Course detail
Machine learning foundations with data preparation, model training, evaluation and applied projects.
13th round and summer intake
Online classes are live and recorded. In-person learners can choose regular, extension or weekend attendance when available.
Train ML models
Evaluate performance
Prepare for advanced AI tracks
Course experience
Each monthly part should combine instructor-led lessons, resources, checklists, practice work and evaluation gates before learners move on.
Outline highlights
Career value
Machine Learning is the part of AI that allows systems to learn patterns from data instead of being programmed with every rule manually.
Learners study supervised and unsupervised learning, data preparation, model training, model evaluation and practical projects using Python libraries.
The focus is responsible, understandable model building: students learn why a model works, where it fails and how to evaluate it before using it in real decisions.
Explains recommendation systems, spam filters, fraud detection, image recognition and predictive analytics.
Builds strong problem-solving across data preparation, feature engineering, model selection and evaluation.
Connects Python and data science to high-demand AI engineering pathways.
Prepares learners to build prototypes that solve business, finance, education, health and operations problems.
ይህ ኮርስ የMachine Learning መሰረታዊ ነገሮችን ከ data preparation, model training, evaluation እና ተግባራዊ ፕሮጀክቶች ጋር ያካተተ ነው።
Machine Learning ስርዓቶች በእጅ ህግጋት ሳይፃፉ ከ data ላይ ቅጦችን እንዲማሩ የሚያስችል የAI ክፍል ነው።
ተማሪዎች supervised እና unsupervised learning, data preparation, model training, model evaluation እና ተግባራዊ ፕሮጀክቶችን በPython libraries በመጠቀም ያጠናሉ።
ML models ለማሰልጠን፣ performance ን ለመገምገም እና ለላቀ AI ትምህርት ለመዘጋጀት ክህሎት ማግኘት።
ስለ recommendation systems, spam filters, fraud detection, image recognition እና predictive analytics ማብራሪያዎችን መማር።
በ data preparation, feature engineering, model selection እና evaluation ዙሪያ ጠንካራ ችግር ፈቺነትን መገንባት።
Python እና data science ን ከከፍተኛ ፍላጎት ካለው AI engineering pathways ጋር ማገናኘት።
Machine Learning (ML) ማሽኖች ከተከማቸ ዳታ በመማር፣ በተለያዩ ሁኔታዎች ውስጥ ያለ ሰብዓዊ ጣልቃገብነት ውሳኔ እንዲያደርጉ የሚያስችል የሰው ሰራሽ እውቀት (AI) ቅርንጫፍ ነው።
ይህም ዳታዎችን መተንተን፣ ቅጦችን መለየት እና ትንበያዎችን መስጠት ያካትታል።
MiT የsupervised learning, model evaluation እና ተግባራዊ የML ፕሮጀክቶችን በተግባር ያስተምራል።
Machine Learning በዘመናዊ ቴክኖሎጂ ውስጥ ቁልፍ ሚና ስለሚጫወት (ለምሳሌ recommendation systems, spam filters, fraud detection) የML ክህሎት ከፍተኛ ተፈላጊነት አለው።
ይህ ኮርስ በ data preparation, feature engineering, model selection እና evaluation ዙሪያ ጠንካራ ችግር ፈቺነትን ይገነባል።
የPython እና data science ን ከከፍተኛ ፍላጎት ካለው AI engineering ጋር ያገናኛል።
በዚህ ኮርስ የML models ያሰለጥናሉ።
Performance ን ይገመግማሉ።
ለላቀ AI ትምህርት ይዘጋጃሉ።
Customer churn prediction, sales ወይም demand forecasting, disease-risk demo model with responsible caveats, digit recognition ወይም image classification prototype ይሰራሉ።
Learn from practitioners who ship production software, campaigns and creative work — then guide your evaluations in the LMS.
AI & Machine Learning
ML, DL & AI Instructor
Guides machine learning, deep learning and applied AI project tracks.
Resources are organized by term, monthly part, week and lesson. Full Stack access opens one part at a time after payment and instructor evaluation.
Unlock after payment or instructor evaluation.
Unlock after payment or instructor evaluation.
Best for students who want structured lab access, instructor support and consistent weekday practice.
Designed for learners who need evening sessions three days per week after work or daytime study.
A practical option for working learners and university students who need weekend attendance.
Live virtual classes are recorded so students can review lessons, recap missed points and continue studying after class.
Available for courses longer than three months when tuition is paid in full.
Available when one student registers for two or three courses.
Special support for students who did not pass university entrance and are not eligible for remedial placement.
For online Full Stack enrollment, one eligible family member can receive tuition waived under the family offer.
Machine Learning3 months
Neural network foundations, modern model concepts and applied deep learning workflows.
Python3 months
Data analysis, statistics, visualization and practical workflows for decision-making.
Python3 months
Python foundations for automation, problem solving, data handling and future AI or data science learning.
Machine Learning3 months
AI concepts, responsible use, model workflows and practical applications for modern careers.
