Tune in for a mind-blowing tech talk! 🤯
We hosting a special episode featuring ElizabethYonas, a software engineering student at AAU with a passion for quantum computing, and A2svian, an with a rich internship background.
We'll be exploring the exciting intersection of AI, machine learning, and quantum learning, and how these cutting-edge fields are revolutionizing various industries.
Elizabeth will also be sharing her tips and tricks on her path to get here, providing valuable insights for aspiring engineers and technologists.
Don't miss out on this insightful discussion! 👀
#TechTalk #AI #MachineLearning #QuantumComputing"
We hosting a special episode featuring ElizabethYonas, a software engineering student at AAU with a passion for quantum computing, and A2svian, an with a rich internship background.
We'll be exploring the exciting intersection of AI, machine learning, and quantum learning, and how these cutting-edge fields are revolutionizing various industries.
Elizabeth will also be sharing her tips and tricks on her path to get here, providing valuable insights for aspiring engineers and technologists.
Don't miss out on this insightful discussion! 👀
#TechTalk #AI #MachineLearning #QuantumComputing"
Forwarded from LizTech
Types of Machine Learning Algorithms
🔍 Supervised Learning
Supervised learning algorithms are trained using labeled data, where input data is tagged with the correct output. They learn a mapping from inputs to outputs, enabling predictions for new data. Common supervised learning algorithms include:
1. Linear Regression: Models the relationship between a dependent variable and independent variables by fitting a linear equation to observed data.
2. Logistic Regression: Estimates probabilities for binary classification tasks using a logistic function.
3. Decision Trees: Predicts the value of a target variable by learning simple decision rules from data features.
4. Random Forests: Ensemble of decision trees used for classification and regression, improving model accuracy and controlling overfitting.
5. Support Vector Machines (SVM): Effective in high-dimensional spaces, primarily used for classification but also regression.
6. Neural Networks: Powerful models that capture complex non-linear relationships, widely used in deep learning applications.
🔍 Unsupervised Learning
Unsupervised learning algorithms work with data sets without labeled responses, aiming to infer the natural structure within the data. Common unsupervised learning techniques include:
1. Clustering: Algorithms like K-means, hierarchical clustering, and DBSCAN group objects based on similarities.
2. Association: Finds rules describing relationships within the data, such as market basket analysis.
3. Principal Component Analysis (PCA): Uses statistical techniques to transform correlated variables into uncorrelated ones.
4. Autoencoders: Special neural networks used to learn efficient codings of unlabeled data.
🔍 Reinforcement Learning
Reinforcement learning algorithms learn to make a sequence of decisions to achieve a goal in an uncertain environment. The agent follows a policy based on actions and learns from the consequences through rewards or penalties. Examples of reinforcement learning algorithms include:
1. Q-learning: A model-free algorithm that learns the value of actions in specific states.
2. Deep Q-Networks (DQN): Combines Q-learning with deep neural networks, learning policies directly from high-dimensional sensory inputs.
3. Policy Gradient Methods: Optimize policy parameters directly instead of estimating action values.
4. Monte Carlo Tree Search (MCTS): Used in decision processes to find optimal decisions by simulating scenarios, notably in games like Go.
These categories provide an overview of common machine learning algorithms, each with its strengths and ideal use cases. Depending on the task at hand, certain algorithms may be better suited than others.
#MachineLearning
🔍 Supervised Learning
Supervised learning algorithms are trained using labeled data, where input data is tagged with the correct output. They learn a mapping from inputs to outputs, enabling predictions for new data. Common supervised learning algorithms include:
1. Linear Regression: Models the relationship between a dependent variable and independent variables by fitting a linear equation to observed data.
2. Logistic Regression: Estimates probabilities for binary classification tasks using a logistic function.
3. Decision Trees: Predicts the value of a target variable by learning simple decision rules from data features.
4. Random Forests: Ensemble of decision trees used for classification and regression, improving model accuracy and controlling overfitting.
5. Support Vector Machines (SVM): Effective in high-dimensional spaces, primarily used for classification but also regression.
6. Neural Networks: Powerful models that capture complex non-linear relationships, widely used in deep learning applications.
🔍 Unsupervised Learning
Unsupervised learning algorithms work with data sets without labeled responses, aiming to infer the natural structure within the data. Common unsupervised learning techniques include:
1. Clustering: Algorithms like K-means, hierarchical clustering, and DBSCAN group objects based on similarities.
2. Association: Finds rules describing relationships within the data, such as market basket analysis.
3. Principal Component Analysis (PCA): Uses statistical techniques to transform correlated variables into uncorrelated ones.
4. Autoencoders: Special neural networks used to learn efficient codings of unlabeled data.
🔍 Reinforcement Learning
Reinforcement learning algorithms learn to make a sequence of decisions to achieve a goal in an uncertain environment. The agent follows a policy based on actions and learns from the consequences through rewards or penalties. Examples of reinforcement learning algorithms include:
1. Q-learning: A model-free algorithm that learns the value of actions in specific states.
2. Deep Q-Networks (DQN): Combines Q-learning with deep neural networks, learning policies directly from high-dimensional sensory inputs.
3. Policy Gradient Methods: Optimize policy parameters directly instead of estimating action values.
4. Monte Carlo Tree Search (MCTS): Used in decision processes to find optimal decisions by simulating scenarios, notably in games like Go.
These categories provide an overview of common machine learning algorithms, each with its strengths and ideal use cases. Depending on the task at hand, certain algorithms may be better suited than others.
#MachineLearning
Techeth Podcast S02E08 🎙
Guest: Lydia Abraham
Date: August 13, 2024
Time: 8:00 PM(2:00LT)
Meet Lydia, a rising star in tech! This brilliant Electrical and Computer Engineer, fresh out of AASTU, is not your average grad. She's a proven leader, having rocked GDSCAASTU as its lead and marketing mastermind.
But that's not all! Lydia's got a BA in Business Admin, is crushing it as a Data Analyst, and is obsessed with Machine Learning and AI. She's a multi-talented powerhouse with skills that'll blow your mind.
Tune in to the Techeth Podcast to discover how this amazing young woman is shaping the future of tech!
LIVE on Tuesday, August 13th at 2 PM LT to Learn about her journey, projects and insights on techeth!
@TechInEthio
#LydiaAbraham #GDSCAASTU #DataAnalyst #MachineLearning #AI #Tech #TechethPodcast
Guest: Lydia Abraham
Date: August 13, 2024
Time: 8:00 PM(2:00LT)
Meet Lydia, a rising star in tech! This brilliant Electrical and Computer Engineer, fresh out of AASTU, is not your average grad. She's a proven leader, having rocked GDSCAASTU as its lead and marketing mastermind.
But that's not all! Lydia's got a BA in Business Admin, is crushing it as a Data Analyst, and is obsessed with Machine Learning and AI. She's a multi-talented powerhouse with skills that'll blow your mind.
Tune in to the Techeth Podcast to discover how this amazing young woman is shaping the future of tech!
LIVE on Tuesday, August 13th at 2 PM LT to Learn about her journey, projects and insights on techeth!
@TechInEthio
#LydiaAbraham #GDSCAASTU #DataAnalyst #MachineLearning #AI #Tech #TechethPodcast
Techኢት Podcast S02E14 is out! 🎙
We had an amazing conversation with Dr. Wondwossen Mulugeta , VP for Institutional Development at Addis Ababa University, on the latest episode of the Techኢት Podcast. 🚀 We discussed his background, Natural Language Processing—especially in local languages—trending AI topics, and institutional matters at Addis Ababa University. Dr. Wondwossen also advised young students in tech. Don’t miss this fascinating discussion about Ethiopia's tech future! 🌍✨
🎧 Watch now: YouTube link
@Techinethio
#TechPodcast #AI #DigitalTransformation #NLP #EthiopianTech #Innovation #MachineLearning
We had an amazing conversation with Dr. Wondwossen Mulugeta , VP for Institutional Development at Addis Ababa University, on the latest episode of the Techኢት Podcast. 🚀 We discussed his background, Natural Language Processing—especially in local languages—trending AI topics, and institutional matters at Addis Ababa University. Dr. Wondwossen also advised young students in tech. Don’t miss this fascinating discussion about Ethiopia's tech future! 🌍✨
🎧 Watch now: YouTube link
@Techinethio
#TechPodcast #AI #DigitalTransformation #NLP #EthiopianTech #Innovation #MachineLearning
YouTube
TechኢትPodcast S02 Ep14 [Guest: Dr. Wondwossen Mulugeta ]
Dr. Wondwossen Mulugeta, Vice President for Institutional Development at Addis Ababa University, joins us on this episode of the Techኢት Podcast. With over 21 years in academic leadership, he's driving the university's digital transformation. As an Assistant…
Techኢት Podcast – AI & NLP with Dr. Martha Yifiru!
We’re excited to host Dr. Martha Yifiru, an instructor and researcher at Addis Ababa University, on the next episode of Techኢት Podcast!
Dr. Martha specializes in Artificial Intelligence (AI) and Natural Language Processing (NLP), with a focus on:
✅ ASR (Automatic Speech Recognition), TTS (Text-to-Speech), and Machine Translation
✅ AI applications in health, agriculture, and education
✅ Challenges and opportunities in developing NLP for Ethiopian languages
🔍 We’ll explore the impact of AI and NLP on Ethiopia and beyond, tackling key topics like AI ethics, local language models, dataset challenges, and the future of AI-powered innovations.
📅 Stay tuned for the episode!
@techinethio
#TechኢትPodcast #AI #NLP #ArtificialIntelligence #MachineLearning #Ethiopia #Innovation #TechDiscussion
We’re excited to host Dr. Martha Yifiru, an instructor and researcher at Addis Ababa University, on the next episode of Techኢት Podcast!
Dr. Martha specializes in Artificial Intelligence (AI) and Natural Language Processing (NLP), with a focus on:
✅ ASR (Automatic Speech Recognition), TTS (Text-to-Speech), and Machine Translation
✅ AI applications in health, agriculture, and education
✅ Challenges and opportunities in developing NLP for Ethiopian languages
🔍 We’ll explore the impact of AI and NLP on Ethiopia and beyond, tackling key topics like AI ethics, local language models, dataset challenges, and the future of AI-powered innovations.
📅 Stay tuned for the episode!
@techinethio
#TechኢትPodcast #AI #NLP #ArtificialIntelligence #MachineLearning #Ethiopia #Innovation #TechDiscussion
Calling all AI Enthusiasts! 🤖
iCog Labs is seeking talented individuals to join our 2025 batch 1 AI Talent Program. You'll work on cutting-edge projects, learn from experts, and grow your skills.
What we're looking for:
* Passion for AI
* Strong problem-solving skills
* A desire to learn and innovate
Deadline: July 31
Apply Now
https://lnkd.in/d6NXfkPP
@TechInEthio
#AICareers hashtag#MachineLearning hashtag#DataScience hashtag#TechJobs hashtag#AI hashtag#Ethiopia
iCog Labs is seeking talented individuals to join our 2025 batch 1 AI Talent Program. You'll work on cutting-edge projects, learn from experts, and grow your skills.
What we're looking for:
* Passion for AI
* Strong problem-solving skills
* A desire to learn and innovate
Deadline: July 31
Apply Now
https://lnkd.in/d6NXfkPP
@TechInEthio
#AICareers hashtag#MachineLearning hashtag#DataScience hashtag#TechJobs hashtag#AI hashtag#Ethiopia