๐๐ผ๐ผ๐ด๐น๐ฒ ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐๐๐ป
Data analytics is a must-have skill in todayโs digital era, and Google offers exceptional free courses to help you excel
- Google Analytics Certification
- Google Analytics for Power Users
- Advanced Google Analytics
๐๐ข๐ง๐ค ๐:-
https://tinyurl.com/4sc6pupw
Enroll For FREE & Get Certified๐
Data analytics is a must-have skill in todayโs digital era, and Google offers exceptional free courses to help you excel
- Google Analytics Certification
- Google Analytics for Power Users
- Advanced Google Analytics
๐๐ข๐ง๐ค ๐:-
https://tinyurl.com/4sc6pupw
Enroll For FREE & Get Certified๐
๐1
Top IDEs and Editors Used ๐จ๐ปโ๐ป๐๐ก
1. ๐ป VSCode (54% Usage)
2. ๐ IntelliJ IDEA (34% Usage)
3. ๐ Visual Studio (31% Usage)
4. ๐ Vim (11% Usage)
5. ๐ Eclipse (9% Usage)
6. ๐ Sublime Text (5.5% Usage)
7. ๐ PyCharm (5% Usage)
8. ๐ Xcode (4% Usage)
9. ๐ฑ Android Studio (3% Usage)
10. ๐ NetBeans (2% Usage)
11. โ๏ธ Atom (2% Usage)
1. ๐ป VSCode (54% Usage)
2. ๐ IntelliJ IDEA (34% Usage)
3. ๐ Visual Studio (31% Usage)
4. ๐ Vim (11% Usage)
5. ๐ Eclipse (9% Usage)
6. ๐ Sublime Text (5.5% Usage)
7. ๐ PyCharm (5% Usage)
8. ๐ Xcode (4% Usage)
9. ๐ฑ Android Studio (3% Usage)
10. ๐ NetBeans (2% Usage)
11. โ๏ธ Atom (2% Usage)
๐ง๐ผ๐ฝ ๐ฑ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐ ๐๐ป
* Data Science Foundations
* SQL for Data Science
* Python for Data Science
* Introduction to Data Science
* Data Science Projects
๐๐ข๐ง๐ค ๐:-
https://tinyurl.com/yzpdp26d
Enroll For FREE & Get Certified ๐
* Data Science Foundations
* SQL for Data Science
* Python for Data Science
* Introduction to Data Science
* Data Science Projects
๐๐ข๐ง๐ค ๐:-
https://tinyurl.com/yzpdp26d
Enroll For FREE & Get Certified ๐
Harvard CS50 โ Free Computer Science Course (2023 Edition)
Here are the lectures included in this course:
Lecture 0 - Scratch
Lecture 1 - C
Lecture 2 - Arrays
Lecture 3 - Algorithms
Lecture 4 - Memory
Lecture 5 - Data Structures
Lecture 6 - Python
Lecture 7 - SQL
Lecture 8 - HTML, CSS, JavaScript
Lecture 9 - Flask
Lecture 10 - Emoji
Cybersecurity
Link: https://www.freecodecamp.org/news/harvard-university-cs50-computer-science-course-2023/
CS50 from Harvard
http://cs50.harvard.edu/x/2023/certificate/
NVIDIA FREE AI Certification Courses
https://t.me/techpsyche/617
IBM Free Certification Courses
https://tinyurl.com/42nau8jx
More Resources Here
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Here are the lectures included in this course:
Lecture 0 - Scratch
Lecture 1 - C
Lecture 2 - Arrays
Lecture 3 - Algorithms
Lecture 4 - Memory
Lecture 5 - Data Structures
Lecture 6 - Python
Lecture 7 - SQL
Lecture 8 - HTML, CSS, JavaScript
Lecture 9 - Flask
Lecture 10 - Emoji
Cybersecurity
Link: https://www.freecodecamp.org/news/harvard-university-cs50-computer-science-course-2023/
CS50 from Harvard
http://cs50.harvard.edu/x/2023/certificate/
NVIDIA FREE AI Certification Courses
https://t.me/techpsyche/617
IBM Free Certification Courses
https://tinyurl.com/42nau8jx
More Resources Here
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Forwarded from Mobile Dev Resources . Android . iOS . Flutter . Kotlin . Swift . Java . React Native
๐กBuilding a Better Mobile App for Your Startup
โ๏ธ Always prioritize the user experience. Understand the context in which your app will be usedโwhether users are on the move, multitasking, or in a specific environment.
โ๏ธ Simplicity is key. Avoid overwhelming your users with too many features or cluttered interfaces.
โ๏ธ Pay close attention to usability. Ensure that interactive elements are large enough for easy tapping, and provide clear visual cues for actions.
โ๏ธ Test, test, and test again. Get your app into the hands of real users as early as possible. Observe how they interact with your app, and take note of any areas where they stumble or become confused.
โ๏ธ Lastly, remember that design is an iterative process. Be open to making adjustments and refinements based on user feedback and usage data. A well-designed app is not just aesthetically pleasing but also highly functional, intuitive, and tailored to meet the needs of its users.
Mobile Dev Resources: https://t.me/mobiledevresourcestp
โ๏ธ Always prioritize the user experience. Understand the context in which your app will be usedโwhether users are on the move, multitasking, or in a specific environment.
โ๏ธ Simplicity is key. Avoid overwhelming your users with too many features or cluttered interfaces.
โ๏ธ Pay close attention to usability. Ensure that interactive elements are large enough for easy tapping, and provide clear visual cues for actions.
โ๏ธ Test, test, and test again. Get your app into the hands of real users as early as possible. Observe how they interact with your app, and take note of any areas where they stumble or become confused.
โ๏ธ Lastly, remember that design is an iterative process. Be open to making adjustments and refinements based on user feedback and usage data. A well-designed app is not just aesthetically pleasing but also highly functional, intuitive, and tailored to meet the needs of its users.
Mobile Dev Resources: https://t.me/mobiledevresourcestp
Forwarded from Machine Learning Resources TP
Key Concepts for Machine Learning Interviews
1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests.
2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE.
3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand.
4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees.
5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE).
6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization.
7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking.
8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis.
10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods.
11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients.
12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data.
13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment.
14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound.
15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayesโ theorem, prior and posterior distributions, and Bayesian networks.
I have curated the best Data Science & Machine Learning Resources.
๐๐
https://topmate.io/learning_resources/1406977
Like if you need similar content ๐๐
Machine Learning Free Book: https://t.me/mlresourcestp/16
IBM AI/ML Free Courses with Certification: https://tinyurl.com/42nau8jx
ENJOY LEARNING ๐๐
1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests.
2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE.
3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand.
4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees.
5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE).
6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization.
7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking.
8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis.
10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods.
11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients.
12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data.
13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment.
14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound.
15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayesโ theorem, prior and posterior distributions, and Bayesian networks.
I have curated the best Data Science & Machine Learning Resources.
๐๐
https://topmate.io/learning_resources/1406977
Like if you need similar content ๐๐
Machine Learning Free Book: https://t.me/mlresourcestp/16
IBM AI/ML Free Courses with Certification: https://tinyurl.com/42nau8jx
ENJOY LEARNING ๐๐
Here's a good list of cheat sheets for programmers (all free):
Data Science Cheatsheet
https://github.com/aaronwangy/Data-Science-Cheatsheet
SQL Cheatsheet
sqltutorial.org/sql-cheat-sheet
https://t.me/sqlresourcestp/90
https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf
Java Programming Cheatsheet
https://introcs.cs.princeton.edu/java/11cheatsheet/
https://t.me/javaresourcestp/44
Javascript Cheatsheet
quickref.me/javascript.html
https://t.me/javascriptresourcestp/468
Data Analytics Cheatsheets
https://dataanalytics.beehiiv.com/p/data
Python Cheat sheet
quickref.me/python.html
https://t.me/pythonresourcestp/42
GIT Cheatsheet
https://t.me/techpsyche/131
Machine Learning Cheatsheet
https://t.me/mlresourcestp/9
HTML Cheatsheet
https://web.stanford.edu/group/csp/cs21/htmlcheatsheet.pdf
htmlcheatsheet.com
CSS Cheatsheet
htmlcheatsheet.com/css
jQuery Cheatsheet
https://t.me/javascriptresourcestp/462
Join for more free resources
https://t.me/techpsyche
Like for more โค๏ธ
ENJOY LEARNING๐๐
Free entry to our WhatsApp channel
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Data Science Cheatsheet
https://github.com/aaronwangy/Data-Science-Cheatsheet
SQL Cheatsheet
sqltutorial.org/sql-cheat-sheet
https://t.me/sqlresourcestp/90
https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf
Java Programming Cheatsheet
https://introcs.cs.princeton.edu/java/11cheatsheet/
https://t.me/javaresourcestp/44
Javascript Cheatsheet
quickref.me/javascript.html
https://t.me/javascriptresourcestp/468
Data Analytics Cheatsheets
https://dataanalytics.beehiiv.com/p/data
Python Cheat sheet
quickref.me/python.html
https://t.me/pythonresourcestp/42
GIT Cheatsheet
https://t.me/techpsyche/131
Machine Learning Cheatsheet
https://t.me/mlresourcestp/9
HTML Cheatsheet
https://web.stanford.edu/group/csp/cs21/htmlcheatsheet.pdf
htmlcheatsheet.com
CSS Cheatsheet
htmlcheatsheet.com/css
jQuery Cheatsheet
https://t.me/javascriptresourcestp/462
Join for more free resources
https://t.me/techpsyche
Like for more โค๏ธ
ENJOY LEARNING๐๐
Free entry to our WhatsApp channel
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Forwarded from Python Resources TP
How to get job as python fresher?
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, youโre recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once youโll learn several Python web frameworks and other trending technologies.
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, youโre recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once youโll learn several Python web frameworks and other trending technologies.
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
Are crypto transactions anonymous?
Crypto transactions on blockchains are โpseudonymous,โ meaning they can be traced to wallet addresses (via public keys) but have no direct connection with peopleโs identities.
Every transaction is open to the public, and anyone with an internet connection can view them. The date, the amount sent and received, the wallet addresses โ all of this data is impossible to conceal.
However, if you use a non-custodial wallet, it will be impossible to identify you as the walletโs owner (unless you deanonymize yourself).
For example, if you send crypto from a centralized exchange to your non-custodial wallet, the exchange now knows who the non-custodial wallet belongs to since you must pass Know Your Customer requirements by showing your ID.
Therefore, if you practice the basics, you can be completely anonymous on the blockchain, and no one will ever know your personal information.
Cryptocurrency Mining: https://t.me/techpsyche/663
#crypto
Crypto transactions on blockchains are โpseudonymous,โ meaning they can be traced to wallet addresses (via public keys) but have no direct connection with peopleโs identities.
Every transaction is open to the public, and anyone with an internet connection can view them. The date, the amount sent and received, the wallet addresses โ all of this data is impossible to conceal.
However, if you use a non-custodial wallet, it will be impossible to identify you as the walletโs owner (unless you deanonymize yourself).
For example, if you send crypto from a centralized exchange to your non-custodial wallet, the exchange now knows who the non-custodial wallet belongs to since you must pass Know Your Customer requirements by showing your ID.
Therefore, if you practice the basics, you can be completely anonymous on the blockchain, and no one will ever know your personal information.
Cryptocurrency Mining: https://t.me/techpsyche/663
#crypto
๐1
Arduino Uno!
What is Arduino Uno?
Arduino Uno is a microcontroller board based on the ATmega328P, developed by (link unavailable) It's a popular, user-friendly platform for creating interactive electronic projects.
Key Features
1. Microcontroller: ATmega328P, an 8-bit processor with 32 KB of flash memory.
2. Input/Output: 14 digital input/output pins, 6 analog input pins, and a USB connection.
3. Programming: Can be programmed using the Arduino IDE (Integrated Development Environment).
4. Shield Compatibility: Supports various shields, such as Wi-Fi, Ethernet, and motor control shields.
Uses
1. Robotics: Ideal for building robots, robotic arms, and autonomous vehicles.
2. Home Automation: Can be used to control lighting, temperature, and security systems.
3. Wearables: Suitable for creating wearable projects, such as smartwatches and fitness trackers.
4. Prototyping: Great for rapid prototyping and proof-of-concept development.
More Resources
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
#arduino
What is Arduino Uno?
Arduino Uno is a microcontroller board based on the ATmega328P, developed by (link unavailable) It's a popular, user-friendly platform for creating interactive electronic projects.
Key Features
1. Microcontroller: ATmega328P, an 8-bit processor with 32 KB of flash memory.
2. Input/Output: 14 digital input/output pins, 6 analog input pins, and a USB connection.
3. Programming: Can be programmed using the Arduino IDE (Integrated Development Environment).
4. Shield Compatibility: Supports various shields, such as Wi-Fi, Ethernet, and motor control shields.
Uses
1. Robotics: Ideal for building robots, robotic arms, and autonomous vehicles.
2. Home Automation: Can be used to control lighting, temperature, and security systems.
3. Wearables: Suitable for creating wearable projects, such as smartwatches and fitness trackers.
4. Prototyping: Great for rapid prototyping and proof-of-concept development.
More Resources
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
#arduino
๐๐ฒ๐ ๐ฌ๐ผ๐๐ฟ ๐๐ฟ๐ฒ๐ฎ๐บ ๐๐ผ๐ฏ ๐๐ป ๐๐บ๐ฎ๐๐ผ๐ป, ๐๐ผ๐ผ๐ด๐น๐ฒ, ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐, ๐ก๐ฉ๐๐๐๐, ๐ฎ๐ป๐ฑ ๐ ๐ฒ๐๐ฎ (๐๐ฎ๐ฐ๐ฒ๐ฏ๐ผ๐ผ๐ธ) ๐๐ถ๐๐ต ๐๐ต๐ฒ๐๐ฒ ๐ฐ๐ผ๐บ๐ฝ๐ฟ๐ฒ๐ต๐ฒ๐ป๐๐ถ๐๐ฒ ๐ฟ๐ฒ๐๐ผ๐๐ฟ๐ฐ๐ฒ๐ ๐๐ป
1๏ธโฃ Amazon Interviewing Guide
2๏ธโฃ Google Interview Tips
3๏ธโฃ Microsoft Hiring Tips
4๏ธโฃ NVIDIA Hiring Process
5๏ธโฃ Meta Onsite SWE Prep Guide
๐๐ข๐ง๐ค๐:-
https://tinyurl.com/3rj868rf
Crack Interview & Get Your Dream Job In Top MNCs
1๏ธโฃ Amazon Interviewing Guide
2๏ธโฃ Google Interview Tips
3๏ธโฃ Microsoft Hiring Tips
4๏ธโฃ NVIDIA Hiring Process
5๏ธโฃ Meta Onsite SWE Prep Guide
๐๐ข๐ง๐ค๐:-
https://tinyurl.com/3rj868rf
Crack Interview & Get Your Dream Job In Top MNCs
Forwarded from Free Courses: Google | Microsoft | Udemy | Coursera | IBM | NVIDIA | LinkedIn Learning | MIT | Udemy Coupons & PDF Books
30th ๐ฅ๏ธ March 2025 Free Udemy Coupons New Coupons Added
โโโโโโโโโโโโโโโโโโโโโ
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โโโโโโโโโโโโโโโโโโโโโ
#01 Clustering & Unsupervised Learning in Python
https://techurl.in/voaMZ
#02 The Complete Django Rest Framework Bootcamp
https://techurl.in/tEuDI
#03 Python Microservices: Build, Scale, and Deploy like a Pro!
https://techurl.in/KKxgQ
#04 Python OOP: A Complete Course in Object Oriented Programming
https://techurl.in/mKPVw
#05 HTML 5,Python,Django And Flask Framework Full-Stack Course
https://techurl.in/LCKGu
#06 NumPy, Pandas, & Python for Data Analysis: A Complete Guide
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#07 Data-Centric Machine Learning with Python: Hands-On Guide
https://techurl.in/hXuUS
#08 Python & Java: Master Backend & Frontend Web Developments
https://techurl.in/VzORW
#09 Numpy For Data Science - Real Time Experience
https://techurl.in/CnbTn
#10 Python For Data Science - Real Time Experience
https://techurl.in/rcMqR
#11 Python for Beginners
https://techurl.in/RUKwN
#12 Complete Python Course for IT Administrators
https://techurl.in/nHPci
#13 Learn the Python Programming Language
https://techurl.in/UhqQx
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Udemy Coupons Expire After 1000 Redemptions
https://tinyurl.com/udemycouponsfree
So Please Join Our Telegram Or WhatsApp Channel To Get An Instant Alert For Coupons.
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Join Our WhatsApp Channel:
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Do share in your groups.โจ
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โ Free Certificate upon Completion ๐ฅณ
โโโโโโโโโโโโโโโโโโโโโ
#01 Clustering & Unsupervised Learning in Python
https://techurl.in/voaMZ
#02 The Complete Django Rest Framework Bootcamp
https://techurl.in/tEuDI
#03 Python Microservices: Build, Scale, and Deploy like a Pro!
https://techurl.in/KKxgQ
#04 Python OOP: A Complete Course in Object Oriented Programming
https://techurl.in/mKPVw
#05 HTML 5,Python,Django And Flask Framework Full-Stack Course
https://techurl.in/LCKGu
#06 NumPy, Pandas, & Python for Data Analysis: A Complete Guide
https://techurl.in/hjijD
#07 Data-Centric Machine Learning with Python: Hands-On Guide
https://techurl.in/hXuUS
#08 Python & Java: Master Backend & Frontend Web Developments
https://techurl.in/VzORW
#09 Numpy For Data Science - Real Time Experience
https://techurl.in/CnbTn
#10 Python For Data Science - Real Time Experience
https://techurl.in/rcMqR
#11 Python for Beginners
https://techurl.in/RUKwN
#12 Complete Python Course for IT Administrators
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KenyaTrends.co.ke
Kenya Trends - Jobs | Opportunities | Free Resources
Sharing free learning resources, jobs & opportunities.
Remote Senior PHP Developer Job at Learning Tapestry
Job Location: Remote(Worldwide)
Company Headquarters: United States
- Knowledge of PHP
- Knowledge of JavaScript/TypeScript
- Write clean, self-explanatory code
- SQL DBs
- GitHub
Apply Here:
https://kenyatrends.co.ke/7vrx
Job Location: Remote(Worldwide)
Company Headquarters: United States
- Knowledge of PHP
- Knowledge of JavaScript/TypeScript
- Write clean, self-explanatory code
- SQL DBs
- GitHub
Apply Here:
https://kenyatrends.co.ke/7vrx
Remote Client Support Specialist (Healthcare Facilities โ B2B) Job at Clipboard Health โ California, USA
Job Location: Remote (Worldwide)
Company Headquarters: California, USA
What We Look For
๐ Customer-Centric Mindset
๐ Strong Communication Skills
๐ Proactive Problem-Solving
๐ High Accountability
Apply Here:
https://kenyatrends.co.ke/pk66
Job Location: Remote (Worldwide)
Company Headquarters: California, USA
What We Look For
๐ Customer-Centric Mindset
๐ Strong Communication Skills
๐ Proactive Problem-Solving
๐ High Accountability
Apply Here:
https://kenyatrends.co.ke/pk66
BACK-END DEVELOPER REMOTE JOBS ๐๐
Remote BackEnd Engineer (Node.js) Job at Popcorn Labs, Inc
https://kenyatrends.co.ke/s16j
Remote Engineering Technical Lead - Node.js
https://kenyatrends.co.ke/s16j
Remote Senior Backend Software Developer Job at Missive(Quebec, Canada)
https://kenyatrends.co.ke/x8a6
Remote Backend Engineer (Python) Job at Search Atlas
https://kenyatrends.co.ke/0vlb
Remote Senior Backend Engineer Job at CardNexus
https://kenyatrends.co.ke/wjpd
Remote Mid-level PHP Developer for B2B SaaS Job at Gymdesk
https://kenyatrends.co.ke/kzeg
Remote BackEnd Engineer (Node.js) Job at Popcorn Labs, Inc
https://kenyatrends.co.ke/s16j
Remote Engineering Technical Lead - Node.js
https://kenyatrends.co.ke/s16j
Remote Senior Backend Software Developer Job at Missive(Quebec, Canada)
https://kenyatrends.co.ke/x8a6
Remote Backend Engineer (Python) Job at Search Atlas
https://kenyatrends.co.ke/0vlb
Remote Senior Backend Engineer Job at CardNexus
https://kenyatrends.co.ke/wjpd
Remote Mid-level PHP Developer for B2B SaaS Job at Gymdesk
https://kenyatrends.co.ke/kzeg