Are you looking to become a machine learning engineer? The algorithm brought you to the right place! ๐
I created a free and comprehensive roadmap. Let's go through this thread and explore what you need to know to become an expert machine learning engineer:
Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.
Here are the probability units you will need to focus on:
Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra
Python:
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking
Machine Learning Prerequisites:
Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data
Machine Learning Fundamentals
Using scikit-learn library in combination with other Python libraries for:
Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)
Solving two types of problems:
Regression
Classification
Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.
Types of Neural Networks:
Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.
In Python, itโs the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.
Deep Learning:
Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models
Machine Learning Project Deployment
Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:
Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
I created a free and comprehensive roadmap. Let's go through this thread and explore what you need to know to become an expert machine learning engineer:
Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.
Here are the probability units you will need to focus on:
Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra
Python:
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.
Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking
Machine Learning Prerequisites:
Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data
Machine Learning Fundamentals
Using scikit-learn library in combination with other Python libraries for:
Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)
Solving two types of problems:
Regression
Classification
Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.
Types of Neural Networks:
Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.
In Python, itโs the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.
Deep Learning:
Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models
Machine Learning Project Deployment
Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:
Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
๐10โค6
Here is how you can explain your project in an interview
When youโre in an interview, itโs super important to know how to talk about your projects in a way that impresses the interviewer. Here are some key points to help you do just that:
โค ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐ข๐๐ฒ๐ฟ๐๐ถ๐ฒ๐:
- Start with a quick summary of the project you worked on. What was it all about? What were the main goals? Keep it short and sweet something you can explain in about 30 seconds.
โค ๐ฃ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ ๐ฆ๐๐ฎ๐๐ฒ๐บ๐ฒ๐ป๐:
- What problem were you trying to solve with this project? Explain why this problem was important and needed addressing.
โค ๐ฃ๐ฟ๐ผ๐ฝ๐ผ๐๐ฒ๐ฑ ๐ฆ๐ผ๐น๐๐๐ถ๐ผ๐ป:
- Describe the solution you came up with. How does it work, and why is it a good fix for the problem?
โค ๐ฌ๐ผ๐๐ฟ ๐ฅ๐ผ๐น๐ฒ:
- Talk about what you specifically did. What were your main tasks? Did you face any challenges, and how did you overcome them? Make sure itโs clear whether you were leading the project, a key player, or supporting the team.
โค ๐ง๐ฒ๐ฐ๐ต๐ป๐ผ๐น๐ผ๐ด๐ถ๐ฒ๐ ๐ฎ๐ป๐ฑ ๐ง๐ผ๐ผ๐น๐:
- Mention the tech and tools you used. This shows your technical know-how and your ability to choose the right tools for the job.
โค ๐๐บ๐ฝ๐ฎ๐ฐ๐ ๐ฎ๐ป๐ฑ ๐๐ฐ๐ต๐ถ๐ฒ๐๐ฒ๐บ๐ฒ๐ป๐๐:
- Share the results of your project. Did it make things better? How? Mention any improvements, efficiencies, or positive feedback you got. This helps show the project was a success and highlights your contribution.
โค ๐ง๐ฒ๐ฎ๐บ ๐๐ผ๐น๐น๐ฎ๐ฏ๐ผ๐ฟ๐ฎ๐๐ถ๐ผ๐ป:
- If you worked with a team, talk about how you collaborated. What was your role in the team? How did you communicate and contribute to the teamโs success?
โค ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐:
- Reflect on what you learned from the project. How did it help you grow professionally? What new skills did you gain, and what would you do differently next time?
โค ๐ง๐ถ๐ฝ๐ ๐ณ๐ผ๐ฟ ๐ฌ๐ผ๐๐ฟ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ฃ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ฎ๐๐ถ๐ผ๐ป:
- Be ready with a 30 second elevator pitch about your projects, and also have a five-minute detailed overview ready.
- Know why you chose the project, what your role was, what decisions you made, and how the results compared to what you expected.
- Be clear on the scope of the project whether it was a long-term effort or a quick task.
- If thereโs a pause after you describe the project, donโt hesitate to ask if theyโd like more details or if thereโs a specific part theyโre interested in.
Remember, ๐ฐ๐ผ๐บ๐บ๐๐ป๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐ถ๐ ๐ธ๐ฒ๐. You might have done great work, but if you donโt explain it well, itโs hard for the interviewer to understand your impact. So, practice explaining your projects with clarity.
When youโre in an interview, itโs super important to know how to talk about your projects in a way that impresses the interviewer. Here are some key points to help you do just that:
โค ๐ฃ๐ฟ๐ผ๐ท๐ฒ๐ฐ๐ ๐ข๐๐ฒ๐ฟ๐๐ถ๐ฒ๐:
- Start with a quick summary of the project you worked on. What was it all about? What were the main goals? Keep it short and sweet something you can explain in about 30 seconds.
โค ๐ฃ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ ๐ฆ๐๐ฎ๐๐ฒ๐บ๐ฒ๐ป๐:
- What problem were you trying to solve with this project? Explain why this problem was important and needed addressing.
โค ๐ฃ๐ฟ๐ผ๐ฝ๐ผ๐๐ฒ๐ฑ ๐ฆ๐ผ๐น๐๐๐ถ๐ผ๐ป:
- Describe the solution you came up with. How does it work, and why is it a good fix for the problem?
โค ๐ฌ๐ผ๐๐ฟ ๐ฅ๐ผ๐น๐ฒ:
- Talk about what you specifically did. What were your main tasks? Did you face any challenges, and how did you overcome them? Make sure itโs clear whether you were leading the project, a key player, or supporting the team.
โค ๐ง๐ฒ๐ฐ๐ต๐ป๐ผ๐น๐ผ๐ด๐ถ๐ฒ๐ ๐ฎ๐ป๐ฑ ๐ง๐ผ๐ผ๐น๐:
- Mention the tech and tools you used. This shows your technical know-how and your ability to choose the right tools for the job.
โค ๐๐บ๐ฝ๐ฎ๐ฐ๐ ๐ฎ๐ป๐ฑ ๐๐ฐ๐ต๐ถ๐ฒ๐๐ฒ๐บ๐ฒ๐ป๐๐:
- Share the results of your project. Did it make things better? How? Mention any improvements, efficiencies, or positive feedback you got. This helps show the project was a success and highlights your contribution.
โค ๐ง๐ฒ๐ฎ๐บ ๐๐ผ๐น๐น๐ฎ๐ฏ๐ผ๐ฟ๐ฎ๐๐ถ๐ผ๐ป:
- If you worked with a team, talk about how you collaborated. What was your role in the team? How did you communicate and contribute to the teamโs success?
โค ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐:
- Reflect on what you learned from the project. How did it help you grow professionally? What new skills did you gain, and what would you do differently next time?
โค ๐ง๐ถ๐ฝ๐ ๐ณ๐ผ๐ฟ ๐ฌ๐ผ๐๐ฟ ๐๐ป๐๐ฒ๐ฟ๐๐ถ๐ฒ๐ ๐ฃ๐ฟ๐ฒ๐ฝ๐ฎ๐ฟ๐ฎ๐๐ถ๐ผ๐ป:
- Be ready with a 30 second elevator pitch about your projects, and also have a five-minute detailed overview ready.
- Know why you chose the project, what your role was, what decisions you made, and how the results compared to what you expected.
- Be clear on the scope of the project whether it was a long-term effort or a quick task.
- If thereโs a pause after you describe the project, donโt hesitate to ask if theyโd like more details or if thereโs a specific part theyโre interested in.
Remember, ๐ฐ๐ผ๐บ๐บ๐๐ป๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐ถ๐ ๐ธ๐ฒ๐. You might have done great work, but if you donโt explain it well, itโs hard for the interviewer to understand your impact. So, practice explaining your projects with clarity.
๐13โค5
1ยณ+2ยณ+3ยณ+4ยณ+5ยณ+6ยณ+7ยณ+8ยณ+9ยณ
= 2025 ๐ Happy New Year ๐ฅณ
= 2025 ๐ Happy New Year ๐ฅณ
โค51๐7
๐ Embark on a Journey of Discovery and Innovation with @DeepLearning_ai! and @MachineLearning_Programming ๐
What We Offer:
* ๐ง Deep Dives into AI & ML.
* ๐ค Latest in Deep Learning.
* ๐ Data Science Mastery.
* ๐ Computer Vision & Image Processing.
* ๐ Exclusive Access to Research Papers.
Why Us?
* Connect with experts and enthusiasts.
* Stay updated, stay ahead.
* Empower your knowledge and career in tech.
Ready for a deep dive? Click here to explore, learn, and grow with
@DeepLearning_ai
@MachineLearning_Programming!
Step into the futureโtoday.
What We Offer:
* ๐ง Deep Dives into AI & ML.
* ๐ค Latest in Deep Learning.
* ๐ Data Science Mastery.
* ๐ Computer Vision & Image Processing.
* ๐ Exclusive Access to Research Papers.
Why Us?
* Connect with experts and enthusiasts.
* Stay updated, stay ahead.
* Empower your knowledge and career in tech.
Ready for a deep dive? Click here to explore, learn, and grow with
@DeepLearning_ai
@MachineLearning_Programming!
Step into the futureโtoday.
Forwarded from Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert
In every family tree, there is 1 person who breaks out the middle-class chain and works hard to become a millionaire and changes the lives of everyone forever.
May that be you in 2025.
Happy New Year!
May that be you in 2025.
Happy New Year!
โค53
Complete Roadmap to become a data scientist in 5 months
Free Resources to learn Data Science: https://t.iss.one/datasciencefun
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 and Visualization
- Day 11-15: Pandas for data manipulation.
- Day 16-20: Data visualization with Matplotlib and Seaborn.
Week 5-6: Machine Learning Foundations
- Day 21-25: Introduction to scikit-learn.
- Day 26-30: Linear regression and logistic regression.
Work on Data Science Projects: https://t.iss.one/pythonspecialist/29
Week 7-8: Advanced Machine Learning
- Day 31-35: Decision trees and random forests.
- Day 36-40: Clustering (K-Means, DBSCAN) and dimensionality reduction.
Week 9-10: Deep Learning
- Day 41-45: Basics of Neural Networks and TensorFlow/Keras.
- Day 46-50: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Week 11-12: Data Engineering
- Day 51-55: Learn about SQL and databases.
- Day 56-60: Data preprocessing and cleaning.
Week 13-14: Model Evaluation and Optimization
- Day 61-65: Cross-validation, hyperparameter tuning.
- Day 66-70: Evaluation metrics (accuracy, precision, recall, F1-score).
Week 15-16: Big Data and Tools
- Day 71-75: Introduction to big data technologies (Hadoop, Spark).
- Day 76-80: Basics of cloud computing (AWS, GCP, Azure).
Week 17-18: Deployment and Production
- Day 81-85: Model deployment with Flask or FastAPI.
- Day 86-90: Containerization with Docker, cloud deployment (AWS, Heroku).
Week 19-20: Specialization
- Day 91-95: NLP or Computer Vision, based on your interests.
Week 21-22: Projects and Portfolios
- Day 96-100: Work on personal data science projects.
Week 23-24: Soft Skills and Networking
- Day 101-105: Improve communication and presentation skills.
- Day 106-110: Attend online data science meetups or forums.
Week 25-26: Interview Preparation
- Day 111-115: Practice coding interviews on platforms like LeetCode.
- Day 116-120: Review your projects and be ready to discuss them.
Week 27-28: Apply for Jobs
- Day 121-125: Start applying for entry-level data scientist positions.
Week 29-30: Interviews
- Day 126-130: Attend interviews, practice whiteboard problems.
Week 31-32: Continuous Learning
- Day 131-135: Stay updated with the latest trends in data science.
Week 33-34: Accepting Offers
- Day 136-140: Evaluate job offers and negotiate if necessary.
Week 35-36: Settling In
- Day 141-150: Start your new data science job, adapt to the team, and continue learning on the job.
ENJOY LEARNING ๐๐
Free Resources to learn Data Science: https://t.iss.one/datasciencefun
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 and Visualization
- Day 11-15: Pandas for data manipulation.
- Day 16-20: Data visualization with Matplotlib and Seaborn.
Week 5-6: Machine Learning Foundations
- Day 21-25: Introduction to scikit-learn.
- Day 26-30: Linear regression and logistic regression.
Work on Data Science Projects: https://t.iss.one/pythonspecialist/29
Week 7-8: Advanced Machine Learning
- Day 31-35: Decision trees and random forests.
- Day 36-40: Clustering (K-Means, DBSCAN) and dimensionality reduction.
Week 9-10: Deep Learning
- Day 41-45: Basics of Neural Networks and TensorFlow/Keras.
- Day 46-50: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Week 11-12: Data Engineering
- Day 51-55: Learn about SQL and databases.
- Day 56-60: Data preprocessing and cleaning.
Week 13-14: Model Evaluation and Optimization
- Day 61-65: Cross-validation, hyperparameter tuning.
- Day 66-70: Evaluation metrics (accuracy, precision, recall, F1-score).
Week 15-16: Big Data and Tools
- Day 71-75: Introduction to big data technologies (Hadoop, Spark).
- Day 76-80: Basics of cloud computing (AWS, GCP, Azure).
Week 17-18: Deployment and Production
- Day 81-85: Model deployment with Flask or FastAPI.
- Day 86-90: Containerization with Docker, cloud deployment (AWS, Heroku).
Week 19-20: Specialization
- Day 91-95: NLP or Computer Vision, based on your interests.
Week 21-22: Projects and Portfolios
- Day 96-100: Work on personal data science projects.
Week 23-24: Soft Skills and Networking
- Day 101-105: Improve communication and presentation skills.
- Day 106-110: Attend online data science meetups or forums.
Week 25-26: Interview Preparation
- Day 111-115: Practice coding interviews on platforms like LeetCode.
- Day 116-120: Review your projects and be ready to discuss them.
Week 27-28: Apply for Jobs
- Day 121-125: Start applying for entry-level data scientist positions.
Week 29-30: Interviews
- Day 126-130: Attend interviews, practice whiteboard problems.
Week 31-32: Continuous Learning
- Day 131-135: Stay updated with the latest trends in data science.
Week 33-34: Accepting Offers
- Day 136-140: Evaluate job offers and negotiate if necessary.
Week 35-36: Settling In
- Day 141-150: Start your new data science job, adapt to the team, and continue learning on the job.
ENJOY LEARNING ๐๐
๐18โค7
๐จ30 FREE Dataset Sources for Data Science Projects๐ฅ
Data Simplifier: https://datasimplifier.com/best-data-analyst-projects-for-freshers/
US Government Dataset: https://www.data.gov/
Open Government Data (OGD) Platform India: https://data.gov.in/
The World Bank Open Data: https://data.worldbank.org/
Data World: https://data.world/
BFI - Industry Data and Insights: https://www.bfi.org.uk/data-statistics
The Humanitarian Data Exchange (HDX): https://data.humdata.org/
Data at World Health Organization (WHO): https://www.who.int/data
FBIโs Crime Data Explorer: https://crime-data-explorer.fr.cloud.gov/
AWS Open Data Registry: https://registry.opendata.aws/
FiveThirtyEight: https://data.fivethirtyeight.com/
IMDb Datasets: https://www.imdb.com/interfaces/
Kaggle: https://www.kaggle.com/datasets
UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/index.php
Google Dataset Search: https://datasetsearch.research.google.com/
Nasdaq Data Link: https://data.nasdaq.com/
Recommender Systems and Personalization Datasets: https://cseweb.ucsd.edu/~jmcauley/datasets.html
Reddit - Datasets: https://www.reddit.com/r/datasets/
Open Data Network by Socrata: https://www.opendatanetwork.com/
Climate Data Online by NOAA: https://www.ncdc.noaa.gov/cdo-web/
Azure Open Datasets: https://azure.microsoft.com/en-us/services/open-datasets/
IEEE Data Port: https://ieee-dataport.org/
Wikipedia: Database: https://dumps.wikimedia.org/
BuzzFeed News: https://github.com/BuzzFeedNews/everything
Academic Torrents: https://academictorrents.com/
Yelp Open Dataset: https://www.yelp.com/dataset
The NLP Index by Quantum Stat: https://index.quantumstat.com/
Computer Vision Online: https://www.computervisiononline.com/dataset
Visual Data Discovery: https://www.visualdata.io/
Roboflow Public Datasets: https://public.roboflow.com/
Computer Vision Group, TUM: https://vision.in.tum.de/data/datasets
Data Simplifier: https://datasimplifier.com/best-data-analyst-projects-for-freshers/
US Government Dataset: https://www.data.gov/
Open Government Data (OGD) Platform India: https://data.gov.in/
The World Bank Open Data: https://data.worldbank.org/
Data World: https://data.world/
BFI - Industry Data and Insights: https://www.bfi.org.uk/data-statistics
The Humanitarian Data Exchange (HDX): https://data.humdata.org/
Data at World Health Organization (WHO): https://www.who.int/data
FBIโs Crime Data Explorer: https://crime-data-explorer.fr.cloud.gov/
AWS Open Data Registry: https://registry.opendata.aws/
FiveThirtyEight: https://data.fivethirtyeight.com/
IMDb Datasets: https://www.imdb.com/interfaces/
Kaggle: https://www.kaggle.com/datasets
UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/index.php
Google Dataset Search: https://datasetsearch.research.google.com/
Nasdaq Data Link: https://data.nasdaq.com/
Recommender Systems and Personalization Datasets: https://cseweb.ucsd.edu/~jmcauley/datasets.html
Reddit - Datasets: https://www.reddit.com/r/datasets/
Open Data Network by Socrata: https://www.opendatanetwork.com/
Climate Data Online by NOAA: https://www.ncdc.noaa.gov/cdo-web/
Azure Open Datasets: https://azure.microsoft.com/en-us/services/open-datasets/
IEEE Data Port: https://ieee-dataport.org/
Wikipedia: Database: https://dumps.wikimedia.org/
BuzzFeed News: https://github.com/BuzzFeedNews/everything
Academic Torrents: https://academictorrents.com/
Yelp Open Dataset: https://www.yelp.com/dataset
The NLP Index by Quantum Stat: https://index.quantumstat.com/
Computer Vision Online: https://www.computervisiononline.com/dataset
Visual Data Discovery: https://www.visualdata.io/
Roboflow Public Datasets: https://public.roboflow.com/
Computer Vision Group, TUM: https://vision.in.tum.de/data/datasets
๐14
Data Science Interview Cheat Sheet! ๐ง
1๏ธโฃ Key Concepts
Master statistics, machine learning, and programming basics. Theyโre always top priorities!
2๏ธโฃ Essential Tools
Know your way around Python, SQL, and data visualization platforms like Tableau or Power BI.
3๏ธโฃ Real-World Projects
Be ready to explain your projectsโwhat problem you solved, how you did it, and the results you achieved! ๐
4๏ธโฃ Problem-Solving Skills
Practice coding challenges and case studies.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
1๏ธโฃ Key Concepts
Master statistics, machine learning, and programming basics. Theyโre always top priorities!
2๏ธโฃ Essential Tools
Know your way around Python, SQL, and data visualization platforms like Tableau or Power BI.
3๏ธโฃ Real-World Projects
Be ready to explain your projectsโwhat problem you solved, how you did it, and the results you achieved! ๐
4๏ธโฃ Problem-Solving Skills
Practice coding challenges and case studies.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING ๐๐
๐8โค2
Industry Data Science vs Academia Data Science
Comparing Data Science in academia and Data Science in industry is like comparing tennis with table tennis: they sound similar but in the end, they are completely different!
5 big differences between Data Science in academia and in industry ๐:
1๏ธโฃ Model vs Data: Academia focuses on models, industry focuses on data. In academia, itโs all about trying to find the best model architecture to optimise a defined metric. In industry, loading and processing the data accounts for around 80% of the job.
2๏ธโฃ Novelty vs Efficiency: The end goal of academia is often to publish a paper and to do so, you will need to find and implement a novel approach. Industry is all about efficiency: reusing existing models as much as possible and applying them to your use case.
3๏ธโฃ Complex vs Simple: More often than not, academia requires complex solutions. I know that this isnโt always the case but unfortunately, complex papers get a higher chance of being accepted at top conferences. In industry, itโs all about simplicity: trying to find the simplest solution that solves a specific problem.
4๏ธโฃ Theory vs Engineering: To succeed in academia, you need to have strong theoretical and maths skills. To succeed in industry, you need to develop strong engineering skills. It is great to be able to train a model in a notebook but if you cannot deploy your model in production, it will be completely useless.
5๏ธโฃ Knowledge impact vs $ impact: In academia, itโs all about creating new work and expanding human knowledge. In industry, it is all about using data to drive value and increase revenue.
Comparing Data Science in academia and Data Science in industry is like comparing tennis with table tennis: they sound similar but in the end, they are completely different!
5 big differences between Data Science in academia and in industry ๐:
1๏ธโฃ Model vs Data: Academia focuses on models, industry focuses on data. In academia, itโs all about trying to find the best model architecture to optimise a defined metric. In industry, loading and processing the data accounts for around 80% of the job.
2๏ธโฃ Novelty vs Efficiency: The end goal of academia is often to publish a paper and to do so, you will need to find and implement a novel approach. Industry is all about efficiency: reusing existing models as much as possible and applying them to your use case.
3๏ธโฃ Complex vs Simple: More often than not, academia requires complex solutions. I know that this isnโt always the case but unfortunately, complex papers get a higher chance of being accepted at top conferences. In industry, itโs all about simplicity: trying to find the simplest solution that solves a specific problem.
4๏ธโฃ Theory vs Engineering: To succeed in academia, you need to have strong theoretical and maths skills. To succeed in industry, you need to develop strong engineering skills. It is great to be able to train a model in a notebook but if you cannot deploy your model in production, it will be completely useless.
5๏ธโฃ Knowledge impact vs $ impact: In academia, itโs all about creating new work and expanding human knowledge. In industry, it is all about using data to drive value and increase revenue.
๐13๐2
Who is Data Scientist?
He/she is responsible for collecting, analyzing and interpreting the results, through a large amount of data. This process is used to take an important decision for the business, which can affect the growth and help to face compititon in the market.
A data scientist analyzes data to extract actionable insight from it. More specifically, a data scientist:
Determines correct datasets and variables.
Identifies the most challenging data-analytics problems.
Collects large sets of data- structured and unstructured, from different sources.
Cleans and validates data ensuring accuracy, completeness, and uniformity.
Builds and applies models and algorithms to mine stores of big data.
Analyzes data to recognize patterns and trends.
Interprets data to find solutions.
Communicates findings to stakeholders using tools like visualization.
He/she is responsible for collecting, analyzing and interpreting the results, through a large amount of data. This process is used to take an important decision for the business, which can affect the growth and help to face compititon in the market.
A data scientist analyzes data to extract actionable insight from it. More specifically, a data scientist:
Determines correct datasets and variables.
Identifies the most challenging data-analytics problems.
Collects large sets of data- structured and unstructured, from different sources.
Cleans and validates data ensuring accuracy, completeness, and uniformity.
Builds and applies models and algorithms to mine stores of big data.
Analyzes data to recognize patterns and trends.
Interprets data to find solutions.
Communicates findings to stakeholders using tools like visualization.
๐2
๐๐ฒ๐ฎ๐ฟ๐ป ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ (๐ก๐ผ ๐ฆ๐๐ฟ๐ถ๐ป๐ด๐ ๐๐๐๐ฎ๐ฐ๐ต๐ฒ๐ฑ)
๐ก๐ผ ๐ณ๐ฎ๐ป๐ฐ๐ ๐ฐ๐ผ๐๐ฟ๐๐ฒ๐, ๐ป๐ผ ๐ฐ๐ผ๐ป๐ฑ๐ถ๐๐ถ๐ผ๐ป๐, ๐ท๐๐๐ ๐ฝ๐๐ฟ๐ฒ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด.
๐๐ฒ๐ฟ๐ฒโ๐ ๐ต๐ผ๐ ๐๐ผ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐:
1๏ธโฃ Python Programming for Data Science โ Harvardโs CS50P
The best intro to Python for absolute beginners:
โฌ Covers loops, data structures, and practical exercises.
โฌ Designed to help you build foundational coding skills.
Link: https://cs50.harvard.edu/python/
https://t.iss.one/datasciencefun
2๏ธโฃ Statistics & Probability โ Khan Academy
Want to master probability, distributions, and hypothesis testing? This is where to start:
โฌ Clear, beginner-friendly videos.
โฌ Exercises to test your skills.
Link: https://www.khanacademy.org/math/statistics-probability
https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O
3๏ธโฃ Linear Algebra for Data Science โ 3Blue1Brown
โฌ Learn about matrices, vectors, and transformations.
โฌ Essential for machine learning models.
Link: https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9KzVk3AjplI5PYPxkUr
4๏ธโฃ SQL Basics โ Mode Analytics
SQL is the backbone of data manipulation. This tutorial covers:
โฌ Writing queries, joins, and filtering data.
โฌ Real-world datasets to practice.
Link: https://mode.com/sql-tutorial
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
5๏ธโฃ Data Visualization โ freeCodeCamp
Learn to create stunning visualizations using Python libraries:
โฌ Covers Matplotlib, Seaborn, and Plotly.
โฌ Step-by-step projects included.
Link: https://www.youtube.com/watch?v=JLzTJhC2DZg
https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34
6๏ธโฃ Machine Learning Basics โ Googleโs Machine Learning Crash Course
An in-depth introduction to machine learning for beginners:
โฌ Learn supervised and unsupervised learning.
โฌ Hands-on coding with TensorFlow.
Link: https://developers.google.com/machine-learning/crash-course
7๏ธโฃ Deep Learning โ Fast.aiโs Free Course
Fast.ai makes deep learning easy and accessible:
โฌ Build neural networks with PyTorch.
โฌ Learn by coding real projects.
Link: https://course.fast.ai/
8๏ธโฃ Data Science Projects โ Kaggle
โฌ Compete in challenges to practice your skills.
โฌ Great way to build your portfolio.
Link: https://www.kaggle.com/
๐ก๐ผ ๐ณ๐ฎ๐ป๐ฐ๐ ๐ฐ๐ผ๐๐ฟ๐๐ฒ๐, ๐ป๐ผ ๐ฐ๐ผ๐ป๐ฑ๐ถ๐๐ถ๐ผ๐ป๐, ๐ท๐๐๐ ๐ฝ๐๐ฟ๐ฒ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด.
๐๐ฒ๐ฟ๐ฒโ๐ ๐ต๐ผ๐ ๐๐ผ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐:
1๏ธโฃ Python Programming for Data Science โ Harvardโs CS50P
The best intro to Python for absolute beginners:
โฌ Covers loops, data structures, and practical exercises.
โฌ Designed to help you build foundational coding skills.
Link: https://cs50.harvard.edu/python/
https://t.iss.one/datasciencefun
2๏ธโฃ Statistics & Probability โ Khan Academy
Want to master probability, distributions, and hypothesis testing? This is where to start:
โฌ Clear, beginner-friendly videos.
โฌ Exercises to test your skills.
Link: https://www.khanacademy.org/math/statistics-probability
https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O
3๏ธโฃ Linear Algebra for Data Science โ 3Blue1Brown
โฌ Learn about matrices, vectors, and transformations.
โฌ Essential for machine learning models.
Link: https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9KzVk3AjplI5PYPxkUr
4๏ธโฃ SQL Basics โ Mode Analytics
SQL is the backbone of data manipulation. This tutorial covers:
โฌ Writing queries, joins, and filtering data.
โฌ Real-world datasets to practice.
Link: https://mode.com/sql-tutorial
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
5๏ธโฃ Data Visualization โ freeCodeCamp
Learn to create stunning visualizations using Python libraries:
โฌ Covers Matplotlib, Seaborn, and Plotly.
โฌ Step-by-step projects included.
Link: https://www.youtube.com/watch?v=JLzTJhC2DZg
https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34
6๏ธโฃ Machine Learning Basics โ Googleโs Machine Learning Crash Course
An in-depth introduction to machine learning for beginners:
โฌ Learn supervised and unsupervised learning.
โฌ Hands-on coding with TensorFlow.
Link: https://developers.google.com/machine-learning/crash-course
7๏ธโฃ Deep Learning โ Fast.aiโs Free Course
Fast.ai makes deep learning easy and accessible:
โฌ Build neural networks with PyTorch.
โฌ Learn by coding real projects.
Link: https://course.fast.ai/
8๏ธโฃ Data Science Projects โ Kaggle
โฌ Compete in challenges to practice your skills.
โฌ Great way to build your portfolio.
Link: https://www.kaggle.com/
๐ฅ7๐6โค4
Top 10 important data science concepts
1. Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in the data science pipeline as it ensures the quality and reliability of the data.
2. Exploratory Data Analysis (EDA): EDA is the process of analyzing and visualizing data to gain insights and understand the underlying patterns and relationships. It involves techniques such as summary statistics, data visualization, and correlation analysis.
3. Feature Engineering: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical variables, and creating interaction terms.
4. Machine Learning Algorithms: Machine learning algorithms are mathematical models that learn patterns and relationships from data to make predictions or decisions. Some important machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
5. Model Evaluation and Validation: Model evaluation and validation involve assessing the performance of machine learning models on unseen data. It includes techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC curve analysis.
6. Feature Selection: Feature selection is the process of selecting the most relevant features from a dataset to improve model performance and reduce overfitting. It involves techniques such as correlation analysis, backward elimination, forward selection, and regularization methods.
7. Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving the most important information. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are common dimensionality reduction techniques.
8. Model Optimization: Model optimization involves fine-tuning the parameters and hyperparameters of machine learning models to achieve the best performance. Techniques such as grid search, random search, and Bayesian optimization are used for model optimization.
9. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It involves using charts, graphs, and plots to present data in a visually appealing and understandable manner.
10. Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets that cannot be processed using traditional data processing techniques. It involves technologies such as Hadoop, Spark, and distributed computing to extract insights from massive amounts of data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
1. Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in the data science pipeline as it ensures the quality and reliability of the data.
2. Exploratory Data Analysis (EDA): EDA is the process of analyzing and visualizing data to gain insights and understand the underlying patterns and relationships. It involves techniques such as summary statistics, data visualization, and correlation analysis.
3. Feature Engineering: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical variables, and creating interaction terms.
4. Machine Learning Algorithms: Machine learning algorithms are mathematical models that learn patterns and relationships from data to make predictions or decisions. Some important machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.
5. Model Evaluation and Validation: Model evaluation and validation involve assessing the performance of machine learning models on unseen data. It includes techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC curve analysis.
6. Feature Selection: Feature selection is the process of selecting the most relevant features from a dataset to improve model performance and reduce overfitting. It involves techniques such as correlation analysis, backward elimination, forward selection, and regularization methods.
7. Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving the most important information. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are common dimensionality reduction techniques.
8. Model Optimization: Model optimization involves fine-tuning the parameters and hyperparameters of machine learning models to achieve the best performance. Techniques such as grid search, random search, and Bayesian optimization are used for model optimization.
9. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It involves using charts, graphs, and plots to present data in a visually appealing and understandable manner.
10. Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets that cannot be processed using traditional data processing techniques. It involves technologies such as Hadoop, Spark, and distributed computing to extract insights from massive amounts of data.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
๐8โค1
๐น Supervised Learning - Key Algorithms ๐น
1๏ธโฃ Linear Regression โ Predicts continuous values by fitting a straight line. (๐ House prices)
2๏ธโฃ Logistic Regression โ Classifies data into categories (yes/no). (๐ฉ Spam detection)
3๏ธโฃ SVM (Support Vector Machine) โ Finds the best boundary to separate classes. (๐ Image classification)
4๏ธโฃ Decision Tree โ Splits data based on conditions to classify. (๐ณ Diagnosing diseases)
5๏ธโฃ Random Forest โ Multiple decision trees combined for accuracy. (๐ฆ Loan predictions)
6๏ธโฃ k-NN (k-Nearest Neighbors) โ Classifies based on the nearest neighbors. (๐ Product recommendations)
7๏ธโฃ Naive Bayes โ Uses probability to classify data. (๐จ Spam filter)
8๏ธโฃ Gradient Boosting โ Combines weak models to build a strong one. (๐ Customer churn prediction)
9๏ธโฃ XGBoost โ Faster and more efficient gradient boosting. (๐ Machine learning competitions)
โจ Key Tip: Choose algorithms based on data type (classification/regression)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
1๏ธโฃ Linear Regression โ Predicts continuous values by fitting a straight line. (๐ House prices)
2๏ธโฃ Logistic Regression โ Classifies data into categories (yes/no). (๐ฉ Spam detection)
3๏ธโฃ SVM (Support Vector Machine) โ Finds the best boundary to separate classes. (๐ Image classification)
4๏ธโฃ Decision Tree โ Splits data based on conditions to classify. (๐ณ Diagnosing diseases)
5๏ธโฃ Random Forest โ Multiple decision trees combined for accuracy. (๐ฆ Loan predictions)
6๏ธโฃ k-NN (k-Nearest Neighbors) โ Classifies based on the nearest neighbors. (๐ Product recommendations)
7๏ธโฃ Naive Bayes โ Uses probability to classify data. (๐จ Spam filter)
8๏ธโฃ Gradient Boosting โ Combines weak models to build a strong one. (๐ Customer churn prediction)
9๏ธโฃ XGBoost โ Faster and more efficient gradient boosting. (๐ Machine learning competitions)
โจ Key Tip: Choose algorithms based on data type (classification/regression)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
๐7โค4๐ฅ1
Did you ever want to boost your resume and career with the help of Artificial Intelligence?
Anonymous Poll
73%
Yes, AI is the future! ๐
20%
Iโm curious about AI opportunities. ๐ค
7%
Not yet, but now Iโm interested.
๐2
Some helpful Data science projects for beginners
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2
Text mining : https://www.kaggle.com/kanncaa1/applying-text-mining
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2
Text mining : https://www.kaggle.com/kanncaa1/applying-text-mining
๐5