Mathematics for Machine Learning

📚 Book

𝗡𝗼 𝗗𝗲𝗴𝗿𝗲𝗲? 𝗡𝗼 𝗣𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘀𝗲 𝟰 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗖𝗮𝗻 𝗟𝗮𝗻𝗱 𝗬𝗼𝘂 𝗮 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗝𝗼𝗯😍

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Here are 10 project ideas to work on for Data Analytics

1. Customer Churn Prediction: Predict customer churn for subscription-based services. Skills: EDA, classification models. Tools: Python, Scikit-Learn.
2. Retail Sales Forecasting: Forecast sales using historical data. Skills: Time series analysis. Tools: Python, Statsmodels.
3. Sentiment Analysis: Analyze sentiments in product reviews or tweets. Skills: Text processing, NLP. Tools: Python, NLTK.
4. Loan Approval Prediction: Predict loan approvals based on credit risk. Skills: Classification models. Tools: Python, Scikit-Learn.
5. COVID-19 Data Analysis: Explore and visualize COVID-19 trends. Skills: EDA, visualization. Tools: Python, Tableau.
6. Traffic Accident Analysis: Discover patterns in traffic accidents. Skills: Clustering, heatmaps. Tools: Python, Folium.
7. Movie Recommendation System: Build a recommendation system using user ratings. Skills: Collaborative filtering. Tools: Python, Scikit-Learn.
8. E-commerce Analysis: Analyze top-performing products in e-commerce. Skills: EDA, association rules. Tools: Python, Apriori.
9. Stock Market Analysis: Analyze stock trends using historical data. Skills: Moving averages, sentiment analysis. Tools: Python, Matplotlib.
10. Employee Attrition Analysis: Predict employee turnover. Skills: Classification models, HR analytics. Tools: Python, Scikit-Learn.

And this is how you can work on

Here’s a compact list of free resources for working on data analytics projects:

1. Datasets
• Kaggle Datasets: Wide range of datasets and community discussions.
• UCI Machine Learning Repository: Great for educational datasets.
• Data.gov: U.S. government datasets (e.g., traffic, COVID-19).
2. Learning Platforms
• YouTube: Channels like Data School and freeCodeCamp for tutorials.
• 365DataScience: Data Science & AI Related Courses
3. Tools
• Google Colab: Free Jupyter Notebooks for Python coding.
• Tableau Public & Power BI Desktop: Free data visualization tools.
4. Project Resources
• Kaggle Notebooks & GitHub: Code examples and project walk-throughs.
• Data Analytics on Medium: Project guides and tutorials.

ENJOY LEARNING ✅️✅️

#datascienceprojects

𝟱 𝗙𝗿𝗲𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗵𝗮𝘁’𝗹𝗹 𝗠𝗮𝗸𝗲 𝗦𝗤𝗟 𝗙𝗶𝗻𝗮𝗹𝗹𝘆 𝗖𝗹𝗶𝗰𝗸.😍

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Python Roadmap: 🗺

📂 Basics
 ∟📂 Data Types & Variables
 ∟📂 Operators & Expressions
 ∟📂 Control Flow (if, loops)
  ∟📂 Functions & Modules
   ∟📂 File Handling
    ∟📂 OOP (Classes & Objects)
     ∟📂 Exception Handling
      
∟📂 Advanced Topics (Decorators, Generators)
 ∟📂 Libraries (NumPy, Pandas, Matplotlib)
 ∟📂 Web Scraping / API Integration
 ∟📂 Frameworks (Flask/Django)
  ∟📂 Automation & Scripting
   ∟📂 Projects
    ∟ ✅ Apply For Job

Like if you need a detailed explanation step-by-step ❤️

𝗪𝗮𝗻𝘁 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 — 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 — 𝗗𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗳𝗿𝗼𝗺 𝗚𝗼𝗼𝗴𝗹𝗲?😍

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All The Best 🎊

10 Python Libraries Every AI Engineer Should Know

1. Hugging Face Transformers
A powerful library for using and fine-tuning pre-trained transformer models for NLP. Learn more: Hugging Face NLP Course

2. Ollama
A framework for running and managing open-source LLMs locally with ease. Learn video: Ollama Course

3. OpenAI Python SDK
The official toolkit for integrating OpenAI models into Python applications. Learn more: The official developer quickstart guide

4. Anthropic SDK
A client library for seamless interaction with Claude and other Anthropic models. Learn more: Anthropic Python SDK

5. LangChain
A framework for building LLM applications with modular and extensible components. Learn more: DeepLearning.AI

6. LlamaIndex
A toolkit for integrating custom data sources with LLMs for better retrieval. Learn more: Building Agentic RAG with LlamaIndex

7. SQLAlchemy
A Python SQL toolkit and ORM for efficient and maintainable database interactions. Learn more: SQLAlchemy Unified Tutorial

8. ChromaDB
An open-source vector database optimized for AI-powered search and retrieval. Learn more: Getting Started - Chroma Docs

9. Weaviate
A cloud-native vector search engine for efficient semantic search at scale. Learn more: 101T Work with: Text data

10. Weights & Biases
A platform for tracking, visualizing, and optimizing ML experiments.
Learn more: Effective MLOps: Model Development

#artificialintelligence

𝗧𝗖𝗦 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍

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𝟲 𝗕𝗲𝘀𝘁 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜😍

Power BI Isn’t Just a Tool—It’s a Career Game-Changer🚀

Whether you’re a student, a working professional, or switching careers, learning Power BI can set you apart in the competitive world of data analytics📊

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Your Analytics Journey Starts Now✅️

Exploratory Data Analysis ( EDA)

𝟱 𝗙𝗥𝗘𝗘 𝗜𝗕𝗠 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝗸𝘆𝗿𝗼𝗰𝗸𝗲𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲😍

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5 frequently Asked SQL Interview Questions with Answers in Data Engineering interviews:
𝐃𝐢𝐟𝐟𝐢𝐜𝐮𝐥𝐭𝐲 - 𝐌𝐞𝐝𝐢𝐮𝐦

⚫️Determine the Top 5 Products with the Highest Revenue in Each Category.
Schema: Products (ProductID, Name, CategoryID), Sales (SaleID, ProductID, Amount)

WITH ProductRevenue AS (
SELECT p.ProductID,
p.Name,
p.CategoryID,
SUM(s.Amount) AS TotalRevenue,
RANK() OVER (PARTITION BY p.CategoryID ORDER BY SUM(s.Amount) DESC) AS RevenueRank
FROM Products p
JOIN Sales s ON p.ProductID = s.ProductID
GROUP BY p.ProductID, p.Name, p.CategoryID
)
SELECT ProductID, Name, CategoryID, TotalRevenue
FROM ProductRevenue
WHERE RevenueRank <= 5;

⚫️ Identify Employees with Increasing Sales for Four Consecutive Quarters.
Schema: Sales (EmployeeID, SaleDate, Amount)

WITH QuarterlySales AS (
SELECT EmployeeID,
DATE_TRUNC('quarter', SaleDate) AS Quarter,
SUM(Amount) AS QuarterlyAmount
FROM Sales
GROUP BY EmployeeID, DATE_TRUNC('quarter', SaleDate)
),
SalesTrend AS (
SELECT EmployeeID,
Quarter,
QuarterlyAmount,
LAG(QuarterlyAmount, 1) OVER (PARTITION BY EmployeeID ORDER BY Quarter) AS PrevQuarter1,
LAG(QuarterlyAmount, 2) OVER (PARTITION BY EmployeeID ORDER BY Quarter) AS PrevQuarter2,
LAG(QuarterlyAmount, 3) OVER (PARTITION BY EmployeeID ORDER BY Quarter) AS PrevQuarter3
FROM QuarterlySales
)
SELECT EmployeeID, Quarter, QuarterlyAmount
FROM SalesTrend
WHERE QuarterlyAmount > PrevQuarter1 AND PrevQuarter1 > PrevQuarter2 AND PrevQuarter2 > PrevQuarter3;

⚫️ List Customers Who Made Purchases in Each of the Last Three Years.
Schema: Orders (OrderID, CustomerID, OrderDate)

WITH YearlyOrders AS (
SELECT CustomerID,
EXTRACT(YEAR FROM OrderDate) AS OrderYear
FROM Orders
GROUP BY CustomerID, EXTRACT(YEAR FROM OrderDate)
),
RecentYears AS (
SELECT DISTINCT OrderYear
FROM Orders
WHERE OrderDate >= CURRENT_DATE - INTERVAL '3 years'
),
CustomerYearlyOrders AS (
SELECT CustomerID,
COUNT(DISTINCT OrderYear) AS YearCount
FROM YearlyOrders
WHERE OrderYear IN (SELECT OrderYear FROM RecentYears)
GROUP BY CustomerID
)
SELECT CustomerID
FROM CustomerYearlyOrders
WHERE YearCount = 3;


⚫️ Find the Third Lowest Price for Each Product Category.
Schema: Products (ProductID, Name, CategoryID, Price)

WITH RankedPrices AS (
SELECT CategoryID,
Price,
DENSE_RANK() OVER (PARTITION BY CategoryID ORDER BY Price ASC) AS PriceRank
FROM Products
)
SELECT CategoryID, Price
FROM RankedPrices
WHERE PriceRank = 3;

⚫️ Identify Products with Total Sales Exceeding a Specified Threshold Over the Last 30 Days.
Schema: Sales (SaleID, ProductID, SaleDate, Amount)

WITH RecentSales AS (
SELECT ProductID,
SUM(Amount) AS TotalSales
FROM Sales
WHERE SaleDate >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY ProductID
)
SELECT ProductID, TotalSales
FROM RecentSales
WHERE TotalSales > 200;

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𝟰 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗯𝘆 𝗛𝗮𝗿𝘃𝗮𝗿𝗱 𝗮𝗻𝗱 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗔𝗜😍

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Important Topics to become a data scientist [Advanced Level]
👇👇

1. Mathematics

Linear Algebra
Analytic Geometry
Matrix
Vector Calculus
Optimization
Regression
Dimensionality Reduction
Density Estimation
Classification

2. Probability

Introduction to Probability
1D Random Variable
The function of One Random Variable
Joint Probability Distribution
Discrete Distribution
Normal Distribution

3. Statistics

Introduction to Statistics
Data Description
Random Samples
Sampling Distribution
Parameter Estimation
Hypotheses Testing
Regression

4. Programming

Python:

Python Basics
List
Set
Tuples
Dictionary
Function
NumPy
Pandas
Matplotlib/Seaborn

R Programming:

R Basics
Vector
List
Data Frame
Matrix
Array
Function
dplyr
ggplot2
Tidyr
Shiny

DataBase:
SQL
MongoDB

Data Structures

Web scraping

Linux

Git

5. Machine Learning

How Model Works
Basic Data Exploration
First ML Model
Model Validation
Underfitting & Overfitting
Random Forest
Handling Missing Values
Handling Categorical Variables
Pipelines
Cross-Validation(R)
XGBoost(Python|R)
Data Leakage

6. Deep Learning

Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
TensorFlow
Keras
PyTorch
A Single Neuron
Deep Neural Network
Stochastic Gradient Descent
Overfitting and Underfitting
Dropout Batch Normalization
Binary Classification

7. Feature Engineering

Baseline Model
Categorical Encodings
Feature Generation
Feature Selection

8. Natural Language Processing

Text Classification
Word Vectors

9. Data Visualization Tools

BI (Business Intelligence):
Tableau
Power BI
Qlik View
Qlik Sense

10. Deployment

Microsoft Azure
Heroku
Google Cloud Platform
Flask
Django

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𝗙𝗥𝗘𝗘 𝗚𝗼𝗼𝗴𝗹𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗮𝘁𝗵! 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗲𝗱 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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𝗕𝗲𝘀𝘁 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘😍

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