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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

Unit : 2 : Machine Learning : Understanding Jargons9 Topics

Unit :3 Building Data Science team and responsibility Assignment6 Topics

Unit: 4 Advanced Topics and Usecases in Machine Learning6 Topics

Unit : 5 Develop Border and Understanding with Case studies4 Topics

Unit 6: Introduction to Python5 Topics

Unit 8: Unsupervised Machine Learning : CLustering4 Topics

Unit : 9 Deep Learning Foundation4 Topics

Unit 7: Foundation building in Machine Learning Techniques18 Topics

Supervised Machine Learning Algorithms

Application in predictive Analytics

Linear Regression: Single and Multiple Linear Regression (Estimation)

Modelling and Prediction

coefficient of determination

confidence and prediction intervals

categorical variables, outliers

Handson Demo

Supervised Machine Learning with application in Classification (Prediction)

Linear Classification: Logistic Regression

Implementation and optimization

Estimation of probability using logistic regression

ROC Curve, Feature selection in logistic regression

Naïve Bayes: Bayes Theorem, Naïve Bayes Classifier

K Nearest Neighbor Algorithm (KNN)

Support Vector Machine: Linear Support Vector Machine, Kernelbased Classification, Controlled Support Vector Machine, Support Vector Regression

Decision Tree: Training and Visualizing Decision Tree, CART Training algorithm, Impurity measures, Gini Impurity index, Crossentropy impurity index, Misclassification impurity index, feature importance in tree

Handson demo

Supervised Machine Learning Algorithms
Lesson 9, Topic 14
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Naïve Bayes: Bayes Theorem, Naïve Bayes Classifier
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