I often get asked- what's the BEST Certification for #datascience or #machinelearning?
👉My answer is: none
The reality is that certification don't matter for data science.
This is not commerce. we are not using the same techniques over and over again to solve well-defined problems.
The problems are challenging, the data is messy and numerous techniques are used.
So if you've wondering which certification you should get, Save yourself,some mental energy and stop thinking about it- they are not really matter.
👉 Instead, grab a dataset and start playing with it.
👉 Start applying what you know and trying to solve interesting problems, learn something new every day.
👉 Here are few places to grab datasets to get you started
Google: https://toolbox.google.com/datasetsearch
Kaggle: https://www.kaggle.com/datasets
US Government Dataset: www.data.gov
Quandl: https://www.quandl.com/
UCI
ML repo: http://mlr.cs.umass.edu/ml/datasets.html
World Bank🏦: https://data.worldbank.org/
👉My answer is: none
The reality is that certification don't matter for data science.
This is not commerce. we are not using the same techniques over and over again to solve well-defined problems.
The problems are challenging, the data is messy and numerous techniques are used.
So if you've wondering which certification you should get, Save yourself,some mental energy and stop thinking about it- they are not really matter.
👉 Instead, grab a dataset and start playing with it.
👉 Start applying what you know and trying to solve interesting problems, learn something new every day.
👉 Here are few places to grab datasets to get you started
Google: https://toolbox.google.com/datasetsearch
Kaggle: https://www.kaggle.com/datasets
US Government Dataset: www.data.gov
Quandl: https://www.quandl.com/
UCI
ML repo: http://mlr.cs.umass.edu/ml/datasets.html
World Bank🏦: https://data.worldbank.org/
👍15❤1
7 machine learning secrets
Data cleaning and engineering take 80% of the time of the project I’m working on.
It’s better to understand the key math for data science than try to master it all.
Neural networks look cool on a resume but XGBoost and Logistic regression pay the bills
SQL is a non-negotiable even as a machine learning engineer
Hyperparameter tuning is a must
Project-based learning > tutorials
Cross-validation is your best friend
#machinelearning
Data cleaning and engineering take 80% of the time of the project I’m working on.
It’s better to understand the key math for data science than try to master it all.
Neural networks look cool on a resume but XGBoost and Logistic regression pay the bills
SQL is a non-negotiable even as a machine learning engineer
Hyperparameter tuning is a must
Project-based learning > tutorials
Cross-validation is your best friend
#machinelearning
👍12❤7
Introduction to Machine Learning Class Notes by Huy Nguyen
https://www.cs.cmu.edu/~hn1/documents/machine-learning/notes.pdf
https://www.cs.cmu.edu/~hn1/documents/machine-learning/notes.pdf
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras✅
👍2
🔰 How to become a data scientist in 2025?
👨🏻💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.
🔢 Step 1: Strengthen your math and statistics!
✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:
✅ Linear algebra: matrices, vectors, eigenvalues.
🔗 Course: MIT 18.06 Linear Algebra
✅ Calculus: derivative, integral, optimization.
🔗 Course: MIT Single Variable Calculus
✅ Statistics and probability: Bayes' theorem, hypothesis testing.
🔗 Course: Statistics 110
➖➖➖➖➖
🔢 Step 2: Learn to code.
✏️ Learn Python and become proficient in coding. The most important topics you need to master are:
✅ Python: Pandas, NumPy, Matplotlib libraries
🔗 Course: FreeCodeCamp Python Course
✅ SQL language: Join commands, Window functions, query optimization.
🔗 Course: Stanford SQL Course
✅ Data structures and algorithms: arrays, linked lists, trees.
🔗 Course: MIT Introduction to Algorithms
➖➖➖➖➖
🔢 Step 3: Clean and visualize data
✏️ Learn how to process and clean data and then create an engaging story from it!
✅ Data cleaning: Working with missing values and detecting outliers.
🔗 Course: Data Cleaning
✅ Data visualization: Matplotlib, Seaborn, Tableau
🔗 Course: Data Visualization Tutorial
➖➖➖➖➖
🔢 Step 4: Learn Machine Learning
✏️ It's time to enter the exciting world of machine learning! You should know these topics:
✅ Supervised learning: regression, classification.
✅ Unsupervised learning: clustering, PCA, anomaly detection.
✅ Deep learning: neural networks, CNN, RNN
🔗 Course: CS229: Machine Learning
➖➖➖➖➖
🔢 Step 5: Working with Big Data and Cloud Technologies
✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.
✅ Big Data Tools: Hadoop, Spark, Dask
✅ Cloud platforms: AWS, GCP, Azure
🔗 Course: Data Engineering
➖➖➖➖➖
🔢 Step 6: Do real projects!
✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.
✅ Kaggle competitions: solving real-world challenges.
✅ End-to-End projects: data collection, modeling, implementation.
✅ GitHub: Publish your projects on GitHub.
🔗 Platform: Kaggle🔗 Platform: ods.ai
➖➖➖➖➖
🔢 Step 7: Learn MLOps and deploy models
✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.
✅ MLOps training: model versioning, monitoring, model retraining.
✅ Deployment models: Flask, FastAPI, Docker
🔗 Course: Stanford MLOps Course
➖➖➖➖➖
🔢 Step 8: Stay up to date and network
✏️ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.
✅ Read scientific articles: arXiv, Google Scholar
✅ Connect with the data community:
🔗 Site: Papers with code
🔗 Site: AI Research at Google
👨🏻💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.
🔢 Step 1: Strengthen your math and statistics!
✏️ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:
✅ Linear algebra: matrices, vectors, eigenvalues.
🔗 Course: MIT 18.06 Linear Algebra
✅ Calculus: derivative, integral, optimization.
🔗 Course: MIT Single Variable Calculus
✅ Statistics and probability: Bayes' theorem, hypothesis testing.
🔗 Course: Statistics 110
➖➖➖➖➖
🔢 Step 2: Learn to code.
✏️ Learn Python and become proficient in coding. The most important topics you need to master are:
✅ Python: Pandas, NumPy, Matplotlib libraries
🔗 Course: FreeCodeCamp Python Course
✅ SQL language: Join commands, Window functions, query optimization.
🔗 Course: Stanford SQL Course
✅ Data structures and algorithms: arrays, linked lists, trees.
🔗 Course: MIT Introduction to Algorithms
➖➖➖➖➖
🔢 Step 3: Clean and visualize data
✏️ Learn how to process and clean data and then create an engaging story from it!
✅ Data cleaning: Working with missing values and detecting outliers.
🔗 Course: Data Cleaning
✅ Data visualization: Matplotlib, Seaborn, Tableau
🔗 Course: Data Visualization Tutorial
➖➖➖➖➖
🔢 Step 4: Learn Machine Learning
✏️ It's time to enter the exciting world of machine learning! You should know these topics:
✅ Supervised learning: regression, classification.
✅ Unsupervised learning: clustering, PCA, anomaly detection.
✅ Deep learning: neural networks, CNN, RNN
🔗 Course: CS229: Machine Learning
➖➖➖➖➖
🔢 Step 5: Working with Big Data and Cloud Technologies
✏️ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.
✅ Big Data Tools: Hadoop, Spark, Dask
✅ Cloud platforms: AWS, GCP, Azure
🔗 Course: Data Engineering
➖➖➖➖➖
🔢 Step 6: Do real projects!
✏️ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.
✅ Kaggle competitions: solving real-world challenges.
✅ End-to-End projects: data collection, modeling, implementation.
✅ GitHub: Publish your projects on GitHub.
🔗 Platform: Kaggle🔗 Platform: ods.ai
➖➖➖➖➖
🔢 Step 7: Learn MLOps and deploy models
✏️ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.
✅ MLOps training: model versioning, monitoring, model retraining.
✅ Deployment models: Flask, FastAPI, Docker
🔗 Course: Stanford MLOps Course
➖➖➖➖➖
🔢 Step 8: Stay up to date and network
✏️ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.
✅ Read scientific articles: arXiv, Google Scholar
✅ Connect with the data community:
🔗 Site: Papers with code
🔗 Site: AI Research at Google
#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #data
👍2
Stanford’s Machine Learning - by Andrew Ng
A complete lecture notes of 227 pages. Available Free.
Download the notes:
cs229.stanford.edu/main_notes.pdf
A complete lecture notes of 227 pages. Available Free.
Download the notes:
cs229.stanford.edu/main_notes.pdf
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming #Keras ✅
❤8👎4👍2
✅ If Data Science Tools Were Characters… 🧠🔍
📝 Excel — The Office Guy
Knows a bit of everything. Not flashy, but still gets the job done (until it crashes at 1M rows). 🤦♂️
🐍 Python — The All-Rounder
Writes poetry, builds models, scrapes web, visualizes data… and still has time for coffee. ☕
📊 Tableau — The Artist
Can turn boring data into jaw-dropping dashboards. Looks good, speaks in visuals. 🎨
🧮 R — The Statistician
Loves hypothesis tests and plots. Bit quirky, but unmatched in analysis. 🤓
🏗 SQL — The Architect
Knows where everything is stored. Can fetch exactly what you need… if you ask just right. 🏛️
🎯 Scikit-learn — The Model Trainer
Logistic, decision trees, clustering—you name it. Works fast, plays well with Python. ⚙️
🧠 TensorFlow/PyTorch — The Gym Bro
Lifts heavy deep learning weights. Complex but powerful. Needs proper tuning and GPUs. 💪
🗃 Pandas — The Organizer
Cleans, filters, groups, reshapes—loves playing with tables. But can be moody with large files. 🗂️
📍 Matplotlib/Seaborn — The Designer Duo
One is technical, the other stylish. Together they make your data look beautiful. ✨
🔁 Jupyter Notebook — The Presenter
Explains everything step by step. Talks code, visuals, and markdown—all in one flow. 🧑🏫
#DataScience #MachineLearning
📝 Excel — The Office Guy
Knows a bit of everything. Not flashy, but still gets the job done (until it crashes at 1M rows). 🤦♂️
🐍 Python — The All-Rounder
Writes poetry, builds models, scrapes web, visualizes data… and still has time for coffee. ☕
📊 Tableau — The Artist
Can turn boring data into jaw-dropping dashboards. Looks good, speaks in visuals. 🎨
🧮 R — The Statistician
Loves hypothesis tests and plots. Bit quirky, but unmatched in analysis. 🤓
🏗 SQL — The Architect
Knows where everything is stored. Can fetch exactly what you need… if you ask just right. 🏛️
🎯 Scikit-learn — The Model Trainer
Logistic, decision trees, clustering—you name it. Works fast, plays well with Python. ⚙️
🧠 TensorFlow/PyTorch — The Gym Bro
Lifts heavy deep learning weights. Complex but powerful. Needs proper tuning and GPUs. 💪
🗃 Pandas — The Organizer
Cleans, filters, groups, reshapes—loves playing with tables. But can be moody with large files. 🗂️
📍 Matplotlib/Seaborn — The Designer Duo
One is technical, the other stylish. Together they make your data look beautiful. ✨
🔁 Jupyter Notebook — The Presenter
Explains everything step by step. Talks code, visuals, and markdown—all in one flow. 🧑🏫
#DataScience #MachineLearning
❤19😁2
⚙️ Sber500 Batch 7 — Free Accelerator for AI & DeepTech Startups
Scaling your startup beyond local market?
Apply if you have:
• Sales and a team
• DeepTech startup at MVP+ stage (GenAI, robotics, advanced materials, photonics, quantum computing)
• Applied AI for research, Earth remote sensing, or autonomous transport
• Interest in the Russian market
You'll get:
• Up to 12-week online program in English
• Mentors from Europe, US, Asia
• Access to investors and corporate customers
• Demo day in Moscow, Fall 2026
• Community after program ends
Results:
• Revenue grows 4x on average within two years (up to 1,000x for some teams)
• 10,900+ contracts with corporations over 6 seasons
• International alumni from India, South Korea, Armenia, China, Turkey, Algeria
📅 Deadline: 10 April 2026
🌐 Online • English • Free
👉 Apply: https://sberbank-500.ru/
💬 Tap ❤️ for more opportunities!
#MachineLearning #DataScience #GenAI #DeepTech #Startup #AI
Scaling your startup beyond local market?
Apply if you have:
• Sales and a team
• DeepTech startup at MVP+ stage (GenAI, robotics, advanced materials, photonics, quantum computing)
• Applied AI for research, Earth remote sensing, or autonomous transport
• Interest in the Russian market
You'll get:
• Up to 12-week online program in English
• Mentors from Europe, US, Asia
• Access to investors and corporate customers
• Demo day in Moscow, Fall 2026
• Community after program ends
Results:
• Revenue grows 4x on average within two years (up to 1,000x for some teams)
• 10,900+ contracts with corporations over 6 seasons
• International alumni from India, South Korea, Armenia, China, Turkey, Algeria
📅 Deadline: 10 April 2026
🌐 Online • English • Free
👉 Apply: https://sberbank-500.ru/
💬 Tap ❤️ for more opportunities!
#MachineLearning #DataScience #GenAI #DeepTech #Startup #AI
❤4