Back to Course
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
-
Hands-on practice
-
Stochastic Gradient Descent Optimization, coefficient of determination, significance tests, Confidence and prediction intervals, categorical variables, Outliers, auto-regression and transformation of variables, Polynomial Regression
-
Random Forests, Feature importance
-
Stacking
-
Hands-on 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, Kernel-based Classification, Controlled Support Vector Machine, Support Vector Regression
-
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
-
Various time series models for modelling and predicting
-
Hands-on 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 8, Topic 4
In Progress
Modelling and Prediction
Lesson Progress
0% Complete