Introduction to AI
Lead Your Technical Teams with AI Confidence. This is an introductory course in AI specifically for Engineering Leads, designed to help you understand and guide AI work within your teams. It covers machine learning fundamentals, popular algorithms, and key frameworks like Scikit-learn and Tensorflow. The course empowers you to lead technical projects effectively through hands-on activities and an emphasis on project discussion.
Who is this for?
Engineering Leads and managers who need to understand AI fundamentals to effectively guide their technical teams.
Prerequisites:
No formal technical prerequisites are listed, as the course focuses on high-level understanding and strategic guidance.
What You Will Achieve
- Understand machine learning fundamentals (classification, regression, feature engineering) to confidently guide AI projects.
- Learn the principles behind key algorithms like K-nearest neighbors, linear regression, and decision trees.
- Gain familiarity with industry-standard tools like Scikit-learn and Tensorflow.
- Understand deep learning concepts like neural networks (MLP), CNNs, and transfer learning (MobileNetV2).
- Learn to identify and discuss practical production issues like data skew and the need for AI monitoring.
Key Topics Covered
This 8-session, 1.5-hour-per-session curriculum is structured around in-demand business use cases:
- ML Fundamentals: Introduction to Machine Learning, Classification, Regression, Feature Engineering, and how to measure an AI.
- Core Algorithms: K-nearest neighbors (KNN), Linear Regression, and Decision Trees (all with hands-on activities).
- Framework Introduction: Code overview of algorithms in Scikit-learn; Introduction to Tensorflow (MLP with MNIST, CNN with MobileNetV2).
- Deep Learning Intro: Neural networks, CNNs, ImageNet Challenge, and Transfer Learning with images.
- Production Concepts: Understanding Train/Test/Validation, how to “Balance skew,” and AI Monitoring.
Assessment & Certification
Assessment is based on hands-on activities (e.g., “Build an AI with churn data,” “Build an AI using MNIST”) and a final Project Presentation and discussion, emphasizing the communication and leadership aspects of AI.