Python Code to remove Image Background
—————————————————————-
from rembg import remove
from PIL import Image

image_path = 'Image Name' ## ---> Change to Image name

output_image = 'ImageNew' ## ---> Change to new name your image

input = Image.open(image_path)

output = remove(input)

output.save(output_image)

Top 7 FREE Courses By Udacity 👇👇

Introduction to Python Programming

Intro to Java: Functional Programming

SQL for Data Analysis

Intro to Data Analysis

Developing Android Apps with Kotlin

Intro to JavaScript

Intro to Machine Learning

Please give us credits while sharing: -> https://t.iss.one/free4unow_backup

ENJOY LEARNING 👍👍

Work Smarter 💪

Coding and Aptitude Round before interview

Coding challenges are meant to test your coding skills (especially if you are applying for ML engineer role). The coding challenges can contain algorithm and data structures problems of varying difficulty. These challenges will be timed based on how complicated the questions are. These are intended to test your basic algorithmic thinking.
Sometimes, a complicated data science question like making predictions based on twitter data are also given. These challenges are hosted on HackerRank, HackerEarth, CoderByte etc. In addition, you may even be asked multiple-choice questions on the fundamentals of data science and statistics. This round is meant to be a filtering round where candidates whose fundamentals are little shaky are eliminated. These rounds are typically conducted without any manual intervention, so it is important to be well prepared for this round.

Sometimes a separate Aptitude test is conducted or along with the technical round an aptitude test is also conducted to assess your aptitude skills. A Data Scientist is expected to have a good aptitude as this field is continuously evolving and a Data Scientist encounters new challenges every day. If you have appeared for GMAT / GRE or CAT, this should be easy for you.

Resources for Prep:

For algorithms and data structures prep,Leetcode and Hackerrank are good resources.

For aptitude prep, you can refer to IndiaBixand Practice Aptitude.

With respect to data science challenges, practice well on GLabs and Kaggle.

Brilliant is an excellent resource for tricky math and statistics questions.

For practising SQL, SQL Zoo and Mode Analytics are good resources that allow you to solve the exercises in the browser itself.

Things to Note:

Ensure that you are calm and relaxed before you attempt to answer the challenge. Read through all the questions before you start attempting the same. Let your mind go into problem-solving mode before your fingers do!

In case, you are finished with the test before time, recheck your answers and then submit.

Sometimes these rounds don’t go your way, you might have had a brain fade, it was not your day etc. Don’t worry! Shake if off for there is always a next time and this is not the end of the world.

🚨30 FREE Dataset Sources for Data Science Projects🔥

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

FBI’s 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

4 Projects to Add to Your Resume:

1. To-Do List App
2. E-commerce Product Catalog
3. Weather App
4. GitHub Repository Viewer

𝗧𝗼𝗽 𝟭𝟱 𝗚𝗮𝗺𝗲 𝗗𝗲𝘃 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀👾🎮

1. C++: AAA games (Unreal)
2. C#: Unity, indie game
3. JavaScript: Web game
4. Java: Android game
5. Python: Prototypes (Pygame)
6. Lua: Scripting (Roblox)
7. Swift: iOS games
8. Objective-C: Legacy iOS/macOS
9. Rust: System-level (Amethyst)
10. Go: Multiplayer servers
11. HTML5 + JS: Simple 2D games
12. Kotlin: Android apps
13. Haxe: Cross-platform 2D
14. TypeScript: Scalable web games
15. Ruby: Lightweight 2D games

Java Developer Interview ❤
It'll gonna be super helpful for YOU

𝗧𝗼𝗽𝗶𝗰 𝟭: 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗳𝗹𝗼𝘄 𝗮𝗻𝗱 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲
- Please tell me about your project and its architecture, Challenges faced?
- What was your role in the project? Tech Stack of project? why this stack?
- Problem you solved during the project? How collaboration within the team?
- What lessons did you learn from working on this project?
- If you could go back, what would you do differently in this project?

𝗧𝗼𝗽𝗶𝗰 𝟮: 𝗖𝗼𝗿𝗲 𝗝𝗮𝘃𝗮
- String Concepts/Hashcode- Equal Methods
- Immutability
- OOPS concepts
- Serialization
- Collection Framework
- Exception Handling
- Multithreading
- Java Memory Model
- Garbage collection

𝗧𝗼𝗽𝗶𝗰 𝟯: 𝗝𝗮𝘃𝗮-𝟴/𝗝𝗮𝘃𝗮-𝟭𝟭/𝗝𝗮𝘃𝗮𝟭𝟳
- Java 8 features
- Default/Static methods
- Lambda expression
- Functional interfaces
- Optional API
- Stream API
- Pattern matching
- Text block
- Modules

𝗧𝗼𝗽𝗶𝗰 𝟰: 𝗦𝗽𝗿𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸, 𝗦𝗽𝗿𝗶𝗻𝗴-𝗕𝗼𝗼𝘁, 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲, 𝗮𝗻𝗱 𝗥𝗲𝘀𝘁 𝗔𝗣𝗜
- Dependency Injection/IOC, Spring MVC
- Configuration, Annotations, CRUD
- Bean, Scopes, Profiles, Bean lifecycle
- App context/Bean context
- AOP, Exception Handler, Control Advice
- Security (JWT, Oauth)
- Actuators
- WebFlux and Mono Framework
- HTTP methods
- JPA
- Microservice concepts
- Spring Cloud

𝗧𝗼𝗽𝗶𝗰 𝟱: 𝗛𝗶𝗯𝗲𝗿𝗻𝗮𝘁𝗲/𝗦𝗽𝗿𝗶𝗻𝗴-𝗱𝗮𝘁𝗮 𝗝𝗽𝗮/𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 (𝗦𝗤𝗟 𝗼𝗿 𝗡𝗼𝗦𝗤𝗟)
- JPA Repositories
- Relationship with Entities
- SQL queries on Employee department
- Queries, Highest Nth salary queries
- Relational and No-Relational DB concepts
- CRUD operations in DB
- Joins, indexing, procs, function

𝗧𝗼𝗽𝗶𝗰 𝟲: 𝗖𝗼𝗱𝗶𝗻𝗴
- DSA Related Questions
- Sorting and searching using Java API.
- Stream API coding Questions

𝗧𝗼𝗽𝗶𝗰 𝟳: 𝗗𝗲𝘃𝗼𝗽𝘀 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗼𝗻 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗧𝗼𝗼𝗹𝘀
- These types of topics are mostly asked by managers or leads who are heavily working on it, That's why they may grill you on DevOps/deployment-related tools, You should have an understanding of common tools like Jenkins, Kubernetes, Kafka, Cloud, and all.

𝗧𝗼𝗽𝗶𝗰𝘀 𝟴: 𝗕𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲
- The interviewer always wanted to ask about some design patterns, it may be Normal design patterns like singleton, factory, or observer patterns to know that you can use these in coding.

Make sure to scroll through the above messages 💝 definitely you will get the more interesting things 🤠

All the best 👍👍

Python Project Ideas

💻 Want to Clear Your Next Java Developer Interview?

Prepare these topics to ace your next Java interview! 🚀

𝐓𝐨𝐩𝐢𝐜 𝟏: 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐅𝐥𝐨𝐰 𝐚𝐧𝐝 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞
🔹 Describe your project and its architecture.
🔹 Challenges faced and your role in the project.
🔹 Tech stack used and the reasoning behind it.
🔹 Problem-solving, collaboration, and lessons learned.
🔹 Reflect on what you'd do differently.

𝐓𝐨𝐩𝐢𝐜 𝟐: 𝐂𝐨𝐫𝐞 𝐉𝐚𝐯𝐚
🔹 String concepts (hashcode, equals).
🔹 Immutability, OOPS concepts.
🔹 Serialization, Collection Framework.
🔹 Exception handling, multithreading.
🔹 Java Memory Model, garbage collection.

𝐓𝐨𝐩𝐢𝐜 𝟑: 𝐉𝐚𝐯𝐚 𝟖/𝟏𝟏/𝟏𝟕 Features
🔹 Java 8 features: Lambda expressions, Stream API, Optional API.
🔹 Functional interfaces, default/static methods.
🔹 Pattern matching, text blocks, modules.

𝐓𝐨𝐩𝐢𝐜 𝟒: 𝐒𝐩𝐫𝐢𝐧𝐠 & 𝐒𝐩𝐫𝐢𝐧𝐠 𝐁𝐨𝐨𝐭
🔹 Dependency Injection, IOC, Spring MVC.
🔹 Bean lifecycle, scopes, profiles.
🔹 REST API, CRUD, AOP, Exception handling.
🔹 JWT, OAuth, actuators, WebFlux.
🔹 Microservices, Spring Cloud, JPA.

𝐓𝐨𝐩𝐢𝐜 𝟓: 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 (𝐒𝐐𝐋/𝐍𝐨𝐒𝐐𝐋)
🔹 JPA repositories, entity relationships.
🔹 SQL queries (e.g., Nth highest salary).
🔹 Relational and non-relational DB concepts.
🔹 Joins, indexing, stored procedures.

𝐓𝐨𝐩𝐢𝐜 𝟔: 𝐂𝐨𝐝𝐢𝐧𝐠
🔹 DSA-based questions.
🔹 Sorting/searching with Java APIs.
🔹 Stream API coding questions.

𝐓𝐨𝐩𝐢𝐜 𝟕: 𝐃𝐞𝐯𝐎𝐩𝐬 & 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭
🔹 Familiarize yourself with tools like Jenkins, Kubernetes, Kafka, and cloud technologies.

𝐓𝐨𝐩𝐢𝐜 𝟖: 𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬
🔹 Master design patterns like singleton, factory, observer, etc.

✨ Nail your interview with confidence! ✨

#JavaInterview #JavaDeveloper