Machine learning powers so many things around us – from recommendation systems to self-driving cars!

But understanding the different types of algorithms can be tricky.

This is a quick and easy guide to the four main categories: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.

𝟏. 𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data.

𝐒𝐨𝐦𝐞 𝐜𝐨𝐦𝐦𝐨𝐧 𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:

➡️ Linear Regression – For predicting continuous values, like house prices.
➡️ Logistic Regression – For predicting categories, like spam or not spam.
➡️ Decision Trees – For making decisions in a step-by-step way.
➡️ K-Nearest Neighbors (KNN) – For finding similar data points.
➡️ Random Forests – A collection of decision trees for better accuracy.
➡️ Neural Networks – The foundation of deep learning, mimicking the human brain.

𝟐. 𝐔𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
With unsupervised learning, the model explores patterns in data that doesn’t have any labels. It finds hidden structures or groupings.

𝐒𝐨𝐦𝐞 𝐩𝐨𝐩𝐮𝐥𝐚𝐫 𝐮𝐧𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:

➡️ K-Means Clustering – For grouping data into clusters.
➡️ Hierarchical Clustering – For building a tree of clusters.
➡️ Principal Component Analysis (PCA) – For reducing data to its most important parts.
➡️ Autoencoders – For finding simpler representations of data.

𝟑. 𝐒𝐞𝐦𝐢-𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
This is a mix of supervised and unsupervised learning. It uses a small amount of labeled data with a large amount of unlabeled data to improve learning.

𝐂𝐨𝐦𝐦𝐨𝐧 𝐬𝐞𝐦𝐢-𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:

➡️ Label Propagation – For spreading labels through connected data points.
➡️ Semi-Supervised SVM – For combining labeled and unlabeled data.
➡️ Graph-Based Methods – For using graph structures to improve learning.

𝟒. 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠
In reinforcement learning, the model learns by trial and error. It interacts with its environment, receives feedback (rewards or penalties), and learns how to act to maximize rewards.

𝐏𝐨𝐩𝐮𝐥𝐚𝐫 𝐫𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐢𝐧𝐜𝐥𝐮𝐝𝐞:

➡️ Q-Learning – For learning the best actions over time.
➡️ Deep Q-Networks (DQN) – Combining Q-learning with deep learning.
➡️ Policy Gradient Methods – For learning policies directly.
➡️ Proximal Policy Optimization (PPO) – For stable and effective learning.

ENJOY LEARNING 👍👍

You don't need to buy a GPU for machine learning work!

There are other alternatives. Here are some:

1. Google Colab
2. Kaggle
3. Deepnote
4. AWS SageMaker
5. GCP Notebooks
6. Azure Notebooks
7. Cocalc
8. Binder
9. Saturncloud
10. Datablore
11. IBM Notebooks
12. Ola kutrim

Spend your time focusing on your problem.💪💪

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ENJOY LEARNING 👍👍

Hello everyone here are some tableau projects along with the datasets to work on

1. Sales Performance Dashboard:
   - Kaggle: [Sales dataset](https://www.kaggle.com/search?q=sales+dataset)
   - UCI Machine Learning Repository: [Sales Transactions Dataset](https://archive.ics.uci.edu/ml/datasets/sales_transactions_dataset_weekly)

2. Customer Segmentation Analysis:
   - Kaggle: [Customer dataset](https://www.kaggle.com/search?q=customer+dataset)
   - UCI Machine Learning Repository: [Online Retail Dataset](https://archive.ics.uci.edu/ml/datasets/Online+Retail)

3. Inventory Management Dashboard:
   - Kaggle: [Inventory dataset](https://www.kaggle.com/search?q=inventory+dataset)
   - Data.gov: [Inventory datasets](https://www.data.gov/)

4. Financial Analysis Dashboard:
   - Yahoo Finance API: [Yahoo Finance API](https://finance.yahoo.com/quote/GOOG/history?p=GOOG)
   - Quandl: [Financial datasets](https://www.quandl.com/)

5. Social Media Analytics Dashboard:
   - Twitter API: [Twitter API](https://developer.twitter.com/en/docs)
   - Facebook Graph API: [Facebook Graph API](https://developers.facebook.com/docs/graph-api/)

6. Website Analytics Dashboard:
   - Google Analytics API: [Google Analytics API](https://developers.google.com/analytics)
   - SimilarWeb API: [SimilarWeb API](https://www.similarweb.com/corp/developer/)

7. Supply Chain Analysis Dashboard:
   - Kaggle: [Supply chain dataset](https://www.kaggle.com/search?q=supply+chain+dataset)
   - Data.gov: [Supply chain datasets](https://www.data.gov/)

8. Healthcare Analytics Dashboard:
   - CDC Public Health Data: [CDC Public Health Data](https://www.cdc.gov/datastatistics/index.html)
   - HealthData.gov: [Healthcare datasets](https://healthdata.gov/)

9. Employee Performance Dashboard:
   - Kaggle: [Employee dataset](https://www.kaggle.com/search?q=employee+dataset)
   - Glassdoor API: [Glassdoor API](https://www.glassdoor.com/developer/index.htm)

10. Real-time Dashboard:
    - Real-time APIs: Various APIs provide real-time data, such as financial market APIs, weather APIs, etc.
    - Web scraping: Extract real-time data from websites using web scraping tools like BeautifulSoup or Scrapy.

All the best for your career ❤️

Remember: Tough times are opportunities to practice virtue.

Courage, justice, wisdom, self-control. They're forged in fire.

Important Machine Learning Algorithms 👇👇

- Linear Regression
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- k-Nearest Neighbors (kNN)
- Naive Bayes
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
- Neural Networks (Deep Learning)
- Gradient Boosting algorithms (e.g., XGBoost, LightGBM)

Like this post if you want me to explain each algorithm in detail

Share with credits: https://t.iss.one/datasciencefun

ENJOY LEARNING 👍👍

Hello everyone here are some tableau projects along with the datasets to work on

1. Sales Performance Dashboard:
   - Kaggle: [Sales dataset](https://www.kaggle.com/search?q=sales+dataset)
   - UCI Machine Learning Repository: [Sales Transactions Dataset](https://archive.ics.uci.edu/ml/datasets/sales_transactions_dataset_weekly)

2. Customer Segmentation Analysis:
   - Kaggle: [Customer dataset](https://www.kaggle.com/search?q=customer+dataset)
   - UCI Machine Learning Repository: [Online Retail Dataset](https://archive.ics.uci.edu/ml/datasets/Online+Retail)

3. Inventory Management Dashboard:
   - Kaggle: [Inventory dataset](https://www.kaggle.com/search?q=inventory+dataset)
   - Data.gov: [Inventory datasets](https://www.data.gov/)

4. Financial Analysis Dashboard:
   - Yahoo Finance API: [Yahoo Finance API](https://finance.yahoo.com/quote/GOOG/history?p=GOOG)
   - Quandl: [Financial datasets](https://www.quandl.com/)

5. Social Media Analytics Dashboard:
   - Twitter API: [Twitter API](https://developer.twitter.com/en/docs)
   - Facebook Graph API: [Facebook Graph API](https://developers.facebook.com/docs/graph-api/)

6. Website Analytics Dashboard:
   - Google Analytics API: [Google Analytics API](https://developers.google.com/analytics)
   - SimilarWeb API: [SimilarWeb API](https://www.similarweb.com/corp/developer/)

7. Supply Chain Analysis Dashboard:
   - Kaggle: [Supply chain dataset](https://www.kaggle.com/search?q=supply+chain+dataset)
   - Data.gov: [Supply chain datasets](https://www.data.gov/)

8. Healthcare Analytics Dashboard:
   - CDC Public Health Data: [CDC Public Health Data](https://www.cdc.gov/datastatistics/index.html)
   - HealthData.gov: [Healthcare datasets](https://healthdata.gov/)

9. Employee Performance Dashboard:
   - Kaggle: [Employee dataset](https://www.kaggle.com/search?q=employee+dataset)
   - Glassdoor API: [Glassdoor API](https://www.glassdoor.com/developer/index.htm)

10. Real-time Dashboard:
    - Real-time APIs: Various APIs provide real-time data, such as financial market APIs, weather APIs, etc.
    - Web scraping: Extract real-time data from websites using web scraping tools like BeautifulSoup or Scrapy.

Kaggle is not the only source for dataset.

Get dataset to practice your data science and analytics skills from these 10+ other sources:

UNData:
This is a s statistical database of all United Nations data.
https://data.un.org/

Datasimplifier:
https://datasimplifier.com/data-analytics-portfolio/

Tableau Public Data Sets:
https://lnkd.in/dyM6k5CR

US Census Bureau:
https://data.census.gov/

Amazon AWS DataSet:
This is a repository of large datasets relating to many interralated areas.
https://lnkd.in/dPB33xsk

UC Irvine Machine Learning Repository:
https://lnkd.in/d3czdgJ2

USA Open Data:
https://data.gov/

Wikipedia Data Set:
https://t.co/JxzFu8EvIv

Worldbank dataset:
https://lnkd.in/d6qwV-NW

World Health Organization:
https://lnkd.in/dAFJcqFj

Awesome Public Data Sources:
https://t.co/u12vxk8zU3

Google Dataset:
Contains a wide array of information, including articles, theses, books, abstracts, white papers, and court opinions.
https://lnkd.in/d9Zadmfc

Country Codes List:
https://lnkd.in/dGJX9Z5x

FiveThirtyEight:
https://lnkd.in/d8mU8ZHN

BuzzFeed News:
https://lnkd.in/d9iSbSBB

Kaggle:
https://lnkd.in/dVWutrGN

Socrata:
https://lnkd.in/d5nvMnxt

GitHub:
https://lnkd.in/dfuUw5RS

Google dataset Search:
https://lnkd.in/d8YKUbcP

Data.gov:
https://www.data.gov/

Datahub:
https://lnkd.in/dqWd-QuB


Which of these sources have you used to find datasets for your projects?

Complete Roadmap to learn Machine Learning and Artificial Intelligence
👇👇

Week 1-2: Introduction to Machine Learning
- Learn the basics of Python programming language (if you are not already familiar with it)
- Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning
- Study linear algebra and calculus basics
- Complete online courses like Andrew Ng's Machine Learning course on Coursera

Week 3-4: Deep Learning Fundamentals
- Dive into neural networks and deep learning
- Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
- Implement deep learning models using frameworks like TensorFlow or PyTorch
- Complete online courses like Deep Learning Specialization on Coursera

Week 5-6: Natural Language Processing (NLP) and Computer Vision
- Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis
- Dive into computer vision concepts like image classification, object detection, and image segmentation
- Work on projects involving NLP and Computer Vision applications

Week 7-8: Reinforcement Learning and AI Applications
- Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks
- Explore AI applications in fields like healthcare, finance, and autonomous vehicles
- Work on a final project that combines different aspects of Machine Learning and AI

Additional Tips:
- Practice coding regularly to strengthen your programming skills
- Join online communities like Kaggle or GitHub to collaborate with other learners
- Read research papers and articles to stay updated on the latest advancements in the field

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 ML & AI but mastering it is very difficult as AI keeps evolving every day.

Best Resources to learn ML & AI 👇

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ENJOY LEARNING👍👍

Here are some SQL project ideas tailored for data analysis:

🔟 SQL Project Ideas for Data Analysts

1. Sales Database Analysis: Create a database to track sales transactions. Write SQL queries to analyze sales performance by product, region, and time period.

2. Customer Churn Analysis: Build a database with customer data and track churn rates. Use SQL to identify factors contributing to churn and segment customers.

3. E-commerce Order Tracking: Design a database for an e-commerce platform. Write queries to analyze order trends, average order value, and customer purchase history.

4. Employee Performance Metrics: Create a database for employee records and performance reviews. Analyze employee performance trends and identify high performers using SQL.

5. Inventory Management System: Set up a database to track inventory levels. Write SQL queries to monitor stock levels, identify slow-moving items, and generate restock reports.

6. Healthcare Patient Analysis: Build a database to manage patient records and treatments. Use SQL to analyze treatment outcomes, readmission rates, and patient demographics.

7. Social Media Engagement Analysis: Create a database to track user interactions on a social media platform. Write queries to analyze engagement metrics like likes, shares, and comments.

8. Financial Transaction Analysis: Set up a database for financial transactions. Use SQL to identify spending patterns, categorize expenses, and generate monthly financial reports.

9. Website Traffic Analysis: Build a database to track website visitors. Write queries to analyze traffic sources, user behavior, and page performance.

10. Survey Results Analysis: Create a database to store survey responses. Use SQL to analyze responses, identify trends, and visualize findings based on demographic data.

Tackle Real World Data Challenges with These SQL Key Queries...



Scenario 1: Calculating Average

Question:
You have a table Employees with columns EmployeeID, Department, and Salary. Write an SQL query to find the average salary for each department.

Answer:
Assuming the table Employees with columns EmployeeID, Department, and Salary
SELECT Department,
AVG(Salary) AS AverageSalary
FROM Employees
GROUP BY Department;


Scenario 2: Finding Top Performers

Question:
You have a table Sales with columns SalesPersonID, SaleAmount, and SaleDate. Write an SQL query to find the top 3 salespeople with the highest total sales.

Answer:
Assuming the table Sales with columns SalesPersonID, SaleAmount, and SaleDate
SELECT SalesPersonID,
SUM(SaleAmount) AS TotalSales
FROM Sales
GROUP BY SalesPersonID
ORDER BY TotalSales DESC
LIMIT 3;

Scenario 3: Date Range Filtering

Question:
You have a table Orders with columns OrderID, OrderDate, and Amount. Write an SQL query to find the total amount of orders placed in the last 30 days.

Answer:
Assuming the table Orders with columns OrderID, OrderDate, and Amount
SELECT SUM(Amount) AS TotalAmount
FROM Orders
WHERE OrderDate >= CURDATE() - INTERVAL 30 DAY;

Many people ask this common question “Can I get a job with just SQL and Excel?” or “Can I get a job with just Power BI and Python?”.

The answer to all of those questions is yes.

There are jobs that use only SQL, Tableau, Power BI, Excel, Python, or R or some combination of those.

However, the combination of tools you learn impacts the total number of jobs you are qualified for.

For example, let’s say with just SQL and Excel you are qualified for 10 jobs, but if you add Tableau to that, you are qualified for 50 jobs.

If you have a success rate of landing a job you’re qualified for of 4%, having 5 times as many jobs to go for greatly improves your odds of landing a job.

Does this mean you should go out there and learn every single skill any data analyst job requires?

NO!

It’s about finding the core tools that many jobs want.

And, in my opinion, those tools are SQL, Excel, and a visualization tool.

With these three tools, you are qualified for the majority of entry level data jobs and many higher level jobs.

So, you can land a job with whatever tools you’re comfortable with.

But if you have the three tools above in your toolbelt, you will have many more jobs to apply for and greatly improve your chances of snagging one.

4 Python practical projects to do for freshers in data analytics

🧵⬇️

1️⃣ Exploratory Data Analysis (EDA) on a Public Dataset

Use a dataset from Kaggle or data.gov

Clean and preprocess the data

Perform statistical analysis and visualization

Draw insights and present findings

2️⃣ Stock Market Analysis Tool

Fetch real-time stock data using an API (e.g., yfinance)

Implement technical indicators (e.g., moving averages, RSI)

Create visualizations of stock performance

Build a simple prediction model

3️⃣ Social Media Sentiment Analysis

Collect tweets or Reddit posts using APIs

Preprocess text data

Perform sentiment analysis

Visualize sentiment trends over time

4️⃣ Customer Churn Prediction

Use a telecom or e-commerce dataset

Perform feature engineering

Build and compare multiple machine learning models

Evaluate model performance and interpret results

Hope it helps :)

SQL Projects with Datasets 👇

📌E-commerce Sales Analysis:
Dataset: Online retail dataset from the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Online+Retail)

📌Social Media Analytics:
Twitter API or Twitter datasets available on platforms like Kaggle (https://www.kaggle.com/datasets?search=twitter)

📌Healthcare Data Management:
MIMIC-III (Medical Information Mart for Intensive Care III) dataset (https://mimic.mit.edu/docs/iii/)

📌Retail Inventory Management:
Sample retail sales dataset available on platforms like Kaggle (https://www.kaggle.com/datasets?search=retail)

📌Financial Portfolio Analysis:
Yahoo Finance API or finance datasets available on platforms like Kaggle (https://www.kaggle.com/datasets?search=finance)

📌Real Estate Market Analysis:
Zillow dataset (https://www.zillow.com/research/data/) or real estate datasets available on platforms like Kaggle (https://www.kaggle.com/datasets?search=real+estate)

📚 9 must-have Python developer tools.

1. PyCharm IDE

2. Jupyter notebook

3. Keras

4. Pip Package

5. Python Anywhere

6. Scikit-Learn

7. Sphinx

8. Selenium

9. Sublime Text