Many people reached out to me saying telegram may get banned in their countries. So I've decided to create WhatsApp channels based on your interests 👇👇
Free Courses with Certificate: https://whatsapp.com/channel/0029Vamhzk5JENy1Zg9KmO2g
Jobs & Internship Opportunities:
https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Web Development: https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
Python Free Books & Projects: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Java Resources: https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
Coding Interviews: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
SQL: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Power BI: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
Programming Free Resources: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
Data Science Projects: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Learn Data Science & Machine Learning: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Don’t worry Guys your contact number will stay hidden!
ENJOY LEARNING 👍👍
Free Courses with Certificate: https://whatsapp.com/channel/0029Vamhzk5JENy1Zg9KmO2g
Jobs & Internship Opportunities:
https://whatsapp.com/channel/0029VaI5CV93AzNUiZ5Tt226
Web Development: https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
Python Free Books & Projects: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Java Resources: https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
Coding Interviews: https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
SQL: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
Power BI: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c
Programming Free Resources: https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
Data Science Projects: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Learn Data Science & Machine Learning: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Don’t worry Guys your contact number will stay hidden!
ENJOY LEARNING 👍👍
👍6❤4
System Design Interview Preparation
System Design Interview Books:
Essential reads for understanding system design concepts and interview questions.
Grokking the System Design Interview by Design Guru:
A practical guide to system design with real-world scenarios.
Designing Data-Intensive Applications:
Learn about the architecture of data systems and how to design data-heavy applications.
System Design Interview Books:
Essential reads for understanding system design concepts and interview questions.
Grokking the System Design Interview by Design Guru:
A practical guide to system design with real-world scenarios.
Designing Data-Intensive Applications:
Learn about the architecture of data systems and how to design data-heavy applications.
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Complete roadmap to learn data science in 2024 👇👇
1. Learn the Basics:
- Brush up on your mathematics, especially statistics.
- Familiarize yourself with programming languages like Python or R.
- Understand basic concepts in databases and data manipulation.
2. Programming Proficiency:
- Develop strong programming skills, particularly in Python or R.
- Learn data manipulation libraries (e.g., Pandas) and visualization tools (e.g., Matplotlib, Seaborn).
3. Statistics and Mathematics:
- Deepen your understanding of statistical concepts.
- Explore linear algebra and calculus, especially for machine learning.
4. Data Exploration and Preprocessing:
- Practice exploratory data analysis (EDA) techniques.
- Learn how to handle missing data and outliers.
5. Machine Learning Fundamentals:
- Understand basic machine learning algorithms (e.g., linear regression, decision trees).
- Learn how to evaluate model performance.
6. Advanced Machine Learning:
- Dive into more complex algorithms (e.g., SVM, neural networks).
- Explore ensemble methods and deep learning.
7. Big Data Technologies:
- Familiarize yourself with big data tools like Apache Hadoop and Spark.
- Learn distributed computing concepts.
8. Feature Engineering and Selection:
- Master techniques for creating and selecting relevant features in your data.
9. Model Deployment:
- Understand how to deploy machine learning models to production.
- Explore containerization and cloud services.
10. Version Control and Collaboration:
- Use version control systems like Git.
- Collaborate with others using platforms like GitHub.
11. Stay Updated:
- Keep up with the latest developments in data science and machine learning.
- Participate in online communities, read research papers, and attend conferences.
12. Build a Portfolio:
- Showcase your projects on platforms like GitHub.
- Develop a portfolio demonstrating your skills and expertise.
Best Resources to learn Data Science
Intro to Data Analytics by Udacity
Machine Learning course by Google
Machine Learning with Python
Data Science Interview Questions
Data Science Project ideas
Data Science: Linear Regression Course by Harvard
Machine Learning Interview Questions
Free Datasets for Projects
Please give us credits while sharing: -> https://t.iss.one/free4unow_backup
ENJOY LEARNING 👍👍
1. Learn the Basics:
- Brush up on your mathematics, especially statistics.
- Familiarize yourself with programming languages like Python or R.
- Understand basic concepts in databases and data manipulation.
2. Programming Proficiency:
- Develop strong programming skills, particularly in Python or R.
- Learn data manipulation libraries (e.g., Pandas) and visualization tools (e.g., Matplotlib, Seaborn).
3. Statistics and Mathematics:
- Deepen your understanding of statistical concepts.
- Explore linear algebra and calculus, especially for machine learning.
4. Data Exploration and Preprocessing:
- Practice exploratory data analysis (EDA) techniques.
- Learn how to handle missing data and outliers.
5. Machine Learning Fundamentals:
- Understand basic machine learning algorithms (e.g., linear regression, decision trees).
- Learn how to evaluate model performance.
6. Advanced Machine Learning:
- Dive into more complex algorithms (e.g., SVM, neural networks).
- Explore ensemble methods and deep learning.
7. Big Data Technologies:
- Familiarize yourself with big data tools like Apache Hadoop and Spark.
- Learn distributed computing concepts.
8. Feature Engineering and Selection:
- Master techniques for creating and selecting relevant features in your data.
9. Model Deployment:
- Understand how to deploy machine learning models to production.
- Explore containerization and cloud services.
10. Version Control and Collaboration:
- Use version control systems like Git.
- Collaborate with others using platforms like GitHub.
11. Stay Updated:
- Keep up with the latest developments in data science and machine learning.
- Participate in online communities, read research papers, and attend conferences.
12. Build a Portfolio:
- Showcase your projects on platforms like GitHub.
- Develop a portfolio demonstrating your skills and expertise.
Best Resources to learn Data Science
Intro to Data Analytics by Udacity
Machine Learning course by Google
Machine Learning with Python
Data Science Interview Questions
Data Science Project ideas
Data Science: Linear Regression Course by Harvard
Machine Learning Interview Questions
Free Datasets for Projects
Please give us credits while sharing: -> https://t.iss.one/free4unow_backup
ENJOY LEARNING 👍👍
👍10❤9
The reason you're not feeling motivated is because you don't have a clear goal.
You do have a goal, but it's only that you want to make a lot of money. With just that, you'll only experience FOMO (fear of missing out), not money.
Hard work is your responsibility, but you need to set small and immediate goals. For example, if you're studying DSA, it's not something you can complete in one day. A goal for now should be to master one topic thoroughly until you can solve all medium-level questions, and slowly, you'll crack it.
This is crucial at every stage of life.
Motivation will come when you start achieving small things, and eventually, everything will fall into place one day. ♥️
You do have a goal, but it's only that you want to make a lot of money. With just that, you'll only experience FOMO (fear of missing out), not money.
Hard work is your responsibility, but you need to set small and immediate goals. For example, if you're studying DSA, it's not something you can complete in one day. A goal for now should be to master one topic thoroughly until you can solve all medium-level questions, and slowly, you'll crack it.
This is crucial at every stage of life.
Motivation will come when you start achieving small things, and eventually, everything will fall into place one day. ♥️
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Mastery in programming is not about increasing code complexity. It is about solving increasingly complex problems with simple code.
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Forwarded from Coding Interview Resources
Resume Tips for Freshers.pdf
43.4 KB
Resume Tips for Freshers 😄❤️
👍3
Forwarded from Coding Interview Resources
Python Data Structures and Algorithms.pdf
11.5 MB
Python Data Structures and Algorithms
👍2
Forwarded from Coding Interview Resources
TCS SQL.INTERVIEW QUESTION AND ANSWER .pdf
3.9 MB
TCS SQL Interview Questions
👍4
Forwarded from Coding Interview Resources
Algorithms-JeffE.pdf
23.9 MB
Algorithms
Jeff Erickson, 2019
Jeff Erickson, 2019
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