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DTSTART;TZID=UTC:20260222T080000
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SUMMARY:Training :-  Machine Learning Using Python
DESCRIPTION:Proposal for Training on Machine Learning Using PythonTraining Instructor: Dr. Ashish SharmaTotal Sessions: 06Session Duration: 30 – 45 minutes eachMode: Offline mode \n\n\n\n\nIntroductionMachine Learning (ML) has become a core component of modern technology\, enabling systemsto learn from data and make intelligent decisions without explicit programming. From healthcareand finance to education and engineering\, ML applications are transforming industries and creatingnew opportunities.Python is the most widely used programming language for Machine Learning due to its simplicity\,readability\, and powerful ecosystem of libraries such as NumPy\, Pandas\, Matplotlib\, and Scikitlearn. This short-term training program is designed to introduce participants to the fundamentalsof Machine Learning using Python in a concise\, structured\, and practical manner.Given the limited duration of each session (30 – 45 minutes)\, the training focuses on conceptualclarity\, essential algorithms\, and hands-on exposure\, ensuring that learners gain meaningfulunderstanding without being overwhelmed.\n\n\n\nObjectives of the TrainingThe primary objectives of this training program are to:\n\n\n\n\n\nIntroduce the basic concepts and terminology of Machine Learning\n\n\n\nFamiliarize participants with Python as a tool for ML\n\n\n\nExplain different types of Machine Learning techniques\n\n\n\nDemonstrate simple ML workflows using real-world datasets\n\n\n\nEnable participants to understand how ML models are built\, trained\, and evaluated\n\n\n\nBuild confidence to explore advanced ML topics independently after the training\n\n\n\n\n\nExpected OutcomesAfter completing the six-session training\, participants will be able to:\n\n\n\n\nImplement simple Machine Learning models using Scikit-learn \n\n\n\nUnderstand the fundamentals and importance of Machine Learning \n\n\n\nDifferentiate between supervised\, unsupervised\, and basic learning approaches \n\n\n\nUse Python libraries for data handling and visualization \n\n\n\n\nApply foundational ML knowledge to academic\, research\, or practical problem-solvingcontexts\n\n\n\n\n\nTarget AudienceThis training is suitable for:\n\n\n\n\n\nUndergraduate and postgraduate students\n\n\n\nResearch scholars\n\n\n\nFaculty members\n\n\n\nProfessionals from technical and non-technical backgrounds\n\n\n\nAnyone with basic programming knowledge or interest in Machine LearningBasic familiarity with Python is helpful but not mandatory.\n\n\n\n\n\nTraining Structure and Session-wise PlanSession 1: Introduction to Machine LearningContent:\n\n\n\n\n\nWhat is Machine Learning?\n\n\n\nDifference between AI\, ML\, and Data Science\n\n\n\nReal-world applications of ML\n\n\n\nTypes of Machine Learning (Supervised\, Unsupervised\, Reinforcement – overview)Outcome:Participants will gain a clear understanding of what Machine Learning is and where it is used.Session 2: Python for Machine LearningContent:\n\n\n\nWhy Python for Machine Learning?\n\n\n\nOverview of essential libraries: NumPy\, Pandas\, Matplotlib\n\n\n\nWorking with datasets (loading and inspecting data)\n\n\n\nBasic data operations and visualization\n\n\n\n\nOutcome:Participants will be able to handle and explore datasets using Python.Session 3: Data Preprocessing and ExplorationContent: \n\n\n\n\nImportance of data preprocessing\n\n\n\nHandling missing values\n\n\n\nFeature selection and scaling (conceptual overview)\n\n\n\nExploratory Data Analysis (EDA)Outcome:Participants will understand how raw data is prepared for Machine Learning models.Session 4: Supervised Learning – RegressionContent:\n\n\n\nConcept of supervised learning\n\n\n\nIntroduction to regression problems\n\n\n\nLinear Regression: concept and example\n\n\n\nModel training and prediction using Scikit-learnOutcome:Participants will be able to build a basic regression model using Python.Session 5: Supervised Learning – ClassificationContent:\n\n\n\nClassification problems and use cases\n\n\n\nCommon algorithms (Logistic Regression\, KNN – conceptual overview)\n\n\n\nSimple classification example using Scikit-learn\n\n\n\nModel evaluation (accuracy\, confusion matrix – basic idea)Outcome:Participants will understand how classification models work and how their performance ismeasured.\n\n\n\nSession 6: Unsupervised Learning and Conclusion\n\n\n\nContent:\n\n\n\nIntroduction to unsupervised learning\n\n\n\nClustering concepts (K-Means – overview)\n\n\n\nSimple clustering demonstration\n\n\n\nSummary of the complete ML workflow\n\n\n\nCareer paths and next steps in Machine LearningOutcome:Participants will gain exposure to unsupervised learning and understand how to continue learningML.\n\n\n\nTraining Methodology\n\n\n\nShort conceptual explanations\n\n\n\nLive coding demonstrations\n\n\n\nReal-world examples and datasets\n\n\n\nInteractive discussions and Q&A\n\n\n\nStep-by-step explanation of ML workflows\n\n\n\nConclusionThis six-session training program on Machine Learning using Python is designed to provide astrong foundation within a limited time frame. By combining clear explanations with practicaldemonstrations\, the training ensures that participants not only understand the theory but also seehow Machine Learning is applied in practice. Under the guidance of Dr. Ashish Sharma\,participants will be well-prepared to explore advanced Machine Learning concepts andapplications in the future.Proposed by:Asst. Prof. (Dr.) Ashish Sharma\,College of Engineering & Computer Science\,Lebanese French University\, Erbil\, Kurdistan.Training Instructor – Machine Learning using Python
URL:https://lfu.edu.krd/event/training-machine-learning-using-python/
CATEGORIES:College of Engineering and Computer Science
ATTACH;FMTTYPE=image/jpeg:https://lfu.edu.krd/wp-content/uploads/2026/02/poster.jpg
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DTSTART;TZID=UTC:20260222T080000
DTEND;TZID=UTC:20260222T170000
DTSTAMP:20260521T170827
CREATED:20260222T063019Z
LAST-MODIFIED:20260222T063401Z
UID:52603-1771747200-1771779600@lfu.edu.krd
SUMMARY:Nutrition During Fasting: Ramadan and Intermittent Fasting
DESCRIPTION:
URL:https://lfu.edu.krd/event/nutrition-during-fasting-ramadan-and-intermittent-fasting/
CATEGORIES:College of Administration and Economics,College of Health Science
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