Forwarded from Intercom University
NVIDIA FREE AI Courses 🚀💻
Transform your skills with these cutting-edge courses by NVIDIA.
Check out the following NVIDIA FREE AI Certification Courses
Link👇:-
https://intercomuniversity.com/free-courses/nvidia-is-offering-6-ai-llm-courses-for-free/
Enroll For FREE & Get Certified 🎓
Transform your skills with these cutting-edge courses by NVIDIA.
Check out the following NVIDIA FREE AI Certification Courses
Link👇:-
https://intercomuniversity.com/free-courses/nvidia-is-offering-6-ai-llm-courses-for-free/
Enroll For FREE & Get Certified 🎓
Tim Cook stepping down as Apple CEO, John Ternus taking over
Apple announced that Tim Cook will step down as CEO on September 1, 2026, ending a 15-year tenure that began after Steve Jobs. He will transition to executive chairman, remaining involved in strategy and global policy. John Ternus, currently head of hardware engineering, will take over as CEO as part of a long-planned succession.
During Cook’s leadership, Apple grew dramatically—from a company worth about $350 billion to over $4 trillion—while expanding its product lineup with devices like the Apple Watch and AirPods and building a massive services business.
However, his tenure also faced challenges, including missteps like Apple Maps and slower progress in artificial intelligence.
Ternus, a longtime insider and key figure in Apple’s hardware development, is expected to lead the company into its next phase, particularly as it navigates increasing competition and the growing importance of AI.
🔆 t.me/techpsyche
Apple announced that Tim Cook will step down as CEO on September 1, 2026, ending a 15-year tenure that began after Steve Jobs. He will transition to executive chairman, remaining involved in strategy and global policy. John Ternus, currently head of hardware engineering, will take over as CEO as part of a long-planned succession.
During Cook’s leadership, Apple grew dramatically—from a company worth about $350 billion to over $4 trillion—while expanding its product lineup with devices like the Apple Watch and AirPods and building a massive services business.
However, his tenure also faced challenges, including missteps like Apple Maps and slower progress in artificial intelligence.
Ternus, a longtime insider and key figure in Apple’s hardware development, is expected to lead the company into its next phase, particularly as it navigates increasing competition and the growing importance of AI.
🔆 t.me/techpsyche
By the way, don’t be too hard on yourself. You might not have secured a remote job or landed that big opportunity yet, but think about how far you’ve come to reach your current stage, it’s no small thing. For me, the steps I’ve taken matter more than anything else. And honestly, they’ve already paid off in many ways. What truly matters now is to keep making continuous improvements.
❤1
PHP is said to be dead or dying soon. But it is not even sick enough to die😊
Forwarded from Crypto Stract | Web 3, Finance, Business, Blockchain, Crypto, Growth, Stock Market, Money
Tips to help you be successful:
* Implement healthy habits every day
Good habits are the foundation of wealth. They distinguish a successful wealthy person from a loser. In the latter, bad habits prevail. Think about what's stopping you? Awareness is the first step to change.
* Set goals regularly
Successful people are driven by their goals. There are always unconquered peaks in front of them. They plan their day in detail.
* Identify root causes
If you know why you want to achieve wealth and success, you will get there faster. Setting goals is important. But even more important is why you chose this particular goal.
* Get things done
The truth is as old as the world: do not put off until tomorrow what you can do today. Everyone has fears “What if it doesn’t work out?”, “It’s too difficult,” and so on. But successful people overcome them and bring important things to the end, no matter what it takes.
* Do the maximum and even a little more
To do something, if only to quickly and if only to lag behind - the approach of losers. Successful and wealthy people always do even a little more than what is required of them. If you have to stay at work for this - no problem! Putting in more effort is easy!
Entrepreneurs cheat sheet
t.me/cryptostract/125
How I keep my Finances Organized
t.me/cryptostract/147
More Resources Here:
whatsapp.com/channel/0029VajB00n0LKZ7cSloGR1M
* Implement healthy habits every day
Good habits are the foundation of wealth. They distinguish a successful wealthy person from a loser. In the latter, bad habits prevail. Think about what's stopping you? Awareness is the first step to change.
* Set goals regularly
Successful people are driven by their goals. There are always unconquered peaks in front of them. They plan their day in detail.
* Identify root causes
If you know why you want to achieve wealth and success, you will get there faster. Setting goals is important. But even more important is why you chose this particular goal.
* Get things done
The truth is as old as the world: do not put off until tomorrow what you can do today. Everyone has fears “What if it doesn’t work out?”, “It’s too difficult,” and so on. But successful people overcome them and bring important things to the end, no matter what it takes.
* Do the maximum and even a little more
To do something, if only to quickly and if only to lag behind - the approach of losers. Successful and wealthy people always do even a little more than what is required of them. If you have to stay at work for this - no problem! Putting in more effort is easy!
Entrepreneurs cheat sheet
t.me/cryptostract/125
How I keep my Finances Organized
t.me/cryptostract/147
More Resources Here:
whatsapp.com/channel/0029VajB00n0LKZ7cSloGR1M
👍1
Complete Data Science Roadmap 👇👇
1. Introduction to Data Science
- Overview and Importance
- Data Science Lifecycle
- Key Roles (Data Scientist, Analyst, Engineer)
2. Mathematics and Statistics
- Probability and Distributions
- Descriptive/Inferential Statistics
- Hypothesis Testing
- Linear Algebra and Calculus Basics
3. Programming Languages
- Python: NumPy, Pandas, Matplotlib
- R: dplyr, ggplot2
- SQL: Joins, Aggregations, CRUD
4. Data Collection & Preprocessing
- Data Cleaning and Wrangling
- Handling Missing Data
- Feature Engineering
5. Exploratory Data Analysis (EDA)
- Summary Statistics
- Data Visualization (Histograms, Box Plots, Correlation)
6. Machine Learning
- Supervised (Linear/Logistic Regression, Decision Trees)
- Unsupervised (K-Means, PCA)
- Model Selection and Cross-Validation
7. Advanced Machine Learning
- SVM, Random Forests, Boosting
- Neural Networks Basics
8. Deep Learning
- Neural Networks Architecture
- CNNs for Image Data
- RNNs for Sequential Data
9. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Word Embeddings (Word2Vec)
10. Data Visualization & Storytelling
- Dashboards (Tableau, Power BI)
- Telling Stories with Data
11. Model Deployment
- Deploy with Flask or Django
- Monitoring and Retraining Models
12. Big Data & Cloud
- Introduction to Hadoop, Spark
- Cloud Tools (AWS, Google Cloud)
13. Data Engineering Basics
- ETL Pipelines
- Data Warehousing (Redshift, BigQuery)
14. Ethics in Data Science
- Ethical Data Usage
- Bias in AI Models
15. Tools for Data Science
- Jupyter, Git, Docker
16. Career Path & Certifications
- Building a Data Science Portfolio
Learn DatA & AI: https://365datascience.pxf.io/Z6KDgk
Free Notes & Books to learn Data Science: https://t.me/datascienceresourcestp
Python Project Ideas: https://t.me/pythonresourcestp/74
Best Resources to learn Data Science 👇👇
Python Tutorial (http://pythontutorial.net/)
Data Science Course (http://kaggle.com/learn) by Kaggle
Machine Learning Course (http://developers.google.com/machine-learning/crash-course) by Google
Best Data Science & Machine Learning Resources (https://topmate.io/learning_resources/1406977)
Interview Process for Data Science Role at Amazon (https://t.me/datascienceresourcestp/85)
Python Interview Resources (https://t.me/pythonresourcestp/40)
Join for more free courses
https://t.me/techpsyche
Like for more ❤️
ENJOY LEARNING👍👍
Join Our WhatsApp Channel:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
1. Introduction to Data Science
- Overview and Importance
- Data Science Lifecycle
- Key Roles (Data Scientist, Analyst, Engineer)
2. Mathematics and Statistics
- Probability and Distributions
- Descriptive/Inferential Statistics
- Hypothesis Testing
- Linear Algebra and Calculus Basics
3. Programming Languages
- Python: NumPy, Pandas, Matplotlib
- R: dplyr, ggplot2
- SQL: Joins, Aggregations, CRUD
4. Data Collection & Preprocessing
- Data Cleaning and Wrangling
- Handling Missing Data
- Feature Engineering
5. Exploratory Data Analysis (EDA)
- Summary Statistics
- Data Visualization (Histograms, Box Plots, Correlation)
6. Machine Learning
- Supervised (Linear/Logistic Regression, Decision Trees)
- Unsupervised (K-Means, PCA)
- Model Selection and Cross-Validation
7. Advanced Machine Learning
- SVM, Random Forests, Boosting
- Neural Networks Basics
8. Deep Learning
- Neural Networks Architecture
- CNNs for Image Data
- RNNs for Sequential Data
9. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Word Embeddings (Word2Vec)
10. Data Visualization & Storytelling
- Dashboards (Tableau, Power BI)
- Telling Stories with Data
11. Model Deployment
- Deploy with Flask or Django
- Monitoring and Retraining Models
12. Big Data & Cloud
- Introduction to Hadoop, Spark
- Cloud Tools (AWS, Google Cloud)
13. Data Engineering Basics
- ETL Pipelines
- Data Warehousing (Redshift, BigQuery)
14. Ethics in Data Science
- Ethical Data Usage
- Bias in AI Models
15. Tools for Data Science
- Jupyter, Git, Docker
16. Career Path & Certifications
- Building a Data Science Portfolio
Learn DatA & AI: https://365datascience.pxf.io/Z6KDgk
Free Notes & Books to learn Data Science: https://t.me/datascienceresourcestp
Python Project Ideas: https://t.me/pythonresourcestp/74
Best Resources to learn Data Science 👇👇
Python Tutorial (http://pythontutorial.net/)
Data Science Course (http://kaggle.com/learn) by Kaggle
Machine Learning Course (http://developers.google.com/machine-learning/crash-course) by Google
Best Data Science & Machine Learning Resources (https://topmate.io/learning_resources/1406977)
Interview Process for Data Science Role at Amazon (https://t.me/datascienceresourcestp/85)
Python Interview Resources (https://t.me/pythonresourcestp/40)
Join for more free courses
https://t.me/techpsyche
Like for more ❤️
ENJOY LEARNING👍👍
Join Our WhatsApp Channel:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Forwarded from Remote Chrome | RemoteChrome.com | Remote Jobs
♻️ Remote AI, ML Jobs to Apply in April 2026
Apply Here:
https://remotechrome.com/remotejobs/top-13-remote-ai-ml-jobs-to-apply-in-april-2026/
More Remote Tech Jobs Here👇
https://t.me/remotechrome
SHARE WITH YOUR FRIENDS🥳🥳
Apply Here:
https://remotechrome.com/remotejobs/top-13-remote-ai-ml-jobs-to-apply-in-april-2026/
More Remote Tech Jobs Here👇
https://t.me/remotechrome
SHARE WITH YOUR FRIENDS🥳🥳
Forwarded from Product Design Resources TP . UX Design . Graphic Design . Video Editing . 2D 3D Animation
5 Awesome tools for Web Designers
🎨 Color Hunt (https://colorhunt.co/)
🎨 Cool Backgrounds (https://coolbackgrounds.io/)
🎨 99 Designs (https://99designs.com/categories)
🎨 UI Design Tips (https://t.me/designresourcestp)
🎨 Gravit Designer (https://www.designer.io/en/)
🎨 Color Hunt (https://colorhunt.co/)
🎨 Cool Backgrounds (https://coolbackgrounds.io/)
🎨 99 Designs (https://99designs.com/categories)
🎨 UI Design Tips (https://t.me/designresourcestp)
🎨 Gravit Designer (https://www.designer.io/en/)
Forwarded from Crypto Stract | Web 3, Finance, Business, Blockchain, Crypto, Growth, Stock Market, Money
What is funding in crypto trading? 💸
In cryptocurrencies, funding refers to the funding rate that is redistributed among traders holding positions in perpetual futures.
Funding is a periodic payment/write-off for traders with open positions in perpetual futures, which allows them to compensate for the long-term difference between the price of the underlying asset and the derivative contract.
The need for funding arose from the idea of perpetual futures, which have no maturity and can be held indefinitely. Therefore, to compensate for the difference in the price of the asset and the contract, a financing rate mechanism was launched.
Non-Recourse Loans in Crypto: t.me/cryptostract/151
Signs of Bearish Trend in Crypto: t.me/cryptostract/152
Cryptocurrency Investing: t.me/cryptostract/153
Liquidity: t.me/cryptostract/154
More Resources Here:
https://whatsapp.com/channel/0029VajB00n0LKZ7cSloGR1M
In cryptocurrencies, funding refers to the funding rate that is redistributed among traders holding positions in perpetual futures.
Funding is a periodic payment/write-off for traders with open positions in perpetual futures, which allows them to compensate for the long-term difference between the price of the underlying asset and the derivative contract.
The need for funding arose from the idea of perpetual futures, which have no maturity and can be held indefinitely. Therefore, to compensate for the difference in the price of the asset and the contract, a financing rate mechanism was launched.
Non-Recourse Loans in Crypto: t.me/cryptostract/151
Signs of Bearish Trend in Crypto: t.me/cryptostract/152
Cryptocurrency Investing: t.me/cryptostract/153
Liquidity: t.me/cryptostract/154
More Resources Here:
https://whatsapp.com/channel/0029VajB00n0LKZ7cSloGR1M
Forwarded from Mobile Dev Resources . Android . iOS . Flutter . Kotlin . Swift . Java . React Native
🚀Kotlin Multiplatform, Compose Multiplatform
Let's take a look at the future of cross-platform development...
These are now serious trends in the world of mobile and cross-platform development. You may be wondering: "What are they? How are they different from Flutter or React Native? Can we bring our projects to them? What will their future be?"
Let's take a step-by-step look at them.
🔧 What is Kotlin Multiplatform (KMP)?
To put it very simply:
Instead of coding for Android with Kotlin, for iOS with Swift, for the Backend with Java or NodeJS, and writing everything separately again, you write business logic (app logic, database, API call, validation, data model, etc.) only once with Kotlin and share it across all platforms.
🎯 What does that mean?
On Android → you use the same Kotlin code.
On iOS → that code becomes a library usable for Swift.
You can even use the same logic for Desktop and Web.
✅ KMP Benefits
🔄 Gradual migration, you don't have to build the project from scratch, you can gradually add shared modules.
⚡️ Native performance, because you write the UI separately for each platform, the speed and feel are completely native.
👨💻 One language for everywhere, especially great for Android, because they work with the same Kotlin.
🛠Easy implementation and integration, KMP fits easily in the middle of existing projects (Enterprise and Startup).
🎨 What is Compose Multiplatform (CMP)?
Well, KMP shares the logic, but the UI is still separate. This is where Compose Multiplatform comes in.
And CMP is actually the multiplatform version of Jetpack Compose. With it, you can write your UI once and run it on:
Android
iOS (now stable 🎉)
Desktop (Windows, macOS, Linux)
Web (WASM)
✅ Benefits of CMP
🖼 Common UI for everywhere Write once, use everywhere.
🚀 Excellent performance and close to native
It is 100% native on Android.
It is now stable on iOS (since version 1.8.0) and provides a user experience exactly like SwiftUI (scrolls, drag&drop, text selection, adaptive UI, etc.).
It is also stable and very useful on Desktop (JetBrains itself wrote its products with it).
🧩 Gradual integration with UIKit/SwiftUI means you can work hybrid and gradually migrate UIs.
💪 Official support from JetBrains and Google means it is future-proof and serious.
⚖️ Comparison with Flutter and React Native
The Flutter framework is very fast and good for shared UI, but the UI is completely custom and runs on its own engine (Skia), meaning it is not native.
The React Native framework
The UI is written in JavaScript/TypeScript and connected to native with Bridge, which may sometimes cause performance issues.
The KMP + CMP frameworks share both logic and UI, and you always have full access to native APIs.
🔮 The future of KMP and CMP
Well, KMP is already used in large companies like Netflix and CashApp.
And CMP is now completely stable for Android, Desktop, and iOS, and the Web is also growing rapidly.
JetBrains has built its own apps and tools with CMP, meaning it has full faith in its future.
The combination of these two technologies (KMP + CMP) is really becoming a complete stack for developing Enterprise and Startup applications.
Flutter vs. React Native: t.me/mobiledevresourcestp/95
What makes an app actually work?: t.me/mobiledevresourcestp/121
Join Our WhatsApp Channel:
whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Let's take a look at the future of cross-platform development...
These are now serious trends in the world of mobile and cross-platform development. You may be wondering: "What are they? How are they different from Flutter or React Native? Can we bring our projects to them? What will their future be?"
Let's take a step-by-step look at them.
🔧 What is Kotlin Multiplatform (KMP)?
To put it very simply:
Instead of coding for Android with Kotlin, for iOS with Swift, for the Backend with Java or NodeJS, and writing everything separately again, you write business logic (app logic, database, API call, validation, data model, etc.) only once with Kotlin and share it across all platforms.
🎯 What does that mean?
On Android → you use the same Kotlin code.
On iOS → that code becomes a library usable for Swift.
You can even use the same logic for Desktop and Web.
✅ KMP Benefits
🔄 Gradual migration, you don't have to build the project from scratch, you can gradually add shared modules.
⚡️ Native performance, because you write the UI separately for each platform, the speed and feel are completely native.
👨💻 One language for everywhere, especially great for Android, because they work with the same Kotlin.
🛠Easy implementation and integration, KMP fits easily in the middle of existing projects (Enterprise and Startup).
🎨 What is Compose Multiplatform (CMP)?
Well, KMP shares the logic, but the UI is still separate. This is where Compose Multiplatform comes in.
And CMP is actually the multiplatform version of Jetpack Compose. With it, you can write your UI once and run it on:
Android
iOS (now stable 🎉)
Desktop (Windows, macOS, Linux)
Web (WASM)
✅ Benefits of CMP
🖼 Common UI for everywhere Write once, use everywhere.
🚀 Excellent performance and close to native
It is 100% native on Android.
It is now stable on iOS (since version 1.8.0) and provides a user experience exactly like SwiftUI (scrolls, drag&drop, text selection, adaptive UI, etc.).
It is also stable and very useful on Desktop (JetBrains itself wrote its products with it).
🧩 Gradual integration with UIKit/SwiftUI means you can work hybrid and gradually migrate UIs.
💪 Official support from JetBrains and Google means it is future-proof and serious.
⚖️ Comparison with Flutter and React Native
The Flutter framework is very fast and good for shared UI, but the UI is completely custom and runs on its own engine (Skia), meaning it is not native.
The React Native framework
The UI is written in JavaScript/TypeScript and connected to native with Bridge, which may sometimes cause performance issues.
The KMP + CMP frameworks share both logic and UI, and you always have full access to native APIs.
🔮 The future of KMP and CMP
Well, KMP is already used in large companies like Netflix and CashApp.
And CMP is now completely stable for Android, Desktop, and iOS, and the Web is also growing rapidly.
JetBrains has built its own apps and tools with CMP, meaning it has full faith in its future.
The combination of these two technologies (KMP + CMP) is really becoming a complete stack for developing Enterprise and Startup applications.
Flutter vs. React Native: t.me/mobiledevresourcestp/95
What makes an app actually work?: t.me/mobiledevresourcestp/121
Join Our WhatsApp Channel:
whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Forwarded from Remote Chrome | RemoteChrome.com | Remote Jobs
Remote Chrome
How to Make Money Online Without PayPal (Real Methods That Actually Work) - Remote Chrome
Let me be upfront with you. I spent three years building income streams online before I realized how heavily I'd tied everything to one payment processor.
How to Make Money Online Without PayPal (Real Methods That Actually Work)
Guide Here: https://remotechrome.com/blog/how-to-make-money-online-without-paypal-real-methods-that-actually-work/
Guide Here: https://remotechrome.com/blog/how-to-make-money-online-without-paypal-real-methods-that-actually-work/
Forwarded from Mobile Dev Resources . Android . iOS . Flutter . Kotlin . Swift . Java . React Native
Explore Flutter’s Hot Reload 🔄
Flutter’s Hot Reload feature allows you to:
- Instantly see changes in your code.
- Speed up the development process.
- Experiment with UI designs quickly.
Boost your productivity with Flutter’s powerful Hot Reload feature! 🚀
Flutter Roadmap & Free Learning Resources Here👇
t.me/mobiledevresourcestp/84
Join Our WhatsApp Channel:
whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Flutter’s Hot Reload feature allows you to:
- Instantly see changes in your code.
- Speed up the development process.
- Experiment with UI designs quickly.
Boost your productivity with Flutter’s powerful Hot Reload feature! 🚀
Flutter Roadmap & Free Learning Resources Here👇
t.me/mobiledevresourcestp/84
Join Our WhatsApp Channel:
whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Forwarded from Free Courses: Google | Microsoft | Udemy | Coursera | IBM | NVIDIA | LinkedIn Learning | MIT | Udemy Coupons & PDF Books
8 FREE AI Courses by Google 🎓🚀 Learn, Grow, and Succeed
1. Introduction to Generative AI
→ An introductory course to explain what generative AI is.
→ You'll learn how AI is used and how it's different from machine learning.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/536)
2. Image Generation
→ Discover how to train and deploy a model to generate images.
→ After completing this course, you will be awarded a badge.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/541)
3. Responsible AI
→ It explains what responsible AI is and why it's important.
→ Learn the 7 AI principles.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/554)
4. Large Language Models
→ Explore what large language models (LLM) are.
→ How you can use prompting tuning to enhance LLM performance.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/539)
5. Transformer and BERT Models
→ Two essential AI models.
→ How it is to build the BERT model.
→ Upon completion, you will be awarded a badge.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/538)
6. Attention Mechanism
→ Introduce you to the attention mechanism.
→ Find out how it can be applied to enhance AI tasks' performance.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/537)
7. Generative AI Studio
→ Integrate AI into your apps.
→ Find out about Generative AI Studio, what it can do, and it's features.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/552)
8. Image recognition
→ Learn how to create an AI that understands images.
→ Practical learning so that you can create your own by the end of the course.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/542)
Best Resources to learn ML & AI 👇
https://t.me/airesourcestp/86
Free and Essential GenAI Courses
https://t.me/airesourcestp/81
AI & ML Free Courses by Top Institutions
https://intercomuniversity.com/category/free-courses/artificial-intelligence/
All the best 👍👍
More Learning Resources Here:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Telegram Channel:
https://t.me/techpsyche
1. Introduction to Generative AI
→ An introductory course to explain what generative AI is.
→ You'll learn how AI is used and how it's different from machine learning.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/536)
2. Image Generation
→ Discover how to train and deploy a model to generate images.
→ After completing this course, you will be awarded a badge.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/541)
3. Responsible AI
→ It explains what responsible AI is and why it's important.
→ Learn the 7 AI principles.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/554)
4. Large Language Models
→ Explore what large language models (LLM) are.
→ How you can use prompting tuning to enhance LLM performance.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/539)
5. Transformer and BERT Models
→ Two essential AI models.
→ How it is to build the BERT model.
→ Upon completion, you will be awarded a badge.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/538)
6. Attention Mechanism
→ Introduce you to the attention mechanism.
→ Find out how it can be applied to enhance AI tasks' performance.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/537)
7. Generative AI Studio
→ Integrate AI into your apps.
→ Find out about Generative AI Studio, what it can do, and it's features.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/552)
8. Image recognition
→ Learn how to create an AI that understands images.
→ Practical learning so that you can create your own by the end of the course.
🔗 Course Link (https://www.cloudskillsboost.google/course_templates/542)
Best Resources to learn ML & AI 👇
https://t.me/airesourcestp/86
Free and Essential GenAI Courses
https://t.me/airesourcestp/81
AI & ML Free Courses by Top Institutions
https://intercomuniversity.com/category/free-courses/artificial-intelligence/
All the best 👍👍
More Learning Resources Here:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Telegram Channel:
https://t.me/techpsyche
❤1
Data Analytics Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Descriptive Statistics
| | |-- Inferential Statistics
| | |-- Probability Theory
| |
| |-- Programming
| | |-- Python (Focus on Libraries like Pandas, NumPy)
| | |-- R (For Statistical Analysis)
| | |-- SQL (For Data Extraction)
|
|-- Data Collection and Storage
| |-- Data Sources
| | |-- APIs
| | |-- Web Scraping
| | |-- Databases
| |
| |-- Data Storage
| | |-- Relational Databases (MySQL, PostgreSQL)
| | |-- NoSQL Databases (MongoDB, Cassandra)
| | |-- Data Lakes and Warehousing (Snowflake, Redshift)
|
|-- Data Cleaning and Preparation
| |-- Handling Missing Data
| |-- Data Transformation
| |-- Data Normalization and Standardization
| |-- Outlier Detection
|
|-- Exploratory Data Analysis (EDA)
| |-- Data Visualization Tools
| | |-- Matplotlib
| | |-- Seaborn
| | |-- ggplot2
| |
| |-- Identifying Trends and Patterns
| |-- Correlation Analysis
|
|-- Advanced Analytics
| |-- Predictive Analytics (Regression, Forecasting)
| |-- Prescriptive Analytics (Optimization Models)
| |-- Segmentation (Clustering Techniques)
| |-- Sentiment Analysis (Text Data)
|
|-- Data Visualization and Reporting
| |-- Visualization Tools
| | |-- Power BI
| | |-- Tableau
| | |-- Google Data Studio
| |
| |-- Dashboard Design
| |-- Interactive Visualizations
| |-- Storytelling with Data
|
|-- Business Intelligence (BI)
| |-- KPI Design and Implementation
| |-- Decision-Making Frameworks
| |-- Industry-Specific Use Cases (Finance, Marketing, HR)
|
|-- Big Data Analytics
| |-- Tools and Frameworks
| | |-- Hadoop
| | |-- Apache Spark
| |
| |-- Real-Time Data Processing
| |-- Stream Analytics (Kafka, Flink)
|
|-- Domain Knowledge
| |-- Industry Applications
| | |-- E-commerce
| | |-- Healthcare
| | |-- Supply Chain
|
|-- Ethical Data Usage
| |-- Data Privacy Regulations (GDPR, CCPA)
| |-- Bias Mitigation in Analysis
| |-- Transparency in Reporting
Free Resources to learn Data Analytics skills👇👇
1. SQL
https://bit.ly/4kNb15x
https://mode.com/sql-tutorial/introduction-to-sql
https://t.me/sqlresourcestp/58
2. Python
https://www.learnpython.org/
https://t.me/pythonresourcestp/67
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://t.me/dataanalysisresourcestp/47
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://tinyurl.com/2b76wzud
https://www.w3schools.com/EXCEL/index.php
Tableau Learning Plan
https://t.me/dataanalysisresourcestp/84
7 Free Data Analytics Courses👇👇
https://tinyurl.com/326exaw7
Data Analyst Checklist
https://t.me/dataanalysisresourcestp/99
Hope it helps :)
Share our channel link with your friends:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Telegram Channel:
https://t.me/techpsyche
|
|-- Fundamentals
| |-- Mathematics
| | |-- Descriptive Statistics
| | |-- Inferential Statistics
| | |-- Probability Theory
| |
| |-- Programming
| | |-- Python (Focus on Libraries like Pandas, NumPy)
| | |-- R (For Statistical Analysis)
| | |-- SQL (For Data Extraction)
|
|-- Data Collection and Storage
| |-- Data Sources
| | |-- APIs
| | |-- Web Scraping
| | |-- Databases
| |
| |-- Data Storage
| | |-- Relational Databases (MySQL, PostgreSQL)
| | |-- NoSQL Databases (MongoDB, Cassandra)
| | |-- Data Lakes and Warehousing (Snowflake, Redshift)
|
|-- Data Cleaning and Preparation
| |-- Handling Missing Data
| |-- Data Transformation
| |-- Data Normalization and Standardization
| |-- Outlier Detection
|
|-- Exploratory Data Analysis (EDA)
| |-- Data Visualization Tools
| | |-- Matplotlib
| | |-- Seaborn
| | |-- ggplot2
| |
| |-- Identifying Trends and Patterns
| |-- Correlation Analysis
|
|-- Advanced Analytics
| |-- Predictive Analytics (Regression, Forecasting)
| |-- Prescriptive Analytics (Optimization Models)
| |-- Segmentation (Clustering Techniques)
| |-- Sentiment Analysis (Text Data)
|
|-- Data Visualization and Reporting
| |-- Visualization Tools
| | |-- Power BI
| | |-- Tableau
| | |-- Google Data Studio
| |
| |-- Dashboard Design
| |-- Interactive Visualizations
| |-- Storytelling with Data
|
|-- Business Intelligence (BI)
| |-- KPI Design and Implementation
| |-- Decision-Making Frameworks
| |-- Industry-Specific Use Cases (Finance, Marketing, HR)
|
|-- Big Data Analytics
| |-- Tools and Frameworks
| | |-- Hadoop
| | |-- Apache Spark
| |
| |-- Real-Time Data Processing
| |-- Stream Analytics (Kafka, Flink)
|
|-- Domain Knowledge
| |-- Industry Applications
| | |-- E-commerce
| | |-- Healthcare
| | |-- Supply Chain
|
|-- Ethical Data Usage
| |-- Data Privacy Regulations (GDPR, CCPA)
| |-- Bias Mitigation in Analysis
| |-- Transparency in Reporting
Free Resources to learn Data Analytics skills👇👇
1. SQL
https://bit.ly/4kNb15x
https://mode.com/sql-tutorial/introduction-to-sql
https://t.me/sqlresourcestp/58
2. Python
https://www.learnpython.org/
https://t.me/pythonresourcestp/67
https://www.geeksforgeeks.org/python-programming-language/learn-python-tutorial
3. R
https://www.datacamp.com/courses/free-introduction-to-r
4. Data Structures
https://leetcode.com/study-plan/data-structure/
https://www.udacity.com/course/data-structures-and-algorithms-in-python--ud513
5. Data Visualization
https://www.freecodecamp.org/learn/data-visualization/
https://t.me/dataanalysisresourcestp/47
https://www.tableau.com/learn/training/20223
https://www.workout-wednesday.com/power-bi-challenges/
6. Excel
https://excel-practice-online.com/
https://tinyurl.com/2b76wzud
https://www.w3schools.com/EXCEL/index.php
Tableau Learning Plan
https://t.me/dataanalysisresourcestp/84
7 Free Data Analytics Courses👇👇
https://tinyurl.com/326exaw7
Data Analyst Checklist
https://t.me/dataanalysisresourcestp/99
Hope it helps :)
Share our channel link with your friends:
https://whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
Telegram Channel:
https://t.me/techpsyche
Forwarded from Kenya Talent Help | Jobs & Opportunities | Jobs in Kenya | Kenya Jobs | Careers | Job Vacancies Kenya
ICT Consultant AT The Green Belt Movement (GBM)
Organization: The Green Belt Movement (GBM)
Location: Nairobi, Kenya
Apply Here:
To apply for this job email your details to jobs@greenbeltmovement.org
🎁 Bonus for Job Seekers:
whatsapp.com/channel/0029VaguO7j5K3zXLbJl9J1x
More Jobs in Kenya are uploaded in this👇Telegram Channel daily
t.me/kenyatalenthelp
Organization: The Green Belt Movement (GBM)
Location: Nairobi, Kenya
Apply Here:
To apply for this job email your details to jobs@greenbeltmovement.org
🎁 Bonus for Job Seekers:
whatsapp.com/channel/0029VaguO7j5K3zXLbJl9J1x
More Jobs in Kenya are uploaded in this👇Telegram Channel daily
t.me/kenyatalenthelp
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