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.
ማሽን ለርኒንግ ማለት ኮምፒውተሮች ከብዙ ዳታ በራሳቸው ቅጦችን እንዲለዩ፣ ግንኙነቶችን እንዲረዱ እና ትንበያ/ውሳኔ እንዲሰጡ የሚያስችል መስክ ነው።
ዳታ ነዳጅ ከሆነ፣ ማሽን ለርኒንግ ሞተሩ ነው። ሞዴሉ ከዳታ ይማራል፣ ከዚያም በአዲስ መረጃ ላይ የተመሰረተ መገመት ይችላል።
በዚህ ኮርስ supervised learning, regression, classification, clustering, feature engineering, model evaluation እና practical ML projects ይሸፈናሉ።
ትንበያ፣ classification እና recommendation ስርዓቶችን ለመገንባት ያስችላል።
የዳታ ቅጦችን እና የተደበቁ ግንኙነቶችን ለመረዳት ያግዛል።
ለ AI Engineer, Data Scientist እና ML Engineer መንገድ ይከፍታል።
ማሽን ለርኒንግ ኮምፒውተር ህጎችን በእጅ ብቻ እንዲከተል ሳይሆን ከዳታ በራሱ እንዲማር ያደርጋል። ለምሳሌ የደንበኛ ባህሪን መገመት፣ spam ኢሜይል መለየት፣ የሽያጭ ትንበያ ማድረግ እና recommendation ስርዓት መገንባት ይቻላል።
ትምህርቱ ዳታን ማዘጋጀት፣ features መፍጠር፣ model መምረጥ፣ training/testing ማድረግ፣ accuracy, precision, recall እና confusion matrix የመሳሰሉ መለኪያዎችን መተርጎም ያካትታል።
Supervised learning በቀድሞ የተመለያየ ዳታ ላይ በመማር አዲስ ነገርን ይመድባል ወይም ይገመታል። Regression የቁጥር ውጤት ለመገመት፣ Classification ደግሞ ነገሮችን በምድብ ለመከፋፈል ይጠቅማል።
Unsupervised learning ደግሞ ያልተመለያየ ዳታ ውስጥ ተመሳሳይ ቡድኖችን ወይም ቅጦችን ያገኛል። Clustering የደንበኞችን ባህሪ፣ market segments ወይም hidden patterns ለመለየት ይጠቅማል።
ተማሪዎች Python, Pandas, NumPy, Scikit-learn, visualization tools እና Jupyter Notebook በመጠቀም spam detector, sales forecasting, customer churn prediction, image classification እና recommendation prototype ይሰራሉ።
የMiT አቀራረብ ሞዴል ብቻ ማሰራት አይደለም፤ ውጤቱ በእውነት የሚታመን ነው? ዳታው የተዘረፈ ነው? ሞዴሉ bias አለው? በቢዝነስ ውሳኔ ላይ መጠቀም ይገባል? የሚሉ ጥያቄዎችንም ያስተምራል።
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.
