Forwarded from Tamire Dawud
📢 Hello Dears,
Congratulations! 🎉
The registration page for the Huawei ICT Competition – Northern Africa Innovation Track is now officially published.
As you all know, the Huawei ICT Competition is divided into three tracks to engage both students and instructors:
❶. Practice Competition – Focused on testing ICT knowledge and hands-on skills (Networking, Cloud, Computing, Security, etc.).
❷. Innovation Competition – Team-based projects solving real-world problems using Huawei technologies like AI and Cloud.
❸. Teacher Competition – Designed for ICT instructors to strengthen teaching capacity and showcase expertise.
👉 Each competition has its own registration link, rules, and deadlines:
• The Practice Competition registration link has already been shared: 👉 https://e.huawei.com/en/talent/#/ict-academy/ict-competition/regional-competition?zoneCode=026902&zoneId=98269659&compId=85132004&divisionName=Northern%20Africa&type=C001&isCollectGender=N&enrollmentDeadline=2025-12-31%2023%3A59%3A59&compTotalApplicantCount=797
• The Innovation Competition registration link is now available (see below).
• The Teacher Competition registration link will be released very soon — please stay tuned.
We encourage all eligible students and instructors to register and actively participate. This is a great opportunity to enhance your skills, collaborate, and showcase your talent on an international stage. 🌍✨
🔗 Innovation Competition Registration Page:
https://e.huawei.com/en/talent/#/ict/innovation-details?zoneCode=026902&zoneId=98269677&compId=85132008&divisionName=Northern%20Africa&type=C002&isCollectGender=N&enrollmentDeadline=2025-12-24%2023%3A59%3A59&compTotalApplicantCount=0%20%20%EF%BC%88
Steps to Participate:
⓵. Register for the Innovation Competition using the above link.
⓶. Complete the online learning space.
⓷. Upload your project before the deadline.
📌 Competition Instructions:
👉Team Formation & Requirements:
👉Each team must consist of three students AND one instructor (mandatory).
👉Participants must be current undergraduates, master’s, or PhD students.
👉Teams are encouraged to come from the same university, ideally with members from different grades to maximize complementary skills.
👉Instructor role: Guides the team, supports project planning, and ensures the proper use of Huawei technologies..
#️⃣ Eligibility:
✍️Each student can only participate in one track (either Innovation OR Practice, not both). Once registered, team members cannot be changed.
#️⃣ Project Requirements:
✍️Submissions must use Huawei AI-related technologies (MindSpore, CANN, ModelArts).
✍️ Projects must solve real-life or industry-specific challenges (software or software + hardware systems).
✍️Entries must be original,practical, functional, and innovative.
✍️ Huawei technologies must be clearly highlighted in diagrams, process flows, or codes.
✍️ Final submissions should include design scheme, functions, value, and problem solved.
Disqualification Rules:
👉Teams unable to demonstrate functionality will be disqualified.
👉 Failure to use Huawei’s specified technologies will make entries ineligible.
👉 Reusing previous projects without improvements is prohibited.
👉 Entries must not violate laws, contain discriminatory content, or infringe on privacy.
Congratulations! 🎉
The registration page for the Huawei ICT Competition – Northern Africa Innovation Track is now officially published.
As you all know, the Huawei ICT Competition is divided into three tracks to engage both students and instructors:
❶. Practice Competition – Focused on testing ICT knowledge and hands-on skills (Networking, Cloud, Computing, Security, etc.).
❷. Innovation Competition – Team-based projects solving real-world problems using Huawei technologies like AI and Cloud.
❸. Teacher Competition – Designed for ICT instructors to strengthen teaching capacity and showcase expertise.
👉 Each competition has its own registration link, rules, and deadlines:
• The Practice Competition registration link has already been shared: 👉 https://e.huawei.com/en/talent/#/ict-academy/ict-competition/regional-competition?zoneCode=026902&zoneId=98269659&compId=85132004&divisionName=Northern%20Africa&type=C001&isCollectGender=N&enrollmentDeadline=2025-12-31%2023%3A59%3A59&compTotalApplicantCount=797
• The Innovation Competition registration link is now available (see below).
• The Teacher Competition registration link will be released very soon — please stay tuned.
We encourage all eligible students and instructors to register and actively participate. This is a great opportunity to enhance your skills, collaborate, and showcase your talent on an international stage. 🌍✨
🔗 Innovation Competition Registration Page:
https://e.huawei.com/en/talent/#/ict/innovation-details?zoneCode=026902&zoneId=98269677&compId=85132008&divisionName=Northern%20Africa&type=C002&isCollectGender=N&enrollmentDeadline=2025-12-24%2023%3A59%3A59&compTotalApplicantCount=0%20%20%EF%BC%88
Steps to Participate:
⓵. Register for the Innovation Competition using the above link.
⓶. Complete the online learning space.
⓷. Upload your project before the deadline.
📌 Competition Instructions:
👉Team Formation & Requirements:
👉Each team must consist of three students AND one instructor (mandatory).
👉Participants must be current undergraduates, master’s, or PhD students.
👉Teams are encouraged to come from the same university, ideally with members from different grades to maximize complementary skills.
👉Instructor role: Guides the team, supports project planning, and ensures the proper use of Huawei technologies..
#️⃣ Eligibility:
✍️Each student can only participate in one track (either Innovation OR Practice, not both). Once registered, team members cannot be changed.
#️⃣ Project Requirements:
✍️Submissions must use Huawei AI-related technologies (MindSpore, CANN, ModelArts).
✍️ Projects must solve real-life or industry-specific challenges (software or software + hardware systems).
✍️Entries must be original,practical, functional, and innovative.
✍️ Huawei technologies must be clearly highlighted in diagrams, process flows, or codes.
✍️ Final submissions should include design scheme, functions, value, and problem solved.
Disqualification Rules:
👉Teams unable to demonstrate functionality will be disqualified.
👉 Failure to use Huawei’s specified technologies will make entries ineligible.
👉 Reusing previous projects without improvements is prohibited.
👉 Entries must not violate laws, contain discriminatory content, or infringe on privacy.
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Dimension reduction
Dimension reduction is the process of reducing the number of variables (dimensions) in a dataset while keeping its most important information. It is a powerful technique for simplifying complex data, which offers benefits such as improved computational efficiency, better model performance, and easier data visualization.
Why reduce dimensions?
💡 Curse of dimensionality: When a dataset has too many dimensions relative to the number of data points, it can become sparse, making it difficult for machine learning models to find meaningful patterns.
🔑 Eliminate redundancy and noise: Datasets often contain variables that are highly correlated or irrelevant, adding noise and complexity that can confuse models.
📊 Improve visualization: The human brain is limited to visualizing data in two or three dimensions. Dimensionality reduction allows you to represent high-dimensional data in a way that is easier for people to understand.
🎯 Increase efficiency: Fewer dimensions mean less computational time and resources are needed to process the data, which is especially important for large datasets.
⚡️ Prevent overfitting: By simplifying the dataset and removing noise, a model is less likely to learn the random fluctuations in the data and more likely to generalize well to new data.
Common techniques
There are two primary approaches to dimensionality reduction:
1. Feature extraction
This method transforms the original variables into a new, smaller set of variables (components) that are combinations of the original ones.
👉 Principal Component Analysis (PCA): A popular unsupervised method that creates new, uncorrelated components, ordered by the amount of variance they explain.
👉 Factor Analysis (EFA): An unsupervised method used to identify underlying, unobserved (latent) factors that cause the correlations among the observed variables.
👉 t-SNE (t-Distributed Stochastic Neighbor Embedding): A nonlinear method especially useful for visualizing high-dimensional data by placing similar data points closer together in a lower-dimensional space.
2. Feature selection
This method selects a subset of the most relevant original variables, discarding the rest. It does not transform the variables.
Filter methods: Use statistical measures to score features and keep the best ones, for example, by filtering out low-variance or highly correlated variables.
Wrapper methods: Evaluate different subsets of features by training and testing a model with each subset to see which performs best.
https://medium.com/@souravbanerjee423/demystify-the-power-of-dimensionality-reduction-in-machine-learning-26b70b882571
@data_to_pattern @data_to_pattern @data_to_pattern
Dimension reduction is the process of reducing the number of variables (dimensions) in a dataset while keeping its most important information. It is a powerful technique for simplifying complex data, which offers benefits such as improved computational efficiency, better model performance, and easier data visualization.
Why reduce dimensions?
💡 Curse of dimensionality: When a dataset has too many dimensions relative to the number of data points, it can become sparse, making it difficult for machine learning models to find meaningful patterns.
🔑 Eliminate redundancy and noise: Datasets often contain variables that are highly correlated or irrelevant, adding noise and complexity that can confuse models.
📊 Improve visualization: The human brain is limited to visualizing data in two or three dimensions. Dimensionality reduction allows you to represent high-dimensional data in a way that is easier for people to understand.
🎯 Increase efficiency: Fewer dimensions mean less computational time and resources are needed to process the data, which is especially important for large datasets.
⚡️ Prevent overfitting: By simplifying the dataset and removing noise, a model is less likely to learn the random fluctuations in the data and more likely to generalize well to new data.
Common techniques
There are two primary approaches to dimensionality reduction:
1. Feature extraction
This method transforms the original variables into a new, smaller set of variables (components) that are combinations of the original ones.
👉 Principal Component Analysis (PCA): A popular unsupervised method that creates new, uncorrelated components, ordered by the amount of variance they explain.
👉 Factor Analysis (EFA): An unsupervised method used to identify underlying, unobserved (latent) factors that cause the correlations among the observed variables.
👉 t-SNE (t-Distributed Stochastic Neighbor Embedding): A nonlinear method especially useful for visualizing high-dimensional data by placing similar data points closer together in a lower-dimensional space.
2. Feature selection
This method selects a subset of the most relevant original variables, discarding the rest. It does not transform the variables.
Filter methods: Use statistical measures to score features and keep the best ones, for example, by filtering out low-variance or highly correlated variables.
Wrapper methods: Evaluate different subsets of features by training and testing a model with each subset to see which performs best.
https://medium.com/@souravbanerjee423/demystify-the-power-of-dimensionality-reduction-in-machine-learning-26b70b882571
@data_to_pattern @data_to_pattern @data_to_pattern
Medium
Demystify the Power of Dimensionality Reduction in Machine Learning
In the world of machine learning, navigating the vast landscape of high-dimensional data can be as thrilling as it is challenging. Imagine…
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Forwarded from Ethiopian Data Science and ML Community
🇪🇹 Hello Ethiopian Data Science & ML Community!
Are you ready to grow your skills, build your portfolio, and compete with top data scientists across Africa and the world? 🌍
Zindi is Africa’s leading platform for data science and AI challenges — connecting learners, professionals, and organizations through real-world problems and exciting competitions! 💻🔥
By joining Zindi, you can:
✅Compete in AI challenges with real data and prizes
✅ Build your data science portfolio and gain global visibility
✅ Learn from others and improve your practical skills
✅ Connect with employers through Zindi Talent Search
🔝 Current Zindi Leaderboard Highlights
Ethiopian talent is making waves! 🇪🇹
💡 Let’s Build a Strong Ethiopian Data Science & ML Community!
Together, we can grow our skills, make a global impact, and showcase Ethiopian talent!
🔗 Join Now: https://zindi.africa/
🚀 Let’s connect, compete, and create a thriving Ethiopian data science community!
JOIN
@ethiopian_ds_ml @ethiopian_ds_ml @ethiopian_ds_ml
Are you ready to grow your skills, build your portfolio, and compete with top data scientists across Africa and the world? 🌍
Zindi is Africa’s leading platform for data science and AI challenges — connecting learners, professionals, and organizations through real-world problems and exciting competitions! 💻🔥
By joining Zindi, you can:
✅Compete in AI challenges with real data and prizes
✅ Build your data science portfolio and gain global visibility
✅ Learn from others and improve your practical skills
✅ Connect with employers through Zindi Talent Search
🔝 Current Zindi Leaderboard Highlights
Ethiopian talent is making waves! 🇪🇹
💡 Let’s Build a Strong Ethiopian Data Science & ML Community!
Together, we can grow our skills, make a global impact, and showcase Ethiopian talent!
🔗 Join Now: https://zindi.africa/
🚀 Let’s connect, compete, and create a thriving Ethiopian data science community!
JOIN
@ethiopian_ds_ml @ethiopian_ds_ml @ethiopian_ds_ml
👏4❤1
Modeling Overfitting
When you’re training a machine learning model, few things are as frustrating as watching your training accuracy skyrocket while your validation accuracy flatlines or worse, starts dropping
More
https://medium.com/@segnigirma11/understanding-detecting-and-fixing-overfitting-in-machine-learning-6f84e8109489
When you’re training a machine learning model, few things are as frustrating as watching your training accuracy skyrocket while your validation accuracy flatlines or worse, starts dropping
More
https://medium.com/@segnigirma11/understanding-detecting-and-fixing-overfitting-in-machine-learning-6f84e8109489
Medium
Understanding, Detecting, and Fixing Overfitting in Machine Learning
By Segni girma
❤3
Forwarded from Ethiopian Data Science and ML Community
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Forwarded from Ethiopian Data Science and ML Community
🚀 Discover One of the Best Websites for Machine Learning & AI – ml-science.com
If you’re serious about growing your skills in Machine Learning, Data Science, and Artificial Intelligence, you must check out ml-science.com.
💡 This website offers:
✅ In-depth tutorials and explanations on key ML and AI concepts
✅ Practical guides and coding examples for real-world projects
✅ Clear, structured learning paths for both beginners and professionals
✅ Updates on modern AI technologies and research trends
What makes it stand out is how simple yet powerful the content is you’ll learn not just the what, but the why behind every concept.
🔥 Whether you’re a student, researcher, or tech enthusiast, this site will help you level up your understanding and build real expertise in ML and AI.
👉 Explore it today and share it with your friends — let’s inspire more people to learn, innovate, and shape the future of AI!
🌍 www.ml-science.com
If you’re serious about growing your skills in Machine Learning, Data Science, and Artificial Intelligence, you must check out ml-science.com.
💡 This website offers:
✅ In-depth tutorials and explanations on key ML and AI concepts
✅ Practical guides and coding examples for real-world projects
✅ Clear, structured learning paths for both beginners and professionals
✅ Updates on modern AI technologies and research trends
What makes it stand out is how simple yet powerful the content is you’ll learn not just the what, but the why behind every concept.
🔥 Whether you’re a student, researcher, or tech enthusiast, this site will help you level up your understanding and build real expertise in ML and AI.
👉 Explore it today and share it with your friends — let’s inspire more people to learn, innovate, and shape the future of AI!
🌍 www.ml-science.com
The Science of Machine Learning & AI
Machine Learning Mathematics, Data Science, Computer Science
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Forwarded from CSEC ASTU (Bereket ∞)
🎙 Data Science Experience Sharing — Learn from the Best!
Curious about how successful data scientists started their journey? 🤔
Join us this Nov 15 as Zindi experts share their inspiring stories, career paths, and lessons learned from real-world data challenges.
💡 Hear firsthand how they navigated obstacles, built winning mindsets, and turned data into impact.
Don’t miss this chance to learn, connect, and get inspired to level up your data science journey!
📅 Date: Nov 15
📍 Venue: ASTU B-508 R-10
🕒 Time: 02:00 PM OR 08:00 Local Time
Link
🔗 Follow, Join, and Subscribe for More Updates!
📌 CSEC ASTU - LinkedIn
📌 CSEC ASTU - Telegram
📌 CSEC ASTU - YouTube
❗️❗️Registration open until this coming Friday: Oct 31, 2025.
@CSEC_ASTU
Curious about how successful data scientists started their journey? 🤔
Join us this Nov 15 as Zindi experts share their inspiring stories, career paths, and lessons learned from real-world data challenges.
💡 Hear firsthand how they navigated obstacles, built winning mindsets, and turned data into impact.
Don’t miss this chance to learn, connect, and get inspired to level up your data science journey!
📅 Date: Nov 15
📍 Venue: ASTU B-508 R-10
🕒 Time: 02:00 PM OR 08:00 Local Time
Registration link:
Link
🔗 Follow, Join, and Subscribe for More Updates!
📌 CSEC ASTU - LinkedIn
📌 CSEC ASTU - Telegram
📌 CSEC ASTU - YouTube
❗️❗️Registration open until this coming Friday: Oct 31, 2025.
@CSEC_ASTU
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Forwarded from Ethiopian Data Science and ML Community
📊 Predict SME Financial Health | Zindi Challenge
SMEs are vital to Southern Africa’s economy but often financially fragile. Traditional metrics like revenue don’t capture true wellbeing.
🚀 Zindi presents the Financial Health Index (FHI) — a data-driven measure of SME financial stability across savings, debt, resilience, and access to finance.
🤖 Use socio-economic and business data from Eswatini, Lesotho, Zimbabwe & Malawi to build ML models that predict FHI and help shape inclusive financial support.
Prizes
1st place: $750 USD
2nd place: $500 USD
3rd place: $250 USD
🔗 Participate now on Zindi: https://zindi.africa/competitions/dataorg-financial-health-prediction-challenge
SMEs are vital to Southern Africa’s economy but often financially fragile. Traditional metrics like revenue don’t capture true wellbeing.
🚀 Zindi presents the Financial Health Index (FHI) — a data-driven measure of SME financial stability across savings, debt, resilience, and access to finance.
🤖 Use socio-economic and business data from Eswatini, Lesotho, Zimbabwe & Malawi to build ML models that predict FHI and help shape inclusive financial support.
Prizes
1st place: $750 USD
2nd place: $500 USD
3rd place: $250 USD
🔗 Participate now on Zindi: https://zindi.africa/competitions/dataorg-financial-health-prediction-challenge
zindi.africa
data.org Financial Health Prediction Challenge 💰 - Win $1 500 USD
Can you predict the financial well-being of small businesses? Join 421 AI builders. ~2 months left
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