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

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⚠️ 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

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Data Science

common data analysis and machine learning tasks using python

Creator: Ujjwal Karn
Stars ⭐️: 5.3k
Forked By: 1.5k
GithubRepo: https://github.com/ujjwalkarn/DataSciencePython


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Python for Deep Learning: Build Neural Networks in Python

Complete Deep Learning Course to Master Data science, Tensorflow, Artificial Intelligence, and Neural Networks

Rating ⭐️: 4.2 out 5
Students 👨‍🎓 : 145651
Duration ⏰ : 2 hours on-demand video
Created by 👨‍🏫: Meta Brains, school of AI

🔗 Course Link

⚠️ Its free for first 1000 enrollments only!


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15 different Careers in AI

𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 vs 𝐆𝐫𝐚𝐩𝐡 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬

Selecting the right database depends on your data needs—vector databases excel in similarity searches and embeddings, while graph databases are best for managing complex relationships between entities.


𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬:
- Data Encoding: Vector databases encode data into vectors, which are numerical representations of the data.
- Partitioning and Indexing: Data is partitioned into chunks and encoded into vectors, which are then indexed for efficient retrieval.
- Ideal Use Cases: Perfect for tasks involving embedding representations, such as image recognition, natural language processing, and recommendation systems.
- Nearest Neighbor Searches: They excel in performing nearest neighbor searches, finding the most similar data points to a given query efficiently.
- Efficiency: The indexing of vectors enables fast and accurate information retrieval, making these databases suitable for high-dimensional data.

𝐆𝐫𝐚𝐩𝐡 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬:
- Relational Information Management: Graph databases are designed to handle and query relational information between entities.
- Node and Edge Representation: Entities are represented as nodes, and relationships between them as edges, allowing for intricate data modeling.
- Complex Relationships: They excel in scenarios where understanding and navigating complex relationships between data points is crucial.
- Knowledge Extraction: By indexing the resulting knowledge base, they can efficiently extract sub-knowledge bases, helping users focus on specific entities or relationships.
- Use Cases: Ideal for applications like social networks, fraud detection, and knowledge graphs where relationships and connections are the primary focus.

𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧:
Choosing between a vector and a graph database depends on the nature of your data and the type of queries you need to perform. Vector databases are the go-to choice for tasks requiring similarity searches and embedding representations, while graph databases are indispensable for managing and querying complex relationships.

Source: Ashish Joshi

Data Science Full Course For Beginners
⏰ 24 hours long

Created by IBM ✅

https://www.youtube.com/watch?v=WlLgysXJ0Ec

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