Data Science & Machine Learning

14 Jul 2025 11:59

Random Module in Python 👆

Data Science & Machine Learning

14 Jul 2025 08:49

𝟯 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗦𝘁𝗮𝗿𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱!😍

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Data Science & Machine Learning

13 Jul 2025 19:07

🚀 Complete Roadmap to Become a Data Scientist in 5 Months

📅 Week 1-2: Fundamentals
✅ Day 1-3: Introduction to Data Science, its applications, and roles.
✅ Day 4-7: Brush up on Python programming 🐍.
✅ Day 8-10: Learn basic statistics 📊 and probability 🎲.

🔍 Week 3-4: Data Manipulation & Visualization
📝 Day 11-15: Master Pandas for data manipulation.
📈 Day 16-20: Learn Matplotlib & Seaborn for data visualization.

🤖 Week 5-6: Machine Learning Foundations
🔬 Day 21-25: Introduction to scikit-learn.
📊 Day 26-30: Learn Linear & Logistic Regression.

🏗 Week 7-8: Advanced Machine Learning
🌳 Day 31-35: Explore Decision Trees & Random Forests.
📌 Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.

🧠 Week 9-10: Deep Learning
🤖 Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
📸 Day 46-50: Learn CNNs & RNNs for image & text data.

🏛 Week 11-12: Data Engineering
🗄 Day 51-55: Learn SQL & Databases.
🧹 Day 56-60: Data Preprocessing & Cleaning.

📊 Week 13-14: Model Evaluation & Optimization
📏 Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
📉 Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).

🏗 Week 15-16: Big Data & Tools
🐘 Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
☁️ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).

🚀 Week 17-18: Deployment & Production
🛠 Day 81-85: Deploy models using Flask or FastAPI.
📦 Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).

🎯 Week 19-20: Specialization
📝 Day 91-95: Choose NLP or Computer Vision, based on your interest.

🏆 Week 21-22: Projects & Portfolio
📂 Day 96-100: Work on Personal Data Science Projects.

💬 Week 23-24: Soft Skills & Networking
🎤 Day 101-105: Improve Communication & Presentation Skills.
🌐 Day 106-110: Attend Online Meetups & Forums.

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💻 Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
📂 Day 116-120: Review your projects & prepare for discussions.

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📩 Day 121-125: Start applying for Entry-Level Data Scientist positions.

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📝 Day 126-130: Attend Interviews & Practice Whiteboard Problems.

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📰 Day 131-135: Stay updated with the Latest Data Science Trends.

🏆 Week 33-34: Accepting Offers
📝 Day 136-140: Evaluate job offers & Negotiate Your Salary.

🏢 Week 35-36: Settling In
🎯 Day 141-150: Start your New Data Science Job, adapt & keep learning!

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Data Science & Machine Learning

13 Jul 2025 10:25

SQL Cheatsheet 📝

This SQL cheatsheet is designed to be your quick reference guide for SQL programming. Whether you’re a beginner learning how to query databases or an experienced developer looking for a handy resource, this cheatsheet covers essential SQL topics.

1. Database Basics
- CREATE DATABASE db_name;
- USE db_name;

2. Tables
- Create Table: CREATE TABLE table_name (col1 datatype, col2 datatype);
- Drop Table: DROP TABLE table_name;
- Alter Table: ALTER TABLE table_name ADD column_name datatype;

3. Insert Data
- INSERT INTO table_name (col1, col2) VALUES (val1, val2);

4. Select Queries
- Basic Select: SELECT * FROM table_name;
- Select Specific Columns: SELECT col1, col2 FROM table_name;
- Select with Condition: SELECT * FROM table_name WHERE condition;

5. Update Data
- UPDATE table_name SET col1 = value1 WHERE condition;

6. Delete Data
- DELETE FROM table_name WHERE condition;

7. Joins
- Inner Join: SELECT * FROM table1 INNER JOIN table2 ON table1.col = table2.col;
- Left Join: SELECT * FROM table1 LEFT JOIN table2 ON table1.col = table2.col;
- Right Join: SELECT * FROM table1 RIGHT JOIN table2 ON table1.col = table2.col;

8. Aggregations
- Count: SELECT COUNT(*) FROM table_name;
- Sum: SELECT SUM(col) FROM table_name;
- Group By: SELECT col, COUNT(*) FROM table_name GROUP BY col;

9. Sorting & Limiting
- Order By: SELECT * FROM table_name ORDER BY col ASC|DESC;
- Limit Results: SELECT * FROM table_name LIMIT n;

10. Indexes
- Create Index: CREATE INDEX idx_name ON table_name (col);
- Drop Index: DROP INDEX idx_name;

11. Subqueries
- SELECT * FROM table_name WHERE col IN (SELECT col FROM other_table);

12. Views
- Create View: CREATE VIEW view_name AS SELECT * FROM table_name;
- Drop View: DROP VIEW view_name;

Data Science & Machine Learning

12 Jul 2025 21:02

Ever wondered what the difference is between a Data Analyst and a Data Scientist? Both roles are in high demand, but they tackle data in different ways.

Data Science & Machine Learning

12 Jul 2025 17:10

Data Analyst: Analyzes data to provide insights and reports for decision-making.

Data Scientist: Builds models to predict outcomes and uncover deeper insights from data.

Data Engineer: Creates and maintains the systems that store and process data.

Data Science & Machine Learning

12 Jul 2025 12:24

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Data Science & Machine Learning

12 Jul 2025 09:16

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Data Science & Machine Learning

11 Jul 2025 11:32

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Data Science & Machine Learning

11 Jul 2025 09:07

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Data Science & Machine Learning

10 Jul 2025 13:50

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Data Science & Machine Learning

10 Jul 2025 08:17

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Data Science & Machine Learning

09 Jul 2025 16:33

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Data Science & Machine Learning

09 Jul 2025 12:38

Data Science Cheatsheet 💪

Data Science & Machine Learning

09 Jul 2025 09:12

🔅SQL Revision Notes for Interview💡

Data Science & Machine Learning

14 Jul 2025 10:08

SQL Joins — A Practical Cheatsheet for Professionals

If you’re working with relational data — whether you’re a business analyst, backend dev, or aspiring data scientist — mastering SQL joins isn’t optional. It’s fundamental.

Here’s a concise guide to the most important join types, with real-world use cases:


INNER JOIN

Returns records with matching keys from both tables.
Use case: Show only customers who’ve placed at least one order.


LEFT JOIN (OUTER)

Returns all rows from the left table, and matched rows from the right.
Use case: List all customers, including those with zero orders.


RIGHT JOIN (OUTER)

Returns all rows from the right table. Rarely used, but powerful.
Use case: Show all orders, even if the customer was deleted.


FULL OUTER JOIN

Returns all records from both tables.
Use case: Capture everything — matched and unmatched.


CROSS JOIN

Returns the cartesian product.
Use case: Generate every possible product/supplier combo.


SELF JOIN

Joins a table to itself.
Use case: Show employees and their reporting managers.


Best Practices

Use aliases (A, B) for clean code
Prefer JOIN ON over WHERE for clarity
Always test joins with LIMIT to prevent overloads

Data Science & Machine Learning

13 Jul 2025 21:10

Here's a good list of cheat sheets for programmers (all free):

Data Science Cheatsheet

https://github.com/aaronwangy/Data-Science-Cheatsheet

SQL Cheatsheet

sqltutorial.org/sql-cheat-sheet

t.me/sqlspecialist/827

https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf

Java Programming Cheatsheet

https://introcs.cs.princeton.edu/java/11cheatsheet/

Javascript Cheatsheet

quickref.me/javascript.html

t.me/javascript_courses/532

Data Analytics Cheatsheets

https://dataanalytics.beehiiv.com/p/data

Python Cheat sheet

quickref.me/python.html

/channel/pythondevelopersindia/314

GIT and Machine Learning Cheatsheet

/channel/datasciencefun/714

HTML Cheatsheet

https://web.stanford.edu/group/csp/cs21/htmlcheatsheet.pdf

htmlcheatsheet.com

CSS Cheatsheet

htmlcheatsheet.com/css

jQuery Cheatsheet

t.me/webdevelopmentbook/90

Data Visualization
t.me/datasciencefun/698

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Data Science & Machine Learning

13 Jul 2025 15:43

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Data Science & Machine Learning

13 Jul 2025 08:38

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Data Science & Machine Learning

12 Jul 2025 18:51

If you want to Excel in Data Science and become an expert, master these essential concepts:

Core Data Science Skills:

• Python for Data Science – Pandas, NumPy, Matplotlib, Seaborn
• SQL for Data Extraction – SELECT, JOIN, GROUP BY, CTEs, Window Functions
• Data Cleaning & Preprocessing – Handling missing data, outliers, duplicates
• Exploratory Data Analysis (EDA) – Visualizing data trends

Machine Learning (ML):

• Supervised Learning – Linear Regression, Decision Trees, Random Forest
• Unsupervised Learning – Clustering, PCA, Anomaly Detection
• Model Evaluation – Cross-validation, Confusion Matrix, ROC-AUC
• Hyperparameter Tuning – Grid Search, Random Search

Deep Learning (DL):

• Neural Networks – TensorFlow, PyTorch, Keras
• CNNs & RNNs – Image & sequential data processing
• Transformers & LLMs – GPT, BERT, Stable Diffusion

Big Data & Cloud Computing:

• Hadoop & Spark – Handling large datasets
• AWS, GCP, Azure – Cloud-based data science solutions
• MLOps – Deploy models using Flask, FastAPI, Docker

Statistics & Mathematics for Data Science:

• Probability & Hypothesis Testing – P-values, T-tests, Chi-square
• Linear Algebra & Calculus – Matrices, Vectors, Derivatives
• Time Series Analysis – ARIMA, Prophet, LSTMs

Real-World Applications:

• Recommendation Systems – Personalized AI suggestions
• NLP (Natural Language Processing) – Sentiment Analysis, Chatbots
• AI-Powered Business Insights – Data-driven decision-making

React with ❤️ for more

Data Science & Machine Learning

12 Jul 2025 16:13

Steps to become a data analyst

Learn the Basics of Data Analysis:
Familiarize yourself with foundational concepts in data analysis, statistics, and data visualization. Online courses and textbooks can help.
Free books & other useful data analysis resources - /channel/learndataanalysis

Develop Technical Skills:
Gain proficiency in essential tools and technologies such as:

SQL: Learn how to query and manipulate data in relational databases.
Free Resources- @sqlanalyst

Excel: Master data manipulation, basic analysis, and visualization.
Free Resources- @excel_analyst

Data Visualization Tools: Become skilled in tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn.
Free Resources- @PowerBI_analyst

Programming: Learn a programming language like Python or R for data analysis and manipulation.
Free Resources- @pythonanalyst

Statistical Packages: Familiarize yourself with packages like Pandas, NumPy, and SciPy (for Python) or ggplot2 (for R).

Hands-On Practice:
Apply your knowledge to real datasets. You can find publicly available datasets on platforms like Kaggle or create your datasets for analysis.

Build a Portfolio:
Create data analysis projects to showcase your skills. Share them on platforms like GitHub, where potential employers can see your work.

Networking:
Attend data-related meetups, conferences, and online communities. Networking can lead to job opportunities and valuable insights.

Data Analysis Projects:
Work on personal or freelance data analysis projects to gain experience and demonstrate your abilities.

Job Search:
Start applying for entry-level data analyst positions or internships. Look for job listings on company websites, job boards, and LinkedIn.
Jobs & Internship opportunities: @getjobss

Prepare for Interviews:
Practice common data analyst interview questions and be ready to discuss your past projects and experiences.

Continual Learning:
The field of data analysis is constantly evolving. Stay updated with new tools, techniques, and industry trends.

Soft Skills:
Develop soft skills like critical thinking, problem-solving, communication, and attention to detail, as they are crucial for data analysts.

Never ever give up:
The journey to becoming a data analyst can be challenging, with complex concepts and technical skills to learn. There may be moments of frustration and self-doubt, but remember that these are normal parts of the learning process. Keep pushing through setbacks, keep learning, and stay committed to your goal.

ENJOY LEARNING 👍👍

Data Science & Machine Learning

12 Jul 2025 12:06

Core data science concepts you should know:

🔢 1. Statistics & Probability

Descriptive statistics: Mean, median, mode, standard deviation, variance

Inferential statistics: Hypothesis testing, confidence intervals, p-values, t-tests, ANOVA

Probability distributions: Normal, Binomial, Poisson, Uniform

Bayes' Theorem

Central Limit Theorem


📊 2. Data Wrangling & Cleaning

Handling missing values

Outlier detection and treatment

Data transformation (scaling, encoding, normalization)

Feature engineering

Dealing with imbalanced data


📈 3. Exploratory Data Analysis (EDA)

Univariate, bivariate, and multivariate analysis

Correlation and covariance

Data visualization tools: Matplotlib, Seaborn, Plotly

Insights generation through visual storytelling


🤖 4. Machine Learning Fundamentals

Supervised Learning: Linear regression, logistic regression, decision trees, SVM, k-NN

Unsupervised Learning: K-means, hierarchical clustering, PCA

Model evaluation: Accuracy, precision, recall, F1-score, ROC-AUC

Cross-validation and overfitting/underfitting

Bias-variance tradeoff


🧠 5. Deep Learning (Basics)

Neural networks: Perceptron, MLP

Activation functions (ReLU, Sigmoid, Tanh)

Backpropagation

Gradient descent and learning rate

CNNs and RNNs (intro level)


🗃️ 6. Data Structures & Algorithms (DSA)

Arrays, lists, dictionaries, sets

Sorting and searching algorithms

Time and space complexity (Big-O notation)

Common problems: string manipulation, matrix operations, recursion


💾 7. SQL & Databases

SELECT, WHERE, GROUP BY, HAVING

JOINS (inner, left, right, full)

Subqueries and CTEs

Window functions

Indexing and normalization


📦 8. Tools & Libraries

Python: pandas, NumPy, scikit-learn, TensorFlow, PyTorch

R: dplyr, ggplot2, caret

Jupyter Notebooks for experimentation

Git and GitHub for version control


🧪 9. A/B Testing & Experimentation

Control vs. treatment group

Hypothesis formulation

Significance level, p-value interpretation

Power analysis


🌐 10. Business Acumen & Storytelling

Translating data insights into business value

Crafting narratives with data

Building dashboards (Power BI, Tableau)

Knowing KPIs and business metrics

React ❤️ for more

Data Science & Machine Learning

11 Jul 2025 16:48

Data Analyst Roadmap

Like if it helps ❤️

Data Science & Machine Learning

11 Jul 2025 11:12

30 Days Python Roadmap for Data Analysts 👆

Data Science & Machine Learning

10 Jul 2025 16:50

SQL interview questions with answers 😄👇

1. Question: What is SQL?

   Answer: SQL (Structured Query Language) is a programming language designed for managing and manipulating relational databases. It is used to query, insert, update, and delete data in databases.

2. Question: Differentiate between SQL and MySQL.

   Answer: SQL is a language for managing relational databases, while MySQL is an open-source relational database management system (RDBMS) that uses SQL as its language.

3. Question: Explain the difference between INNER JOIN and LEFT JOIN.

   Answer: INNER JOIN returns rows when there is a match in both tables, while LEFT JOIN returns all rows from the left table and the matched rows from the right table, filling in with NULLs for non-matching rows.

4. Question: How do you remove duplicate records from a table?

   Answer: Use the DISTINCT keyword in a SELECT statement to retrieve unique records. For example: SELECT DISTINCT column1, column2 FROM table;

5. Question: What is a subquery in SQL?

   Answer: A subquery is a query nested inside another query. It can be used to retrieve data that will be used in the main query as a condition to further restrict the data to be retrieved.

6. Question: Explain the purpose of the GROUP BY clause.

   Answer: The GROUP BY clause is used to group rows that have the same values in specified columns into summary rows, like when using aggregate functions such as COUNT, SUM, AVG, etc.

7. Question: How can you add a new record to a table?

   Answer: Use the INSERT INTO statement. For example: INSERT INTO table_name (column1, column2) VALUES (value1, value2);

8. Question: What is the purpose of the HAVING clause?

   Answer: The HAVING clause is used in combination with the GROUP BY clause to filter the results of aggregate functions based on a specified condition.

9. Question: Explain the concept of normalization in databases.

   Answer: Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves breaking down tables into smaller, related tables.

10. Question: How do you update data in a table in SQL?

    Answer: Use the UPDATE statement to modify existing records in a table. For example: UPDATE table_name SET column1 = value1 WHERE condition;

Data Science & Machine Learning

10 Jul 2025 09:18

Tableau Cheat Sheet ✅

This Tableau cheatsheet is designed to be your quick reference guide for data visualization and analysis using Tableau. Whether you’re a beginner learning the basics or an experienced user looking for a handy resource, this cheatsheet covers essential topics.

1. Connecting to Data
   - Use *Connect* pane to connect to various data sources (Excel, SQL Server, Text files, etc.).

2. Data Preparation
   - Data Interpreter: Clean data automatically using the Data Interpreter.
   - Join Data: Combine data from multiple tables using joins (Inner, Left, Right, Outer).
   - Union Data: Stack data from multiple tables with the same structure.

3. Creating Views
   - Drag & Drop: Drag fields from the Data pane onto Rows, Columns, or Marks to create visualizations.
   - Show Me: Use the *Show Me* panel to select different visualization types.

4. Types of Visualizations
   - Bar Chart: Compare values across categories.
   - Line Chart: Display trends over time.
   - Pie Chart: Show proportions of a whole (use sparingly).
   - Map: Visualize geographic data.
   - Scatter Plot: Show relationships between two variables.

5. Filters
   - Dimension Filters: Filter data based on categorical values.
   - Measure Filters: Filter data based on numerical values.
   - Context Filters: Set a context for other filters to improve performance.

6. Calculated Fields
   - Create calculated fields to derive new data:
     - Example: Sales Growth = SUM([Sales]) - SUM([Previous Sales])

7. Parameters
   - Use parameters to allow user input and control measures dynamically.

8. Formatting
   - Format fonts, colors, borders, and lines using the Format pane for better visual appeal.

9. Dashboards
   - Combine multiple sheets into a dashboard using the *Dashboard* tab.
   - Use dashboard actions (filter, highlight, URL) to create interactivity.

10. Story Points
    - Create a story to guide users through insights with narrative and visualizations.

11. Publishing & Sharing
    - Publish dashboards to Tableau Server or Tableau Online for sharing and collaboration.

12. Export Options
    - Export to PDF or image for offline use.

13. Keyboard Shortcuts
    - Show/Hide Sidebar: Ctrl+Alt+T
    - Duplicate Sheet: Ctrl + D
    - Undo: Ctrl + Z
    - Redo: Ctrl + Y

14. Performance Optimization
    - Use extracts instead of live connections for faster performance.
    - Optimize calculations and filters to improve dashboard loading times.

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Data Science & Machine Learning

09 Jul 2025 19:45

When preparing for an SQL project-based interview, the focus typically shifts from theoretical knowledge to practical application. Here are some SQL project-based interview questions that could help assess your problem-solving skills and experience:

1. Database Design and Schema
- Question: Describe a database schema you have designed in a past project. What were the key entities, and how did you establish relationships between them?
- Follow-Up: How did you handle normalization? Did you denormalize any tables for performance reasons?

2. Data Modeling
- Question: How would you model a database for an e-commerce application? What tables would you include, and how would they relate to each other?
- Follow-Up: How would you design the schema to handle scenarios like discount codes, product reviews, and inventory management?

3. Query Optimization
- Question: Can you discuss a time when you optimized an SQL query? What was the original query, and what changes did you make to improve its performance?
- Follow-Up: What tools or techniques did you use to identify and resolve the performance issues?

4. ETL Processes
- Question: Describe an ETL (Extract, Transform, Load) process you have implemented. How did you handle data extraction, transformation, and loading?
- Follow-Up: How did you ensure data quality and consistency during the ETL process?

5. Handling Large Datasets
- Question: In a project where you dealt with large datasets, how did you manage performance and storage issues?
- Follow-Up: What indexing strategies or partitioning techniques did you use?

6. Joins and Subqueries
- Question: Provide an example of a complex query you wrote involving multiple joins and subqueries. What was the business problem you were solving?
- Follow-Up: How did you ensure that the query performed efficiently?

7. Stored Procedures and Functions
- Question: Have you created stored procedures or functions in any of your projects? Can you describe one and explain why you chose to encapsulate the logic in a stored procedure?
- Follow-Up: How did you handle error handling and logging within the stored procedure?

8. Data Integrity and Constraints
- Question: How did you enforce data integrity in your SQL projects? Can you give examples of constraints (e.g., primary keys, foreign keys, unique constraints) you implemented?
- Follow-Up: How did you handle situations where constraints needed to be temporarily disabled or modified?

9. Version Control and Collaboration
- Question: How did you manage database version control in your projects? What tools or practices did you use to ensure collaboration with other developers?
- Follow-Up: How did you handle conflicts or issues arising from multiple developers working on the same database?

10. Data Migration
- Question: Describe a data migration project you worked on. How did you ensure that the migration was successful, and what steps did you take to handle data inconsistencies or errors?
- Follow-Up: How did you test the migration process before moving to the production environment?

11. Security and Permissions
- Question: In your SQL projects, how did you manage database security?
- Follow-Up: How did you handle encryption or sensitive data within the database?

12. Handling Unstructured Data
- Question: Have you worked with unstructured or semi-structured data in an SQL environment?
- Follow-Up: What challenges did you face, and how did you overcome them?

13. Real-Time Data Processing
   - Question: Can you describe a project where you handled real-time data processing using SQL? What were the key challenges, and how did you address them?
   - Follow-Up: How did you ensure the performance and reliability of the real-time data processing system?

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Data Science & Machine Learning

09 Jul 2025 13:53

Top 20 AI Concepts You Should Know

1 - Machine Learning: Core algorithms, statistics, and model training techniques.
2 - Deep Learning: Hierarchical neural networks learning complex representations automatically.
3 - Neural Networks: Layered architectures efficiently model nonlinear relationships accurately.
4 - NLP: Techniques to process and understand natural language text.
5 - Computer Vision: Algorithms interpreting and analyzing visual data effectively
6 - Reinforcement Learning: Distributed traffic across multiple servers for reliability.
7 - Generative Models: Creating new data samples using learned data.
8 - LLM: Generates human-like text using massive pre-trained data.
9 - Transformers: Self-attention-based architecture powering modern AI models.
10 - Feature Engineering: Designing informative features to improve model performance significantly.
11 - Supervised Learning: Learns useful representations without labeled data.
12 - Bayesian Learning: Incorporate uncertainty using probabilistic model approaches.
13 - Prompt Engineering: Crafting effective inputs to guide generative model outputs.
14 - AI Agents: Autonomous systems that perceive, decide, and act.
15 - Fine-Tuning Models: Customizes pre-trained models for domain-specific tasks.
16 - Multimodal Models: Processes and generates across multiple data types like images, videos, and text.
17 - Embeddings: Transforms input into machine-readable vector formats.
18 - Vector Search: Finds similar items using dense vector embeddings.
19 - Model Evaluation: Assessing predictive performance using validation techniques.
20 - AI Infrastructure: Deploying scalable systems to support AI operations.

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Data Science & Machine Learning

09 Jul 2025 11:15

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Data Science & Machine Learning

09 Jul 2025 08:14

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