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AI/ML for Practitioners

Unit: 1 Introduction for Data Analytics, Machine Learning and Artificial Intelligence5 Topics

Unit 2: Machine Learning: Understanding jargons9 Topics

Unit 3: Building a data science team and responsibility assignment6 Topics

Unit 4: Python: Journey from Foundation Level9 Topics

Unit 5: Advanced Topics Overview in Machine Learning6 Topics

Unit 6: Statistical: Foundation building Block for Machine Learning9 Topics

Descriptive Statistics

Laws and Axioms of Probability

Probability Distribution

Hypothesis Testing and Scores

Handson practice

Stochastic Gradient Descent Optimization, coefficient of determination, significance tests, Confidence and prediction intervals, categorical variables, Outliers, autoregression and transformation of variables, Polynomial Regression

Random Forests, Feature importance

Stacking

Handson practice in Python

Descriptive Statistics

Unit 7: Applied Python with data analytics Libraries4 Topics

Unit 8: Foundation building in Machine Learning Techniques7 Topics

Unit 9: Supervised Machine Learning with application in Classification (Prediction)10 Topics

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

Various time series models for modelling and predicting

Handson demo in Python

Linear Classification: Logistic Regression

Unit 10: Unsupervised Machine Learning: Clustering4 Topics

Unit 11: Case studies: Discussions and implementations – I2 Topics

Unit 12: Case studies: Discussions and implementations – II3 Topics

Unit 13: Deep Learning foundation4 Topics
Lesson 6, Topic 7
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Random Forests, Feature importance
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