Artificial Intelligence and Machine Learning Overview

Course number: CGIAIMLO40

This course will provide students with a solid ‘first-look’ at essential AI and machine learning technologies, related cutting-edge skills, and the innovative tools that support them, with a focus on how they are applied in business in a practical way. These transformative technologies offer paths to enhance decision-making, quickly modernize and improve operational efficiency, and drive innovation within organizations.

The course consists of lectures, valuable demonstrations and group discussions, along with light-hands-on activities working with some of the core skills and tools. Throughout the course, students will learn what AI is and isn’t, differentiating between AI, Machine Learning and Deep Learning, and what’s happening with trending Large Language Model use. Students will examine processing language, images, and audio with AI.

Course objectives

Gain a clear introduction to the exciting world of AI and Machine Learning, along with current trends and tools, enabling you to participate confidently in strategic discussions and help to make informed decisions.
Get a hands-on introduction to a few user-friendly tools and skills offering you a taste of the practical side of AI and Machine Learning.
Explore the practical business applications of AI and Machine Learning in an everyday context, and the potential influence of these technologies on your organization.
Learn the basics about algorithms, NLP, Neural Networks, GPT, Generative AI and other innovative tools, and how
Explore how AI plays a pivotal role in processing language, images, and audio
Learn how organizations maximize use of these technologies to their benefit to improve processes and maximize efficiencies

Prerequisites

Students attending this class should have a grounding in Enterprise computing, and a somewhat technical background, as well asl familiarity with Enterprise IT, a general understanding of systems architecture, and some knowledge of the business drivers that may benefit from applying AI.
Basic knowledge of Python scripting is also helpful but not required. Prior experience with Python can be helpful but is not necessary.

Target Audience

This course is ideally suited for a wide variety of technical learners just getting started with AI or machine Learning, seeking a primer-level overview of these technologies, skills and related tools. Attendees might include:

Developers aspiring to be a ‘Data Scientist’ or Machine Learning engineers
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand data science techniques
Information Architects who want to gain expertise in Machine Learning algorithms
Analytics professionals who want to work in machine learning or artificial intelligence
Graduates looking to build a career in Data Science and machine learning
Experienced professionals who would like to harness machine learning in their fields to get more insight about customers

Course Content

1. Introduction to AI & Machine Learning

Understand what AI and Machine Learning are and why they’re critical for modern business
Exploring definitions and types of AI
Discussing AI in the Modern Age and its role in business
Embrace Change: Learn and Build Confidence using the Tools – Don’t be Replaced By Them

2. Deeper Dive into Machine Learning

Basics of how mathematics are used in or apply to AI
Algorithms: What are they and how are they used in AI and ML
Supervised vs Unsupervised
Classification, Regression, Clustering, Dimensionality Reduction, and Ensemble Methods
The role of Machine Learning in AI and business decision-making
Review a real business scenario where Machine Learning was used to increase efficiency

3. Leveraging AI in Business & Decision Making

Discussing key business areas where AI adds value: Operations, Marketing, Sales, HR, content development, coding and software development
Explore how AI is used in business decision-making
Introduction to predictive analytics
Using AI for strategic decision-making

4. Hot Trends for AI in Business: Large Language Models (LLM), Generative AI and GPT

Understand the basics of Generative AI and how it differs from other AI techniques
Introduction to GPT and its applications in various sectors
Explore how GPT uses machine learning to generate human-like text based on the input it receives.
Understand the concept of language models and how they are trained using large amounts of text data

5. Basics of Neural Networks

What are they and how are they used?
Basic parts: Neurons, activation functions, interactions.
Types: Feedforward, recurrent, convolutional neural networks overview.
How they learn: Forward propagation, backpropagation explained.
Training Neural Networks: Importance of data preprocessing in training.
Deep Neural Networks: Advantages and practical applications overview.
In Action: Image recognition, language processing, etc. use cases.
Ethical Considerations: Addressing biases and ethical concerns in neural networks

6. Natural Language Processing (NLP) & Sentiment Analysis

What is NLP and how is it used?
NLP Language and Semantic Meaning, Bigrams, Trigrams, n-Grams, Root Stemming and Branching
Introduction to Sentiment Analysis: Sentiment indicators, Sentiment Sampling, Predicting Elections based on Sentiment Analysis

7. Using AI for Image, Video, and Audio Processing

Learn about Image processing and Identification, Facial Analysis, Audio Processing
Discuss the role of AI in analyzing streaming video and real-world AV processing

8. AI for Business Technical Tools: Data Science, Deep Learning & The Cloud

Applying AI in Data Science overview
Tools: Python, NumPy, Pandas, SciKitLearn, Hadoop, Spark
NoSQL Databases
Deep Learning overview
AI for Business in the Cloud overview

9. Practical Applications and the Future of AI in Business

What’s next in applied AI for businesses
New AI trends shaping the future of business
Ethical considerations when implementing AI

Available Formats

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