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Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence Admin: @coderfun
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El canal Machine Learning & Artificial Intelligence | Data Science Free Courses (@datasciencefree) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 66 732 suscriptores, ocupando la posición 2 450 en la categoría Educación y el puesto 436 en la región Malasia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 66 732 suscriptores.
Según los últimos datos del 24 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 534, y en las últimas 24 horas de 42, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 0.75%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.79% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 502 visualizaciones. En el primer día suele acumular 524 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
- Intereses temáticos: El contenido se centra en temas clave como sellerflash, waybienad, pricing, buybox, buyer.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Perfect channel to learn Data Analytics, Data Sciene, Machine Learning & Artificial Intelligence
Admin: @coderfun”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 25 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
66 732
Suscriptores
+4224 horas
+687 días
+53430 días
Archivo de publicaciones
+8
Every data scientist should know🙌🤩
Building the machine learning model
Learning Python for data science can be a rewarding experience. Here are some steps you can follow to get started:
1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python.
2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn.
3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio.
4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science.
5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have.
6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus.
7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills.
Remember that learning Python for data science is a continuous process, so be patient and persistent in your efforts. Good luck!
Please react 👍❤️ if you guys want me to share more of this content...
10 Best Practices for Data Science
The main bottleneck in data science are no longer compute power or sophisticated algorithms, but craftsmanship, communication, and process.
And that the aim is to not only produce work that is accurate and correct, but also can be understood, work that others can collaborate on.
Rule 1: Start Organized, Stay Organized
Rule 2: Everything Comes from Somewhere, and the Raw Data is Immutable
Rule 3: Version Control is Basic Professionalism
Rule 4: Notebooks are for Exploration, Source Files are for Repetition
Rule 5: Tests and Sanity Checks Prevent Catastrophes
Rule 6: Fail Loudly, Fail Quickly
Rule 7: Project Runs are Fully Automated from Raw Data to Final Outputs
Rule 8: Important Parameters are Extracted and Centralized
Rule 9: Project Runs are Verbose by Default and Result in Tangible Artifacts
Rule 10: Start with the Simplest Possible End-to-End Pipeline
Lessons
No one tells you to train Machine Learning models in Data Science interviews.
Problems in Data Science interviews are focused on:
1. SQL for Querying Data
2. Python/R for Data Manipulation
3. Scenario Based Problems to test your way of approaching problems
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🔺 Free Machine learning Courses
1️⃣ Intro to ML course : an introductory and self-paced course to start machine learning.
2️⃣ ML for Everybody course : A simple approach to learning machine learning concepts.
3️⃣ ML in Python course : focus on machine learning with Python and Scikit-Learn.
4️⃣ ML Crash Course : A quick but comprehensive introduction to machine learning.
5️⃣ CS229 : ML : An advanced course for those who want to deepen their knowledge
TOP 10 SQL Concepts for Job Interview
1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)
TOP 10 Statistics Concepts for Job Interview
1. Sampling
2. Experiments (A/B tests)
3. Descriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression
TOP 10 Python Concepts for Job Interview
1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming
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ML Engineer vs AI Engineer
ML Engineer / MLOps
-Focuses on the deployment of machine learning models.
-Bridges the gap between data scientists and production environments.
-Designing and implementing machine learning models into production.
-Automating and orchestrating ML workflows and pipelines.
-Ensuring reproducibility, scalability, and reliability of ML models.
-Programming: Python, R, Java
-Libraries: TensorFlow, PyTorch, Scikit-learn
-MLOps: MLflow, Kubeflow, Docker, Kubernetes, Git, Jenkins, CI/CD tools
AI Engineer / Developer
- Applying AI techniques to solve specific problems.
- Deep knowledge of AI algorithms and their applications.
- Developing and implementing AI models and systems.
- Building and integrating AI solutions into existing applications.
- Collaborating with cross-functional teams to understand requirements and deliver AI-powered solutions.
- Programming: Python, Java, C++
- Libraries: TensorFlow, PyTorch, Keras, OpenCV
- Frameworks: ONNX, Hugging Face
Data Scientist Vs. Data Analyst Vs. Data Engineer
What’s the difference between the data roles?
The data role family is more than just one role that does it all.
Here are the key differences.
Data Scientist
- Focuses on deriving insights and creating predictive models.
- Strong background in math, statistics, and machine learning.
- Analyzing complex datasets to identify patterns, trends, and insights.
- Developing predictive models and machine learning algorithms.
- Communicating findings to stakeholders through reports and visualizations.
- Working with data engineers and analysts to implement data-driven solutions.
- Uses tools like Python, R, SQL, Tableau, and others
Data analyst
- Focuses more on interpreting and visualizing data rather than creating predictive models.
- Often works closely with business teams to provide actionable insights.
- Collecting, processing, and performing statistical analyses on large data sets.
- Creating data visualizations and dashboards to communicate insights.
- Conducting ad-hoc analyses and generating reports for business decision-making.
- Ensuring data quality and accuracy.
- Uses tools like Excel, SQL, BI Tools, SAS
Data Engineer
- Focuses on the infrastructure and tools needed to store, process, and retrieve data.
- Designing, building, and maintaining data pipelines and architectures.
- Ensuring data is accessible, reliable, and efficient to process.
- Integrating data from various sources and formats.
- Optimizing database performance and data storage solutions.
- Uses languages like Python, Java, Scala, as well as SQL and NOSQL, ETL tools, data warehouse tools and others
Complete Machine Learning Roadmap
👇👇
1. Introduction to Machine Learning
- Definition
- Purpose
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
2. Mathematics for Machine Learning
- Linear Algebra
- Calculus
- Statistics and Probability
3. Programming Languages for ML
- Python and Libraries (NumPy, Pandas, Matplotlib)
- R
4. Data Preprocessing
- Handling Missing Data
- Feature Scaling
- Data Transformation
5. Exploratory Data Analysis (EDA)
- Data Visualization
- Descriptive Statistics
6. Supervised Learning
- Regression
- Classification
- Model Evaluation
7. Unsupervised Learning
- Clustering (K-Means, Hierarchical)
- Dimensionality Reduction (PCA)
8. Model Selection and Evaluation
- Cross-Validation
- Hyperparameter Tuning
- Evaluation Metrics (Precision, Recall, F1 Score)
9. Ensemble Learning
- Random Forest
- Gradient Boosting
10. Neural Networks and Deep Learning
- Introduction to Neural Networks
- Building and Training Neural Networks
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
11. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Named Entity Recognition (NER)
12. Reinforcement Learning
- Basics
- Markov Decision Processes
- Q-Learning
13. Machine Learning Frameworks
- TensorFlow
- PyTorch
- Scikit-Learn
14. Deployment of ML Models
- Flask for Web Deployment
- Docker and Kubernetes
15. Ethical and Responsible AI
- Bias and Fairness
- Ethical Considerations
16. Machine Learning in Production
- Model Monitoring
- Continuous Integration/Continuous Deployment (CI/CD)
17. Real-world Projects and Case Studies
18. Machine Learning Resources
- Online Courses
- Books
- Blogs and Journals
📚 Learning Resources for Machine Learning:
- [Python for Machine Learning](https://t.me/udacityfreecourse/167)
- [Fast.ai: Practical Deep Learning for Coders](https://course.fast.ai/)
- [Intro to Machine Learning](https://learn.microsoft.com/en-us/training/paths/intro-to-ml-with-python/)
📚 Books:
- Machine Learning Interviews
- Machine Learning for Absolute Beginners
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ENJOY LEARNING! 👍👍
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There’s no single powerful machine learning algorithm that works well on any problem.
Yes, algorithms like XGBoost can help you in Kaggle Competitions to build more accurate models.
But the real world is different. Choose algorithms based on your data characteristics, the assumptions of algorithms, and the problem type.
Iterating over Pandas DataFrames can cost you much performance.
Comparing iterrows() and itertuples() can help in some cases:
1. 𝗶𝘁𝗲𝗿𝗿𝗼𝘄𝘀():
Generates index and Series pairs for each row.
𝗣𝗿𝗼𝘀: Easy to use and intuitive. Suitable for small datasets.
𝗖𝗼𝗻𝘀: Slow for large datasets. Series conversion incurs additional overhead.
𝗨𝘀𝗲 𝗖𝗮𝘀𝗲: Quick data inspection and small-scale transformations.
2. 𝗶𝘁𝗲𝗿𝘁𝘂𝗽𝗹𝗲𝘀():
Returns namedtuples of the DataFrame rows.
𝗣𝗿𝗼𝘀: Much faster than iterrows(). More efficient for large datasets.
𝗖𝗼𝗻𝘀: Slightly less intuitive syntax. Avoid using when mutating DataFrames.
𝗨𝘀𝗲 𝗖𝗮𝘀𝗲: Large-scale data processing and read-only operations.
For optimal performance, use vectorized operations whenever possible! Iteration methods should be your last resort!
I have curated the best interview resources to crack Data Science Interviews
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Machine learning engineering is evolving into two distinct paths:
- ML Engineers who focus on the research side:
Advanced algorithms, GANs, CNNs, RL, learned optimizations, PyTorch, TensorFlow, and Keras. Developing novel architectures and publishing papers.
- ML Engineers who focus on the engineering side:
Model deployment, Python, business metrics, inference optimization, and applying research findings into production.
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Entry-level AI/ML Jobs nowadays
- 3+ years of deploying GPT models without touching the keyboard.
- 5+ years of experience using TensorFlow, scikit-learn, etc.
- 4+ years of Python/Java experience.
- Graduate from a reputable university (TOP TIER UNIVERSITY) with a minimum GPA of 3.99/4.00.
- Expertise in Database System Management, Frontend Development, and System Integration.
- Proficiency in Python and one or more programming languages such as Java, Javascript, or GoLang is a plus
- 4+ years with training, fine-tuning, and deploying LLMs (e.g., GPT, LLAMA, mistral)
• Expertise in using Al development frameworks such as TensorFlow, PyTorch, LangChain, Hugging Face Transformers
- Must be a certified Kubernetes administrator.
- Ability to write production-ready code in less than 24 hours.
- Proven track record of solving world hunger with AI.
- Must have telepathic debugging skills.
- Willing to work weekends, holidays, and during full moons.
Oh, and the most important requirement: must be resilient in handling sudden revisions from the boss
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Salary of a Data Scientist can go up to ₹98 Lakhs in India
You can get this job easily
Just say ‘Bell Curve’ instead of ‘Ghanta’ when talking to people 😂
Evolution in #data and #AI.
Data analyst -> data scientist -> AI engineer -> ???
