (e.g., "Expensive" or "Affordable")

📊 They're used for tasks like:
✅ Classifying emails as spam or not.
✅ Predicting if a customer will buy a product.
✅ Diagnosing diseases in healthcare.

🎯 Why are they Awesome?

Simple to understand (even for non-techies).
Visual and interpretable (you can see the logic behind predictions).
Great for small-to-medium datasets.

⚡️ Limitations:

They can "overfit" (become too specific).
Not the best for very large datasets or complex problems.

🛠 Pro Tip:
To handle overfitting, use Random Forests 🌲🌲 or Gradient Boosted Trees 🚀—advanced versions of decision trees.

What do you think about decision trees? Drop your 🌳 below if you love their simplicity!

Begin to Use Cloud Computing with Anaconda Cloud Notebook

Begin to use Cloud Computing and Anaconda Cloud Notebook with Python, Data Science and Machine Learning [2024]

Rating ⭐️: 4.9 out 5
Students 👨‍🎓 : 1,028
Duration ⏰ : 40min on-demand video
Created by 👨‍🏫: Henrik Johansson

🔗 Course Link


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Big Data Pipeline Cheatsheet

Roadmap To Master Machine Learning