{"id":5735,"date":"2025-10-29T09:40:16","date_gmt":"2025-10-29T09:40:16","guid":{"rendered":"https:\/\/omxwebsites.com\/gammatica\/?page_id=5735"},"modified":"2025-11-07T06:40:55","modified_gmt":"2025-11-07T06:40:55","slug":"machine-learning-essentials","status":"publish","type":"page","link":"https:\/\/omxwebsites.com\/gammatica\/machine-learning-essentials\/","title":{"rendered":"Machine Learning Essentials"},"content":{"rendered":"\t\t
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Machine Learning Essentials\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Learning Format<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Syllabus<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Certification<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Machine Learning Essentials\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Machine Learning Essentials<\/strong> provides a comprehensive introduction to the core concepts and techniques of machine learning. It focuses on teaching how algorithms learn from data to make predictions and decisions without explicit programming. Learners explore key topics such as supervised and unsupervised learning, model evaluation, and data preprocessing. This course builds a strong foundation for applying machine learning in real-world scenarios across industries like finance, healthcare, and technology.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Introduction to Machine<\/strong><\/p>

  • Learning concepts<\/li>
  • Supervised vs Unsupervised learning<\/li>
  • Assignment at end of week<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
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    • Regression models:<\/strong> Linear, Logistic<\/li>
    • \u00a0Hands-on with Scikit-learn<\/li>
    • Assignment at end of week<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
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      • Classification: Decision Trees, Random Forest, SVM<\/li>
      • \u00a0Assignment at end of week<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
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        • Clustering: K-means, DBSCAN<\/li>
        • Evaluation metrics for clustering<\/li>
        • Assignment at end of week<\/li>
        • \u00a0Mock Interview 1<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
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          Model evaluation:<\/strong><\/p>

          • accuracy, precision, recall, F1-score<\/li>
          • Cross-validation techniques<\/li>
          • \u00a0Assignment at end of week<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
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            • Feature engineering & scaling<\/li>
            • \u00a0Assignment at end of week<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
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              • Hyperparameter tuning (GridSearch, RandomSearch)<\/li>
              • Assignment at end of week<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
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                • End-to-end ML project<\/li>
                • \u00a0Final project presentation<\/li>
                • \u00a0Mock Interview 2<\/li>
                • \u00a0Assignment at end of week<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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                  \n\t\t\t\t\t\tTech & Non-Tech Working professional, Freshers, Graduate from any domain.\t\t\t\t\t<\/p>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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                  Machine Learning Essentials Learning Format Online mode Total training duration 80-90 hrs (2 months) Syllabus 8 weeks Certification Yes Machine Learning Essentials Machine Learning Essentials provides a comprehensive introduction to the core concepts and techniques of machine learning. It focuses on teaching how algorithms learn from data to make predictions and decisions without explicit programming. Learners explore key topics such as supervised and unsupervised learning, model evaluation, and data preprocessing. This course builds a strong foundation for applying machine learning in real-world scenarios across industries like finance, healthcare, and technology. Enroll Now Syllabus Summary Week 1 Introduction to Machine Learning concepts Supervised vs Unsupervised learning Assignment at end of week Week 2 Regression models: Linear, Logistic \u00a0Hands-on with Scikit-learn Assignment at end of week Week 3 Classification: Decision Trees, Random Forest, SVM \u00a0Assignment at end of week Week 4 Clustering: K-means, DBSCAN Evaluation metrics for clustering Assignment at end of week \u00a0Mock Interview 1 Week 5 Model evaluation: accuracy, precision, recall, F1-score Cross-validation techniques \u00a0Assignment at end of week Week 6 Feature engineering & scaling \u00a0Assignment at end of week Week 7 Hyperparameter tuning (GridSearch, RandomSearch) Assignment at end of week Week 8 End-to-end ML project \u00a0Final project presentation \u00a0Mock Interview 2 \u00a0Assignment at end of week Introduction to Machine Learning concepts Supervised vs Unsupervised learning Assignment at end of week Regression models: Linear, Logistic \u00a0Hands-on with Scikit-learn Assignment at end of week Classification: Decision Trees, Random Forest, SVM \u00a0Assignment at end of week Clustering: K-means, DBSCAN Evaluation metrics for clustering Assignment at end of week \u00a0Mock Interview 1 Model evaluation: accuracy, precision, recall, F1-score Cross-validation techniques \u00a0Assignment at end of week Feature engineering & scaling \u00a0Assignment at end of week Hyperparameter tuning (GridSearch, RandomSearch) Assignment at end of week End-to-end ML project \u00a0Final project presentation \u00a0Mock Interview 2 \u00a0Assignment at end of week Course Summary Eligibility Tech & Non-Tech Working professional, Freshers, Graduate from any domain. Live Doubt Solving Get your queries solved with daily dedicated doubts solving sessions. Instructor Experts and trainer for top-tech companies. Certification 10+ ISO Globally recognized certified Mode of Learning 100% Live Learning with experienced instructors and hands-on sessions. Real time projects Get practical experience with real-world projects for a career in analytics. 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