syllabus

Introduction

• Introduction to Data Science and AI  

• Introduction to Machine Learning 

Linear regression 

• Model Representation

• Cost Function

• Gradient Descent

• Gradient Descent for Linear Regression 

• Lab Linear Regression with MATLAB

Logistic regression 

• Logistic Regression

• Cost Function of Logistic Regression

• Advance Optimization

• Multi-Class Classification

• Lab Logistic Regression with MATLAB

Neural network 

• Human Brain

• Model Representation

• Vectorized Implementation

• Multi-Class Classification

• Lab Neural Network with MATLAB

Decision tree 

• Decision Tree

• Basic Algorithm 

• Information Gain

• Overfitting In Decision Tree

• Lab Decision Tree with MATLAB

Model Selection 

• Evaluation a Hypothesis

• Training/Validation/Test sets

• Diagnosing bias vs. variance

• Learning curves

• Lab Model-Selection with MATLAB

K-mean  

• K-Mean Algorithm

• Optimization Objective

• Choosing the number of clusters

DBScan  

• DBScan Algorithm 

• DBScab Example 

• Lab K-Mean and DBScan with MATLAB

Dimensionality Reduction 

• PCA 

• PCA Algorithm

• PCA Reconstruction 

• Choosing K for PCA 

• Lab PCA with MATLAB

Anomaly Detection  

• Introduction

• Gaussian Distribution 

• Anomaly Detection Algorithm

• Developing and Evaluating Anomaly Detection

• Select the feature

• Multivariate Gaussian Distribution  

• Lab Anomaly Detection with MATLAB

Introduction to Data Mining  

• CRISP-DM Process

• Introduction to RapidMiner

• Data Exploration

• Data Transformation

• Data Cleansing

Model Selection and Evaluation 

• Recap Machine Learning Algorithm

• Evaluation Techniques

• Hyperparameter Tuning

• Complex Data Mining Process Case Studies

Model Deployment

• Feature Selection Method

• Imbalanced Data Handling

• Model Explanation

• Model Deployment and Production

• Prescriptive Analytics

Introduction to Business Intelligence 

• 5 Elements of BI 

• DATA Preparation

• Explore and Visualization 

Interactive Dashboard 

• Calculation Function 

• DAX Function 

• Public Dashboard 

• BI Tip 

Project 

• Make Executive Decision Support Systems 

• Build Ecosystem of BI 

Determine Business Objectives  

• Business Objectives

• Business Problems

• Business Success Criteria

• Learning style: Workshop, Group Discussion, and Group Work

Assess Situation  

• Risk and Contingencies Assessment

• Resources Assessment

• Human Preferences

• Know yours and other’s preference

• Costs and Benefits analysis

• Assumptions and Constraints

• Learning style: Workshop, Game, Group Discussion, and Group Work

Determine Analytic goals and success criteria 

• Translate business goal into analytics goal

• Describe the intended outputs of the project that enable the achievement of the business objectives

• Define the criteria for a successful outcome to the project in technical terms

• Learning style: Workshop, Game, Group Discussion, and Group Work

Produce Project Plan  

• Project Objectives

• Project Planning

• Project Scheduling

• Story Telling to Stakeholders per Their Thinking Preferences

• Learning style: Workshop, Group Discussion, and Group Work