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AI/Machine Learning For Executives & Managers
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Course ContentUnit: 1 : Introduction for Data Analytics , Machine Learning and Artificial intelligence6 Topics
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Unit : 2 : Machine Learning : Understanding Jargons9 Topics
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Unit :3 Building Data Science team and responsibility Assignment6 Topics
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Unit: 4 Advanced Topics and Usecases in Machine Learning6 Topics
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Unit : 5 Develop Border and Understanding with Case studies4 Topics
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Unit 6: Introduction to Python5 Topics
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Unit 8: Unsupervised Machine Learning : CLustering4 Topics
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Unit : 9 Deep Learning Foundation4 Topics
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Unit 7: Foundation building in Machine Learning Techniques18 Topics
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Supervised Machine Learning Algorithms
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Application in predictive Analytics
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Linear Regression: Single and Multiple Linear Regression (Estimation)
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Modelling and Prediction
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coefficient of determination
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confidence and prediction intervals
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categorical variables, outliers
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Hands-on Demo
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Supervised Machine Learning with application in Classification (Prediction)
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Linear Classification: Logistic Regression
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Implementation and optimization
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Estimation of probability using logistic regression
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ROC Curve, Feature selection in logistic regression
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Naïve Bayes: Bayes Theorem, Naïve Bayes Classifier
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K Nearest Neighbor Algorithm (KNN)
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Support Vector Machine: Linear Support Vector Machine, Kernel-based Classification, Controlled Support Vector Machine, Support Vector Regression
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Decision Tree: Training and Visualizing Decision Tree, CART Training algorithm, Impurity measures, Gini Impurity index, Cross-entropy impurity index, Misclassification impurity index, feature importance in tree
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Hands-on demo
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Supervised Machine Learning Algorithms
Lesson 9, Topic 5
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coefficient of determination
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