Transforming businesses through AI

Begin your journey with an introduction to the evolution of Data Science, its foundational concepts, and its impact across industries. This pre-work sets the stage for deeper exploration in subsequent courses.

Understanding AI Fundamentals: Gain a comprehensive understanding of key concepts such as Artificial Intelligence (AI), Data Science (DS), Machine Learning (ML), Deep Learning (DL), and Generative AI (GenAI).
Demystifying AI: Cut through the hype surrounding these technologies to uncover how AI drives value by enhancing judgment, interaction, and automation within businesses.
Applying the R.O.A.D. Framework: Explore and apply the R.O.A.D. Framework for managing AI projects from inception to implementation, ensuring strategic alignment and successful outcomes.

Essentials of Machine Learning: Dive into the core concepts and terminology of Machine Learning, exploring different types of ML and their applications across various business domains.
Performance Metrics: Learn to calculate key ML performance measures such as precision, recall, and F1 score, and understand the trade-offs between these metrics.
Hypothesis Testing: Gain insights into hypothesis testing, including Type I and Type II errors, to equip yourself with the analytical skills needed for evaluating and predicting ML outcomes effectively.
Activity: Calculate the performance measures for various notional AI scenarios

Selecting AI Algorithms: Learn to select the most suitable AI algorithms for different business challenges by comparing their strengths, weaknesses, and trade-offs.
Exploring Key Algorithms: Deep dive into key AI algorithms such as Support Vector Machines, Naïve Bayes, Decision Trees, Random Forests, and Neural Networks.
Optimizing AI Solutions: Gain the knowledge to match the right algorithm to specific business needs and optimize AI-driven solutions for maximum impact.

Data Structures and Types: Get introduced to essential vocabulary related to data structures and types, including nominal, ordinal, and categorical data.
Ensuring Data Quality: Learn to calculate inter-annotator agreement and explore the trade-offs between data size, consistency, and quality.
Data Labeling and Cognitive Limits: Understand labeling techniques, cognitive limits, and terms of reference to assess and manage data quality effectively in AI projects.

Resource Allocation in AI: Focus on identifying and evaluating the trade-offs between resource allocation, system performance, and bias in Machine Learning and AI systems.
Balancing Performance and Fairness: Explore memory and computational trade-offs, query expressiveness, and performance considerations while learning to identify and mitigate sources of machine bias.
Complex Decision-Making: Navigate the complex decisions involved in optimizing AI systems to achieve both performance and fairness goals.
Project: IT Modernization for Prison Management Using AI

Understanding AI Bias: Explore the various sources of bias in Machine Learning and AI systems, including algorithmic, human, and measurement bias, as well as algorithmic drift.
Risk-Based Mitigation: Learn to identify and mitigate these biases using a risk-based approach combined with human oversight.
Legal and Ethical Responsibilities: Cover the fiscal, performance, privacy, and legal responsibilities tied to AI, equipping you to justify and navigate current laws and international regulations governing AI.

Introduction to Generative AI: Delve into the theory and application of Generative AI, covering key technologies such as Convolutional Neural Networks, Transformers, and Large Language Models.
AI Models and Applications: Identify the fundamental differences between stochastic AI models and expert systems, gaining a deeper understanding of various AI approaches and their practical implications.

AI Leadership and Team Dynamics: Focus on estimating and organizing the people, roles, and responsibilities needed for successful AI projects.
Assessing and Building Teams: Explore common roles and skills in AI, identify which roles can or should be automated, and understand the pace of labor transition.
Ensuring Meritocracy and Diversity: Assess AI team performance to ensure meritocracy and build cognitively diverse teams to enhance project outcomes.

Managing Large AI Projects: Learn a comprehensive framework for managing at-scale AI projects, covering each stage from value realization to continuous improvement.
Custom Solutions and Pricing: Focus on critical management concerns, including data foundations, governance, advanced insights, automation, and interaction. Equip yourself with skills to effectively plan and execute large-scale AI initiatives.

Optimizing AI Delivery: Explore management solutions to optimize the delivery of at-scale AI projects and mitigate associated risks.
Risk Management: Identify potential risk sources and learn strategies for enterprise and system optimization, supported by delivery excellence teams.
Best Practices: Demonstrate sound management practices to ensure effective risk management and project success.
Final Project: Proposal Development for a Large-Scale AI Project