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Artificial Intelligence for Business Course

28 September, 2019 to 26 October, 2019

Duration
5 days: delivered 1 day per week for 5 weeks

Class Schedule:
Five Saturdays 0830Hrs to 1600Hrs: September 2019, 28; October 2019, 5,12,19,26

COURSE OVERVIEW

Leading organizations are using artificial intelligence (AI) across business functions to improve customer service, reduce risk, increase revenue, and optimize processes to increase efficiency.

However, the adoption of AI has remained out of reach of many organizations in mainstream government, industry and commerce because of the acute shortage of the needed specialized talent.

This course provides practical, comprehensive training that enables participants to immediately and effectively partake in enterprise AI projects. Designed for executives and professionals in all business functions across all industries, it does not require participants to have a technical background to benefit. The course provides a practical framework for understanding AI and how to adopt it in the enterprise, and grounding in basic and advanced concepts, considerations and techniques for building and deploying AI applications. Exercises, homeworks and case-studies, as well as hands-on labs using selected modern enterprise AI platforms, enhance participants’ learning and expose them to practical environments. The course includes a group project and an individual project that enable participants to apply the concepts learnt in class to real world business challenges.

In addition, the course prepares participants for exams for internationally recognized AI certifications from RapidMiner. RapidMiner’s platform has been consistently ranked a top three enterprise data science and machine learning platform for the past 6 years by Gartner.

TARGET AUDIENCE
Executives and professionals from all business functions across all industries including executive management, sales, marketing, risk and compliance, procurement, engineering, IT, operations, finance, and analytics.

PREREQUISITES
No prior knowledge of data science, machine learning, programming, or statistics is assumed
Ability to use computer programs such as Excel and business applications and knowledge of basic mathematics

OUTCOMES
Upon successful completion of the course, the participant should be able to:
Identify tasks that can be automated by using AI
Use a structured approach to deliver AI projects
Identify business and technical objectives of an AI project
Build a team with appropriate skills for successfully delivering the project
Identify data sources and perform common data preparations
Apply appropriate techniques and tools to build effective AI models
Evaluate technical performance and fitness of purpose of AI models
Select an enterprise AI development platform
Use a modern AI platform to build and deploy AI models
Use the results from an AI application
Understand the ethical, societal, and legal issues associated with AI

Download the course brochure here

COURSE OUTLINE

Module 1: Artificial Intelligence in the Enterprise
1. Introduction to AI
What is AI?
Types of AI
Key AI technologies
AI, machine learning, deep learning and data science
What AI can do and cannot do
A brief history of AI
Exercises

2. AI in the Enterprise
Applications of AI
Artificial intelligence and enterprise decisions
Identifying opportunities for AI application
Automating enterprise processes
Approaches to AI adoption
Pragmatic enterprise AI
Myths and realities of artificial intelligence
Exercises

3. Building an Enterprise AI Application
Anatomy of an AI application
Methodologies for building AI applications
Overview of enterprise AI tools and platforms
Selecting an AI vendor
Hands-on lab: Introduction to KNIME, RapidMiner, Dataiku

4a. Understanding the Business Problem
Defining the business problem
Converting a business problem into an AI problem
Key roles for a successful AI project
Case study

4b. Building a Dataset
Sources of data
Types of data
Data selection criteria
Case study
Basic statistical measures
Hands-on lab: Visualizing data

5. Data Preparation
Types of data attributes
Transforming attribute values
Data cleaning
Feature selection
Feature generation
Hands-on lab: Data preparation

6. Building and Evaluating AI Models
Selecting algorithms
Bias, variance, overfitting and underfitting
Model training, validation and testing
Cross validation
Evaluation methods and performance criteria
Optimization and parameter tuning
Model selection
Evaluation against business objective
Hands-on lab: Evaluating model performance

Module 2: Fundamental AI Techniques and Algorithms – A Case Study, Hands-On Approach

1. Case study: Price Determination
Modeling methods: Linear regression, k-nearest neighbour, decision trees

2. Case study: Customer Segmentation
Modeling methods: k-means clustering, association rules

Module 3: Fundamental AI Techniques and Algorithms – A Case Study, Hands-On Approach

1. Case study: Churn Prediction
Modeling methods: Logistic regression, linear discriminant analysis, naïve Bayes classifier

2. Case study: Image Recognition
Modeling methods: Neural networks, deep learning

Module 4: Advanced AI Techniques and Algorithms – A Case Study, Hands-On Approach

1. Case study: Customer Segmentation (revisited)
Modeling methods: Social network analysis

2. Case study: Churn Prediction (revisited)
Modeling methods: Support vector machines

3. Case study: Detecting DDoS Cyber-attack
Modeling methods: Ensemble methods

4. Case study: Recommendation Engine
Modeling methods: Collaborative filtering, content-based filtering

5. Case study: Revenue Management
Modeling methods: Time series forecasting

Module 5a: Advanced AI Techniques and Algorithms – A Case Study, Hands-On Approach

1. Case study: Sentiment Analysis
Modeling methods: Natural language processing

2. Case study: Building a Chatbot
Modeling methods: Natural language processing

Day 5b: Deployment and Considerations

1. Deploying AI Models
Deployment considerations
Deployment approaches
Performance monitoring
Case study

2. Ethical, Societal and Legal Considerations
Society’s perception of AI
Bias
Privacy
Security implications: digital, physical and political
The future of work
Case study
Exercises

COURSE DELIVERY

Duration: 5 days, delivered 1 day per week for 5 weeks
Classroom: Lectures, exercises, case-studies, hand-on labs on enterprise AI platforms
Homework: At the end of each module
Group project
Individual work-related project (submission for evaluation by course instructor is optional but recommended)

COURSE DATES

Five Saturdays 0830Hrs to 1600Hrs: September 2019, 28; October 2019, 5,12,19,26

COURSE MATERIAL

Lecture notes
Guide notes for group project
Workbook for individual work-related project

CERTIFICATES

Earn a course certificate upon fulfillment of course requirements

RapidMiner Certifications
The course prepares participants for exams for internationally recognized AI certifications from RapidMiner. RapidMiner’s platform has been consistently ranked a top three enterprise data science and machine learning platform for the past 6 years by Gartner.

VENUE

6th Floor Batanai Gardens, Corner Jason Moyo Avenue/First Street, Harare, Zimbabwe

REGISTRATION

Register for your course of interest by completing the registration form at the top of this page.

PRICE PER PARTICIPANT

RTGS$13,330

PAYMENT

Deposit/transfer payments into the bank account below and email proof of payment to events@sdscope.com
Bank Name: CABS    Account Name: Softclick Investments    Account Number: 1005111049

If you require a quotation, invoice or proforma invoice send your request to events@sdscope.com

COURSE INSTRUCTOR

Shepherd Fungayi    CEO at SDscope
Certified Machine Learning Master – verify credential here
Certified Data Engineering Professional – verify credential here
Certified AI Applications and Use Cases Professional – verify credential here
Started professional career as a telecommunications engineer over 20 years ago
Deep understanding of ICT technology architectures
Experience includes projects in IT, data analytics and AI in telecommunications and financial industries
Profile: www.linkedin.com/in/shepherd-fungayi

CONTACT DETAILS

Softclick Investments (Pvt) Ltd t/a SDscope
6th Floor Batanai Gardens
57 Jason Moyo Avenue
Harare, Zimbabwe
Phone: +263 242 794 086
Mobile: +263 77 341 9956
Email: events@sdscope.com
Website: www.sdscope.com

Download the course brochure here

Venue

Live online
on Zoom, Teams, Google Meet or similar platform + Google Map
Phone:
+263 77 341 9956
Website:
www.sdscope.com/events/machine-learning-for-business
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