Generative AI Mindmap

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

Whether you’re a student, job seeker, or just hungry to upskill — these 5 beginner-friendly courses are your golden ticket. 🎟️

Just career-boosting knowledge and certificates that make your resume pop📄

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/42vL6br

All The Best 🎊

Important Machine Learning Algorithms 👆

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

SQL seems tough, right? 😩

These 5 FREE SQL resources will take you from beginner to advanced without boring theory dumps or confusion.📊

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/3GtntaC

Master it with ease. 💡

Python Basics for Data Science

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

Dreaming of a career in data but don’t have a degree? You don’t need one. What you do need are the right skills🔗

These 4 free/affordable certifications can get you there. 💻✨

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/4ioaJ2p

Let’s get you certified and hired!✅️

Hi guys 👋

Since many of you were asking me to send Free Job Interview Resources

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ENJOY LEARNING 👍👍

​​Python Learning Courses provided by Microsoft 📚

Recently, I found out that Microsoft provides quality online courses related to Python on Microsoft Learn.
Microsoft Learn is a free online platform that provides access to a set of training courses for the acquisition and improvement of digital skills. Each course is designed as a module, each module contains different lessons and exercises. Below are the modules related to Python learning.

🟢Beginner
1. What is Python?
2. Introduction to Python
3. Take your first steps with Python
4. Set up your Python beginner development environment with Visual Studio Code
5. Branch code execution with the if...elif...else statement in Python
6. Manipulate and format string data for display in Python
7. Perform mathematical operations on numeric data in Python
8. Iterate through code blocks by using the while statement
9. Import standard library modules to add features to Python programs
10. Create reusable functionality with functions in Python
11. Manage a sequence of data by using Python lists
12. Write basic Python in Notebooks
13. Count the number of Moon rocks by type using Python
14. Code control statements in Python
15. Introduction to Python for space exploration
16. Install coding tools for Python development
17. Discover the role of Python in space exploration
18. Crack the code and reveal a secret with Python and Visual Studio Code
19. Introduction to object-oriented programming with Python
20. Use Python basics to solve mysteries and find answers
21. Predict meteor showers by using Python and Visual Studio Code
22. Plan a Moon mission by using Python pandas

🟠Intermediate
1. Create machine learning models
2. Explore and analyze data with Python
3. Build an AI web app by using Python and Flask
4. Get started with Django
5. Architect full-stack applications and automate deployments with GitHub

#materials

𝗗𝗿𝗲𝗮𝗺 𝗝𝗼𝗯 𝗮𝘁 𝗚𝗼𝗼𝗴𝗹𝗲? 𝗧𝗵𝗲𝘀𝗲 𝟰 𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗪𝗶𝗹𝗹 𝗛𝗲𝗹𝗽 𝗬𝗼𝘂 𝗚𝗲𝘁 𝗧𝗵𝗲𝗿𝗲😍

Dreaming of working at Google but not sure where to even begin?📍

Start with these FREE insider resources—from building a resume that stands out to mastering the Google interview process. 🎯

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/441GCKF

Because if someone else can do it, so can you. Why not you? Why not now?✅️

🚀 𝗛𝗼𝘄 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗧𝗵𝗮𝘁 𝗧𝗿𝘂𝗹𝘆 𝗦𝘁𝗮𝗻𝗱𝘀 𝗢𝘂𝘁

In today’s competitive landscape, a strong resume alone won't get you far. If you're aiming for 𝘆𝗼𝘂𝗿 𝗱𝗿𝗲𝗮𝗺 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗿𝗼𝗹𝗲, you need a portfolio that speaks volumes—one that highlights your skills, thinking process, and real-world impact.

A great portfolio isn’t just a collection of projects. It’s your story as a data scientist—and here’s how to make it unforgettable:

🔹 𝗪𝗵𝗮𝘁 𝗠𝗮𝗸𝗲𝘀 𝗮𝗻 𝗘𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼?

✅ Quality Over Quantity – A few impactful projects are far better than a dozen generic ones.

✅ Tell a Story – Clearly explain the problem, your approach, and key insights. Keep it engaging.

✅ Show Range – Demonstrate a variety of skills—data cleaning, visualization, analytics, modeling.

✅ Make It Relevant – Choose projects with real-world business value, not just toy Kaggle datasets.

🔥 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗜𝗱𝗲𝗮𝘀 𝗧𝗵𝗮𝘁 𝗥𝗲𝗰𝗿𝘂𝗶𝘁𝗲𝗿𝘀 𝗡𝗼𝘁𝗶𝗰𝗲

1️⃣ Customer Churn Prediction – Help businesses retain customers through insights.

2️⃣ Social Media Sentiment Analysis – Extract opinions from real-time data like tweets or reviews.

3️⃣ Supply Chain Optimization – Solve efficiency problems using operational data.

4️⃣ E-commerce Recommender System – Personalize shopping experiences with smart suggestions.

5️⃣ Interactive Dashboards – Use Power BI or Tableau to tell compelling visual stories.

📌 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗳𝗼𝗿 𝗮 𝗞𝗶𝗹𝗹𝗲𝗿 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼

💡 Host on GitHub – Keep your code clean, well-structured, and documented.

💡 Write About It – Use Medium or your own site to explain your projects and decisions.

💡 Deploy Your Work – Use tools like Streamlit, Flask, or FastAPI to make your projects interactive.

💡 Open Source Contributions – It’s a great way to gain credibility and connect with others.

A great data science portfolio is not just about code—it's about solving real problems with data.

Free Data Science Resources: https://t.me/datalemur

All the best 👍👍

Accenture Data Scientist Interview Questions!

1st round-

Technical Round

- 2 SQl questions based on playing around views and table, which could be solved by both subqueries and window functions.

- 2 Pandas questions , testing your knowledge on filtering , concatenation , joins and merge.

- 3-4 Machine Learning questions completely based on my Projects, starting from
Explaining the problem statements and then discussing the roadblocks of those projects and some cross questions.

2nd round-

- Couple of python questions agains on pandas and numpy and some hypothetical data.

- Machine Learning projects explanations and cross questions.

- Case Study and a quiz question.

3rd and Final round.

HR interview

Simple Scenerio Based Questions.

Data Science Resources
👇👇
https://t.me/datasciencefun

Like if you need similar content 😄👍

𝗣𝗼𝘄𝗲𝗿𝗕𝗜 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗿𝗼𝗺 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁😍

✅ Beginner-friendly
✅ Straight from Microsoft
✅ And yes… a badge for that resume flex

Perfect for beginners, job seekers, & Working Professionals

𝐋𝐢𝐧𝐤 👇:-

https://pdlink.in/4iq8QlM

Enroll for FREE & Get Certified 🎓

Essential statistics topics for data science

1. Descriptive statistics: Measures of central tendency, measures of dispersion, and graphical representations of data.

2. Inferential statistics: Hypothesis testing, confidence intervals, and regression analysis.

3. Probability theory: Concepts of probability, random variables, and probability distributions.

4. Sampling techniques: Simple random sampling, stratified sampling, and cluster sampling.

5. Statistical modeling: Linear regression, logistic regression, and time series analysis.

6. Machine learning algorithms: Supervised learning, unsupervised learning, and reinforcement learning.

7. Bayesian statistics: Bayesian inference, Bayesian networks, and Markov chain Monte Carlo methods.

8. Data visualization: Techniques for visualizing data and communicating insights effectively.

9. Experimental design: Designing experiments, analyzing experimental data, and interpreting results.

10. Big data analytics: Handling large volumes of data using tools like Hadoop, Spark, and SQL.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.me/datasciencefun

Like if you need similar content 😄👍

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 👍👍

𝗧𝗼𝗽 𝟰 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗧𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗦𝗤𝗟 𝗙𝗼𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 😍

These FREE resources are all you need to go from beginner to confident analyst! 💻📊

✅ Hands-on projects
✅ Beginner to advanced lessons
✅ Resume-worthy skills

𝗟𝗶𝗻𝗸:-👇

https://pdlink.in/4jkQaW1

Learn today, level up tomorrow. Let’s go!✅

We have the Key to unlock AI-Powered Data Skills!

We have got some news for College grads & pros:

Level up with PW Skills' Data Analytics & Data Science with Gen AI course!

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✅ Professional instructors
✅ Flexible learning
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40 ML Questions you must know with answers ✅

𝗟𝗲𝗮𝗿𝗻 𝗡𝗲𝘄 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 & 𝗘𝗮𝗿𝗻 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗲𝘀!😍

Looking to upgrade your skills in Data Science, Programming, AI, Business, and more? 📚💡

This platform offers FREE online courses that help you gain job-ready expertise and earn certificates to showcase your achievements! ✅

𝐋𝐢𝐧𝐤👇:-

https://pdlink.in/41Nulbr

Don’t miss out! Start exploring today📌

𝗛𝗼𝘄 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮 𝗝𝗼𝗯-𝗥𝗲𝗮𝗱𝘆 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗳𝗿𝗼𝗺 𝗦𝗰𝗿𝗮𝘁𝗰𝗵 (𝗘𝘃𝗲𝗻 𝗶𝗳 𝗬𝗼𝘂’𝗿𝗲 𝗮 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿!) 📊

Wanna break into data science but feel overwhelmed by too many courses, buzzwords, and conflicting advice? You’re not alone.

Here’s the truth: You don’t need a PhD or 10 certifications. You just need the right skills in the right order.

Let me show you a proven 5-step roadmap that actually works for landing data science roles (even entry-level) 👇

🔹 Step 1: Learn the Core Tools (This is Your Foundation)

Focus on 3 key tools first—don’t overcomplicate:

✅ Python – NumPy, Pandas, Matplotlib, Seaborn
✅ SQL – Joins, Aggregations, Window Functions
✅ Excel – VLOOKUP, Pivot Tables, Data Cleaning

🔹 Step 2: Master Data Cleaning & EDA (Your Real-World Skill)

Real data is messy. Learn how to:

✅ Handle missing data, outliers, and duplicates
✅ Visualize trends using Matplotlib/Seaborn
✅ Use groupby(), merge(), and pivot_table()

🔹 Step 3: Learn ML Basics (No Fancy Math Needed)

Stick to core algorithms first:

✅ Linear & Logistic Regression
✅ Decision Trees & Random Forest
✅ KMeans Clustering + Model Evaluation Metrics

🔹 Step 4: Build Projects That Prove Your Skills

One strong project > 5 courses. Create:

✅ Sales Forecasting using Time Series
✅ Movie Recommendation System
✅ HR Analytics Dashboard using Python + Excel
📍 Upload them on GitHub. Add visuals, write a good README, and share on LinkedIn.

🔹 Step 5: Prep for the Job Hunt (Your Personal Brand Matters)

✅ Create a strong LinkedIn profile with keywords like “Aspiring Data Scientist | Python | SQL | ML”
✅ Add GitHub link + Highlight your Projects
✅ Follow Data Science mentors, engage with content, and network for referrals

🎯 No shortcuts. Just consistent baby steps.

Every pro data scientist once started as a beginner. Stay curious, stay consistent.

Free Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍

Best practices for writing SQL queries:

Join for more: https://t.me/learndataanalysis

1- Write SQL keywords in capital letters.

2- Use table aliases with columns when you are joining multiple tables.

3- Never use select *, always mention list of columns in select clause.

4- Add useful comments wherever you write complex logic. Avoid too many comments.

5- Use joins instead of subqueries when possible for better performance.

6- Create CTEs instead of multiple sub queries , it will make your query easy to read.

7- Join tables using JOIN keywords instead of writing join condition in where clause for better readability.

8- Never use order by in sub queries , It will unnecessary increase runtime.

9- If you know there are no duplicates in 2 tables, use UNION ALL instead of UNION for better performance.

SQL Basics: https://t.me/sqlanalyst/105

𝗪𝗲𝗯 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

Want to master web development? These free certification courses will help you build real-world full-stack skills:

✅ Web Design 🎨
✅ JavaScript ⚡ 
✅ Front-End Libraries 📚
✅ Back-End & APIs 🌐 
✅ Databases 💾 

💡 Start learning today and build your career for FREE! 🚀

𝐋𝐢𝐧𝐤 👇:-

https://pdlink.in/4bqbQwB

Enroll for FREE & Get Certified 🎓

MACHINE LEARNING

Want to make a transition to a career in data?

Here is a 7-step plan for each data role

Data Scientist

Statistics and Math: Advanced statistics, linear algebra, calculus.
Machine Learning: Supervised and unsupervised learning algorithms.
xData Wrangling: Cleaning and transforming datasets.
Big Data: Hadoop, Spark, SQL/NoSQL databases.
Data Visualization: Matplotlib, Seaborn, D3.js.
Domain Knowledge: Industry-specific data science applications.

Data Analyst

Data Visualization: Tableau, Power BI, Excel for visualizations.
SQL: Querying and managing databases.
Statistics: Basic statistical analysis and probability.
Excel: Data manipulation and analysis.
Python/R: Programming for data analysis.
Data Cleaning: Techniques for data preprocessing.
Business Acumen: Understanding business context for insights.

Data Engineer

SQL/NoSQL Databases: MySQL, PostgreSQL, MongoDB, Cassandra.
ETL Tools: Apache NiFi, Talend, Informatica.
Big Data: Hadoop, Spark, Kafka.
Programming: Python, Java, Scala.
Data Warehousing: Redshift, BigQuery, Snowflake.
Cloud Platforms: AWS, GCP, Azure.
Data Modeling: Designing and implementing data models.

#data

𝟱 𝗙𝗥𝗘𝗘 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

Explore AI, machine learning, and cloud computing — straight from Google and FREE

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2. 💻Google AI for JavaScript Developers
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Understanding Popular ML Algorithms:

1️⃣ Linear Regression: Think of it as drawing a straight line through data points to predict future outcomes.

2️⃣ Logistic Regression: Like a yes/no machine - it predicts the likelihood of something happening or not.

3️⃣ Decision Trees: Imagine making decisions by answering yes/no questions, leading to a conclusion.

4️⃣ Random Forest: It's like a group of decision trees working together, making more accurate predictions.

5️⃣ Support Vector Machines (SVM): Visualize drawing lines to separate different types of things, like cats and dogs.

6️⃣ K-Nearest Neighbors (KNN): Friends sticking together - if most of your friends like something, chances are you'll like it too!

7️⃣ Neural Networks: Inspired by the brain, they learn patterns from examples - perfect for recognizing faces or understanding speech.

8️⃣ K-Means Clustering: Imagine sorting your socks by color without knowing how many colors there are - it groups similar things.

9️⃣ Principal Component Analysis (PCA): Simplifies complex data by focusing on what's important, like summarizing a long story with just a few key points.

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🔥 Data Science Roadmap 2025

Step 1: 🐍 Python Basics
Step 2: 📊 Data Analysis (Pandas, NumPy)
Step 3: 📈 Data Visualization (Matplotlib, Seaborn)
Step 4: 🤖 Machine Learning (Scikit-learn)
Step 5: � Deep Learning (TensorFlow/PyTorch)
Step 6: 🗃️ SQL & Big Data (Spark)
Step 7: 🚀 Deploy Models (Flask, FastAPI)
Step 8: 📢 Showcase Projects
Step 9: 💼 Land a Job!

🔓 Pro Tip: Compete on Kaggle

#datascience

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Important questions to ace your machine learning interview with an approach to answer:

1. Machine Learning Project Lifecycle:
   - Define the problem
   - Gather and preprocess data
   - Choose a model and train it
   - Evaluate model performance
   - Tune and optimize the model
   - Deploy and maintain the model

2. Supervised vs Unsupervised Learning:
   - Supervised Learning: Uses labeled data for training (e.g., predicting house prices from features).
   - Unsupervised Learning: Uses unlabeled data to find patterns or groupings (e.g., clustering customer segments).

3. Evaluation Metrics for Regression:
   - Mean Absolute Error (MAE)
   - Mean Squared Error (MSE)
   - Root Mean Squared Error (RMSE)
   - R-squared (coefficient of determination)

4. Overfitting and Prevention:
   - Overfitting: Model learns the noise instead of the underlying pattern.
   - Prevention: Use simpler models, cross-validation, regularization.

5. Bias-Variance Tradeoff:
   - Balancing error due to bias (underfitting) and variance (overfitting) to find an optimal model complexity.

6. Cross-Validation:
   - Technique to assess model performance by splitting data into multiple subsets for training and validation.

7. Feature Selection Techniques:
   - Filter methods (e.g., correlation analysis)
   - Wrapper methods (e.g., recursive feature elimination)
   - Embedded methods (e.g., Lasso regularization)

8. Assumptions of Linear Regression:
   - Linearity
   - Independence of errors
   - Homoscedasticity (constant variance)
   - No multicollinearity

9. Regularization in Linear Models:
   - Adds a penalty term to the loss function to prevent overfitting by shrinking coefficients.

10. Classification vs Regression:
    - Classification: Predicts a categorical outcome (e.g., class labels).
    - Regression: Predicts a continuous numerical outcome (e.g., house price).

11. Dimensionality Reduction Algorithms:
    - Principal Component Analysis (PCA)
    - t-Distributed Stochastic Neighbor Embedding (t-SNE)

12. Decision Tree:
    - Tree-like model where internal nodes represent features, branches represent decisions, and leaf nodes represent outcomes.

13. Ensemble Methods:
    - Combine predictions from multiple models to improve accuracy (e.g., Random Forest, Gradient Boosting).

14. Handling Missing or Corrupted Data:
    - Imputation (e.g., mean substitution)
    - Removing rows or columns with missing data
    - Using algorithms robust to missing values

15. Kernels in Support Vector Machines (SVM):
    - Linear kernel
    - Polynomial kernel
    - Radial Basis Function (RBF) kernel

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Many data scientists don't know how to push ML models to production. Here's the recipe 👇

𝗞𝗲𝘆 𝗜𝗻𝗴𝗿𝗲𝗱𝗶𝗲𝗻𝘁𝘀

🔹 𝗧𝗿𝗮𝗶𝗻 / 𝗧𝗲𝘀𝘁 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 - Ensure Test is representative of Online data
🔹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 - Generate features in real-time
🔹 𝗠𝗼𝗱𝗲𝗹 𝗢𝗯𝗷𝗲𝗰𝘁 - Trained SkLearn or Tensorflow Model
🔹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗼𝗱𝗲 𝗥𝗲𝗽𝗼 - Save model project code to Github
🔹 𝗔𝗣𝗜 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 - Use FastAPI or Flask to build a model API
🔹 𝗗𝗼𝗰𝗸𝗲𝗿 - Containerize the ML model API
🔹 𝗥𝗲𝗺𝗼𝘁𝗲 𝗦𝗲𝗿𝘃𝗲𝗿 - Choose a cloud service; e.g. AWS sagemaker
🔹 𝗨𝗻𝗶𝘁 𝗧𝗲𝘀𝘁𝘀 - Test inputs & outputs of functions and APIs
🔹 𝗠𝗼𝗱𝗲𝗹 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 - Evidently AI, a simple, open-source for ML monitoring

𝗣𝗿𝗼𝗰𝗲𝗱𝘂𝗿𝗲

𝗦𝘁𝗲𝗽 𝟭 - 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴

Don't push a model with 90% accuracy on train set. Do it based on the test set - if and only if, the test set is representative of the online data. Use SkLearn pipeline to chain a series of model preprocessing functions like null handling.

𝗦𝘁𝗲𝗽 𝟮 - 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁

Train your model with frameworks like Sklearn or Tensorflow. Push the model code including preprocessing, training and validation scripts to Github for reproducibility.

𝗦𝘁𝗲𝗽 𝟯 - 𝗔𝗣𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 & 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻

Your model needs a "/predict" endpoint, which receives a JSON object in the request input and generates a JSON object with the model score in the response output. You can use frameworks like FastAPI or Flask. Containzerize this API so that it's agnostic to server environment

𝗦𝘁𝗲𝗽 𝟰 - 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁

Write tests to validate inputs & outputs of API functions to prevent errors. Push the code to remote services like AWS Sagemaker.

𝗦𝘁𝗲𝗽 𝟱 - 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴

Set up monitoring tools like Evidently AI, or use a built-in one within AWS Sagemaker. I use such tools to track performance metrics and data drifts on online data.

Logistic regression fits a logistic model to data and makes predictions about the probability of an event (between 0 and 1).

Naive Bayes uses Bayes Theorem to model the conditional relationship of each attribute to the class variable.

The k-Nearest Neighbor (kNN) method makes predictions by locating similar cases to a given data instance (using a similarity function) and returning the average or majority of the most similar data instances. The kNN algorithm can be used for classification or regression.

Classification and Regression Trees (CART) are constructed from a dataset by making splits that best separate the data for the classes or predictions being made. The CART algorithm can be used for classification or regression.

Support Vector Machines (SVM) are a method that uses points in a transformed problem space that best separate classes into two groups. Classification for multiple classes is supported by a one-vs-all method. SVM also supports regression by modeling the function with a minimum amount of allowable error.

𝗝𝗣 𝗠𝗼𝗿𝗴𝗮𝗻 𝗙𝗥𝗘𝗘 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗻𝘀𝗵𝗶𝗽 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀😍

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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.

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Data Science interviews can be daunting, but with the right approach, you can ace them! If you're feeling overwhelmed, here's a roadmap to guide you through the process and help you succeed:

🔍 𝟭. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀:
Master fundamental concepts like statistics, linear algebra, and probability. These are crucial for tackling both theoretical and practical questions.

💻 𝟮. 𝗪𝗼𝗿𝗸 𝗼𝗻 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀:
Build a strong portfolio by solving real-world problems. Kaggle competitions, open datasets, and personal projects are great ways to gain hands-on experience.

🧠 𝟯. 𝗦𝗵𝗮𝗿𝗽𝗲𝗻 𝗬𝗼𝘂𝗿 𝗖𝗼𝗱𝗶𝗻𝗴 𝗦𝗸𝗶𝗹𝗹𝘀:
Coding is key in Data Science! Practice on platforms like LeetCode, HackerRank, or Codewars to boost your problem-solving ability and efficiency. Be comfortable with Python, SQL, and essential libraries.

📊 𝟰. 𝗠𝗮𝘀𝘁𝗲𝗿 𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴 & 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴:
A significant portion of Data Science work revolves around cleaning and preparing data. Make sure you're comfortable with handling missing data, outliers, and feature engineering.

📚 𝟱. 𝗦𝘁𝘂𝗱𝘆 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 & 𝗠𝗼𝗱𝗲𝗹𝘀:
From decision trees to neural networks, ensure you understand how different models work and when to apply them. Know their strengths, weaknesses, and the mathematical principles behind them.

💬 𝟲. 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗦𝗸𝗶𝗹𝗹𝘀:
Being able to explain complex concepts in a simple way is essential, especially when communicating with non-technical stakeholders. Practice explaining your findings and solutions clearly.

🔄 𝟳. 𝗠𝗼𝗰𝗸 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 & 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸:
Practice mock interviews with peers or mentors. Constructive feedback will help you identify areas of improvement and build confidence.

📈 𝟴. 𝗞𝗲𝗲𝗽 𝗨𝗽 𝗪𝗶𝘁𝗵 𝗧𝗿𝗲𝗻𝗱𝘀:
Data Science is a fast-evolving field! Stay updated on the latest techniques, tools, and industry trends to remain competitive.

👉 𝗣𝗿𝗼 𝗧𝗶𝗽: Be persistent! Rejections are part of the journey, but every experience teaches you something new.

𝟯 𝗙𝗿𝗲𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝘃𝗲𝗹 𝗨𝗽 𝗬𝗼𝘂𝗿 𝗧𝗲𝗰𝗵 𝗦𝗸𝗶𝗹𝗹𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍

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𝗣-𝗩𝗮𝗹𝘂𝗲𝘀 𝗳𝗼𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱

𝗪𝗵𝗲𝗻 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗿𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹, 𝗻𝗼𝘁 𝗲𝘃𝗲𝗿𝘆 𝘃𝗮𝗿𝗶𝗮𝗯𝗹𝗲 𝗶𝘀 𝗰𝗿𝗲𝗮𝘁𝗲𝗱 𝗲𝗾𝘂𝗮𝗹.

Some variables will genuinely impact your predictions, while others are just background noise.

𝗧𝗵𝗲 𝗽-𝘃𝗮𝗹𝘂𝗲 𝗵𝗲𝗹𝗽𝘀 𝘆𝗼𝘂 𝗳𝗶𝗴𝘂𝗿𝗲 𝗼𝘂𝘁 𝘄𝗵𝗶𝗰𝗵 𝗶𝘀 𝘄𝗵𝗶𝗰𝗵.

𝗪𝗵𝗮𝘁 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝗶𝘀 𝗮 𝗣-𝗩𝗮𝗹𝘂𝗲?

𝗔 𝗽-𝘃𝗮𝗹𝘂𝗲 𝗮𝗻𝘀𝘄𝗲𝗿𝘀 𝗼𝗻𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻:
➔ If this variable had no real effect, what’s the probability that we’d still observe results this extreme just by chance?

• 𝗟𝗼𝘄 𝗣-𝗩𝗮𝗹𝘂𝗲 (𝘂𝘀𝘂𝗮𝗹𝗹𝘆 < 0.05): Strong evidence that the variable is important.
• 𝗛𝗶𝗴𝗵 𝗣-𝗩𝗮𝗹𝘂𝗲 (> 0.05): The variable’s relationship with the output could easily be random.

𝗛𝗼𝘄 𝗣-𝗩𝗮𝗹𝘂𝗲𝘀 𝗚𝘂𝗶𝗱𝗲 𝗬𝗼𝘂𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹

𝗜𝗺𝗮𝗴𝗶𝗻𝗲 𝘆𝗼𝘂’𝗿𝗲 𝗮 𝘀𝗰𝘂𝗹𝗽𝘁𝗼𝗿.
You start with a messy block of stone (all your features).
P-values are your chisel.
𝗥𝗲𝗺𝗼𝘃𝗲 the features with high p-values (not useful).
𝗞𝗲𝗲𝗽 the features with low p-values (important).

This results in a leaner, smarter model that doesn’t just memorize noise but learns real patterns.

𝗪𝗵𝘆 𝗣-𝗩𝗮𝗹𝘂𝗲𝘀 𝗠𝗮𝘁𝘁𝗲𝗿

𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗽-𝘃𝗮𝗹𝘂𝗲𝘀, 𝗺𝗼𝗱𝗲𝗹 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝗴𝘂𝗲𝘀𝘀𝘄𝗼𝗿𝗸.

✅ 𝗟𝗼𝘄 𝗣-𝗩𝗮𝗹𝘂𝗲 ➔ Likely genuine effect.
❌ 𝗛𝗶𝗴𝗵 𝗣-𝗩𝗮𝗹𝘂𝗲 ➔ Likely coincidence.

𝗜𝗳 𝘆𝗼𝘂 𝗶𝗴𝗻𝗼𝗿𝗲 𝗶𝘁, 𝘆𝗼𝘂 𝗿𝗶𝘀𝗸:
• Overfitting your model with junk features
• Lowering your model’s accuracy and interpretability
• Making wrong business decisions based on faulty insights

𝗧𝗵𝗲 𝟬.𝟬𝟱 𝗧𝗵𝗿𝗲𝘀𝗵𝗼𝗹𝗱: 𝗡𝗼𝘁 𝗔 𝗠𝗮𝗴𝗶𝗰 𝗡𝘂𝗺𝗯𝗲𝗿

You’ll often hear: If p < 0.05, it’s significant!

𝗕𝘂𝘁 𝗯𝗲 𝗰𝗮𝗿𝗲𝗳𝘂𝗹.
This threshold is not universal.
• In critical fields (like medicine), you might need a much lower p-value (e.g., 0.01).
• In exploratory analysis, you might tolerate higher p-values.

Context always matters.

𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗔𝗱𝘃𝗶𝗰𝗲

When evaluating your regression model:
➔ 𝗗𝗼𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗹𝗼𝗼𝗸 𝗮𝘁 𝗽-𝘃𝗮𝗹𝘂𝗲𝘀 𝗮𝗹𝗼𝗻𝗲.

𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿:
• The feature’s practical importance (not just statistical)
• Multicollinearity (highly correlated variables can distort p-values)
• Overall model fit (R², Adjusted R²)

𝗜𝗻 𝗦𝗵𝗼𝗿𝘁:

𝗟𝗼𝘄 𝗣-𝗩𝗮𝗹𝘂𝗲 = 𝗧𝗵𝗲 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗺𝗮𝘁𝘁𝗲𝗿𝘀.
𝗛𝗶𝗴𝗵 𝗣-𝗩𝗮𝗹𝘂𝗲 = 𝗜𝘁’𝘀 𝗽𝗿𝗼𝗯𝗮𝗯𝗹𝘆 𝗷𝘂𝘀𝘁 𝗻𝗼𝗶𝘀𝗲.

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In a data science project, using multiple scalers can be beneficial when dealing with features that have different scales or distributions. Scaling is important in machine learning to ensure that all features contribute equally to the model training process and to prevent certain features from dominating others.

Here are some scenarios where using multiple scalers can be helpful in a data science project:

1. Standardization vs. Normalization: Standardization (scaling features to have a mean of 0 and a standard deviation of 1) and normalization (scaling features to a range between 0 and 1) are two common scaling techniques. Depending on the distribution of your data, you may choose to apply different scalers to different features.

2. RobustScaler vs. MinMaxScaler: RobustScaler is a good choice when dealing with outliers, as it scales the data based on percentiles rather than the mean and standard deviation. MinMaxScaler, on the other hand, scales the data to a specific range. Using both scalers can be beneficial when dealing with mixed types of data.

3. Feature engineering: In feature engineering, you may create new features that have different scales than the original features. In such cases, applying different scalers to different sets of features can help maintain consistency in the scaling process.

4. Pipeline flexibility: By using multiple scalers within a preprocessing pipeline, you can experiment with different scaling techniques and easily switch between them to see which one works best for your data.

5. Domain-specific considerations: Certain domains may require specific scaling techniques based on the nature of the data. For example, in image processing tasks, pixel values are often scaled differently than numerical features.

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