1. Introduction to AI & Machine Learning
o Understand what AI and Machine Learning are and why they're critical for modern business
o Exploring definitions and types of AI
o Discussing AI in the Modern Age and its role in business
o Embrace Change: Learn and Build Confidence using the Tools - Don't be Replaced By Them
2. Deeper Dive into Machine Learning
o Basics of how mathematics are used in or apply to AI
o Algorithms: What are they and how are they used in AI and ML
o Supervised vs Unsupervised
o Classification, Regression, Clustering, Dimensionality Reduction, and Ensemble Methods
o The role of Machine Learning in AI and business decision-making
o Review a real business scenario where Machine Learning was used to increase efficiency.
3. Leveraging AI in Business & Decision Making
o Discussing key business areas where AI adds value: Operations, Marketing, Sales, HR, content development, coding and software development
o Explore how AI is used in business decision-making
o Introduction to predictive analytics
o Using AI for strategic decision-making
4. Hot Trends for AI in Business: Large Language Models (LLM), Generative AI and GPT
o Understand the basics of Generative AI and how it differs from other AI techniques
o Introduction to GPT and its applications in various sectors
o Explore how GPT uses machine learning to generate human-like text based on the input it receives.
o Understand the concept of language models and how they are trained using large amounts of text data
5. Basics of Neural Networks
o What are they and how are they used?
o Basic parts: Neurons, activation functions, interactions.
o Types: Feedforward, recurrent, convolutional neural networks overview.
o How they learn: Forward propagation, backpropagation explained.
o Training Neural Networks: Importance of data preprocessing in training.
o Deep Neural Networks: Advantages and practical applications overview.
o In Action: Image recognition, language processing, etc. use cases.
o Ethical Considerations: Addressing biases and ethical concerns in neural networks.
6. Natural Language Processing (NLP) & Sentiment Analysis
o What is NLP and how is it used?
o NLP Language and Semantic Meaning, Bigrams, Trigrams, n-Grams, Root Stemming and Branching
o Introduction to Sentiment Analysis: Sentiment indicators, Sentiment Sampling, Predicting Elections based on Sentiment Analysis
7. Using AI for Image, Video, and Audio Processing
o Learn about Image processing and Identification, Facial Analysis, Audio Processing
o 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
o Applying AI in Data Science overview
o Tools: Python, NumPy, Pandas, SciKitLearn, Hadoop, Spark
o NoSQL Databases
o Deep Learning overview
o AI for Business in the Cloud overview
9. Practical Applications and the Future of AI in Business
o What's next in applied AI for businesses
o New AI trends shaping the future of business
o Ethical considerations when implementing AI
10. Next-Steps
o Hands-on Practice
o Resources
o AI & ML Communities