AI Product Leader (Nanodegree) Certificate (AIPLC)^TM

Python is essential for machine learning due to its simplicity and readability, making it accessible for beginners. It supports numerous ML libraries and frameworks like TensorFlow and Scikit-learn, which streamline the development process. Python's strong community and extensive resources facilitate problem-solving and innovation in ML, making it a preferred choice for professionals and researchers in the field.

Outcome:

You will be able to code control flow, lists, loops, functions, strings, dictionaries, classes, and simple algorithms, as well as work with modules and files.  To get there, you will work alongside our instructor through the Python 3 Code Academy course, plus several labs of ours, where you will learn how to troubleshoot some common errors.

Key points:

• 9 hours a week for 2 weeks. However, if students already have some working knowledge of Python we will work with them on more challenging exercises.
• Students may skip this module and begin at Module 1, but you must pass the Python knowledge test.

Statistics underpins machine learning by providing methods to understand and interpret data, which is crucial for designing and training algorithms. It aids in evaluating model performance and understanding results' reliability through techniques like hypothesis testing and A/B testing. By incorporating statistical methods, machine learning practitioners can conduct A/B testing to compare models and make data-driven decisions, ensuring the development of effective and accurate predictive models.

Outcome:

You will be able to create A/B tests and analyze the results to determine if the delta is statistically significant or not. You can articulate the definitions and applications for sample size, power, alpha, 1-sided versus 2-sided tests, and P-values.  You will also be able to implement one-arm bandit and multi-arm bandit tests and be able to calculate NPV, MIRR, and IRR quickly. This module is where we consider the starting point for the AIPLC^TM certificate preparation modules.

Key points:

• 9 hours a week for 1 week, including labs and a communications assignment
• Students can opt to challenge this and skip to the next module depending on the strength of their math skills

In machine learning, data Ingestion (ETL/ELT), cleaning, and visualization are crucial for ensuring data quality and usability. ETL/ELT processes efficiently gather and prepare data from diverse sources. Data cleaning removes inaccuracies and inconsistencies, enhancing model accuracy. Visualization aids in understanding data patterns and anomalies, which is essential for insightful feature engineering and algorithm selection. These steps are fundamental for building robust models and achieving reliable, interpretable machine learning outcomes.

Outcome:

You can create an end-to-end data pipeline in the cloud and can create sophisticated data visualizations with the help of prompt engineering. You are efficient at taking a data set and evaluating it, and cleaning it so it is ready to be used for training. 

Key points:

• 6 hours a week for 2 week, including labs and a communications assignment
• Students can opt to challenge this and skip to the next module depending on the strength of their math skills

Machine learning is important as it enables systems to learn and improve from experience without being explicitly programmed. It's crucial for analyzing large datasets, identifying patterns, and making decisions with minimal or no human intervention. This technology is applied across various fields, such as finance, healthcare, and marketing, improving efficiency, personalization, and predictive capabilities. By automating complex processes, machine learning drives innovation, enhances user experiences, and solves problems that are too intricate for manual analysis. Machine learning is a subset of AI and mostly encompasses everything you have heard of in the new and otherwise.  

Outcome:

You can proficiently run ML algorithms in Jupiter notebooks locally and in a cloud provider for supervised, unsupervised and reinforcement learning. You can substitute in/out sections of the algorithms for better performance and can efficiently augment data and tune hyperparameters. You understand complex algorithms, model evaluation, overfitting, and bias-variance tradeoffs and know which models should be applied to different scenarios. Additionally, you have an in-depth understanding of XGBoost and can utilize top libraries for Python, including Scikit-learn, TensorFlow, Pandas, NumPy, and up-and-coming libraries such as fastai.

Key points:

• 6 hours a week for 2 weeks, including labs and a communications assignment

Deep learning is a subset of machine learning where artificial neural networks, inspired by the human brain, learn from large amounts of data. It's particularly effective for complex tasks like image and speech recognition, natural language processing, and pattern detection, automatically identifying and extracting features without explicit programming.

Outcome:

You understand self-driving car algorithms (proximal policy optimization & Soft Actor Critic), where you will race your own virtual self-driving car against others in your cohort using the AWS DeepRacer platform using reinforcement learning. You can also explain neural networks, architectures (UNet, ResNet, Inception models and others), and frameworks well enough to be confident to present on this material. You have become proficient at running these algorithms both locally and in the cloud. You also have a solid understanding of Transformers and how muti-head attention works. 

Key points:

• 6 hours a week for 3 weeks, including labs and a communications assignment

Large Language Models (LLMs) are pivotal in the field of AI for their ability to process and generate human-like text. They excel in understanding context, nuance, and complexities of natural language, making them invaluable for tasks like translation (one spoken language to another, or a spoken language to code such as Python), content creation, customer service, personal assistants, market research, educational tools, healthcare, legal services, finance, and e-commerce. Additionally, they're becoming invaluable tools for developers, enhancing productivity and creativity in software development. LLMs can generate code snippets, translate code between languages, and even develop entire applications, transforming how programming is approached and executed in the tech industry.

Outcome:

You will deeply understand the available LLMs, including GPT-5, Bert Bloom, PaLM, and LLaMa. You will understand how to select and train one so you don't break the bank. You will also be able to recite the paper "Attention is all you need" and eloquently describe the types of transformers. You can improve your LLM responses with RAG, or fine-tuning using Parameter Efficient Fine Tuning (PEFT) with LoRA, adapters, and soft prompts. Using the latest algorithms and architectures, you can architect and deploy reinforcement learning from human feedback (RLHF) and minimize the human component.

Key points:

• 6 hours a week for 3 weeks, including labs and a communications assignment

Generative AI agents are valuable for their ability to create new content like text, images, and code from scratch or by remixing training data in novel ways. This enables applications in automated content creation, natural language interaction, data augmentation, and further customizations for specific tasks. Additionally, it is possible to create AI Agents that outperform any individual LLM by creatively utilizing two or more LLMs together. 

Outcome:

You can stitch together any component LangSmith, LangChain, or Google's Vertex AI offer and create sophisticated architecture, including, but not limited to, using an LLM to generate summaries and themes from multiple text sources in PDF or machine-ingestible formats. You can create modern LLM-powered Agents for planning (task decomposition, self-reflection), memory (sensory, short-term memory, long-term memory), Maximum Inner Product Search (MIPS), and Tools (MRKL, TALM & Toolformer (finetuning for APIs), HuggingGPT)

Key points:

• 6 hours a week for 4 weeks, including labs and a communications assignment

 There are two components to this capstone:

1. a group project where you will complete an AI/ML product end-to-end, from obtaining data and creating a database to leveraging data for an LLM and creating a front-end with Streamlit. To better understand what it is like working with a data science team, your 2 PM team will also get access to data science, data engineering, MLOps, full-stack dev, and a UX designer for a limited time.¹ You can think of it like a hackathon where you participate in both the PM and the technical elements.
2. Demo day. Here, you will showcase your projects to instructors, guest speakers, and external invitees, including VCs and senior product leaders. 

For some folks reading this right now, we know that it may seem unrealistic or daunting, but you will be armed with the tools through this program even to surprise yourself. We aren't just calling this curriculum the gold standard for funzies ;)

¹One team may have three people if there is an odd number enrolled.

Outcome:

You can collaborate effectively with a team on an AI/ML product as you will be partnered with a data scientist, ML Engineer, data engineer, and full stack developer. Additionally, you can complete an AI/ML product end-to-end from obtaining data and creating a database to leveraging data for an LLM and creating a front-end with Streamlit all by yourselves with the help of an LLM, LangChain, and advice of other teammates. 

Key points:

• 9 hours a week for 3 weeks
• Evaluation criteria: Class participation, individual project submission, and group project submission.

We will cover leading cloud-based AI platforms, including Azure, AWS, GCP, NVidia, and integrations into services like Databricks and Snowflake. 

Outcome:

You can articulate the differences and similarities between various cloud-based AI platforms and make recommendations to companies beginning their AI journey. You can also produce cost estimates for running production workloads for use in NPV analysis. You can also set up near production-grade infrastructure. 

Key points:

• 6 hours a week for 1 week, including labs and a communications assignment

Considering ethics, legal and security aspects in AI is crucial due to AI's far-reaching impact on privacy, data protection, intellectual property, and liability. AI can inadvertently perpetuate biases, posing discrimination concerns. Understanding these challenges is key for professionals to safeguard rights, ensure regulatory compliance, and guide responsible AI development and use, maintaining public trust and upholding legal and ethical standards in the rapidly evolving digital landscape.

Outcome:

You will not only understand the latest ISO AI Management standard 42001 and the policies of the leading companies in the field, but you will also be able to craft an AI policy for your own company if you do not already have one. You know how to overcome key legal hurdles, including privacy and data protection, intellectual property rights, bias and discrimination, liability for AI errors, transparency and explainability, cybersecurity and data breaches, misuse of AI technology, employee rights and automation, international regulation compliance, and AI in contractual agreements. 

Key points:

• 6 hours a week for 1 week, including a communications assignment

"Product Sense and Execution" equips you with essential skills at the intersection of AI/ML and product management. Through mock product sense questions, you'll develop the ability to swiftly identify high-value AI/ML opportunities within organizations. The course emphasizes entrepreneurial thinking—a critical driver of innovation where product managers must validate ideas through customer pain points, technical feasibility, and profit potential. Success in this field requires both strategic vision and practical execution, balancing agile methodology with structured timelines and fostering team alignment. You'll learn to create realistic milestones while building the collaborative commitment necessary for effective delivery.

Outcome:

You understand the AI economy and can present a business case identifying market opportunities where AI can differentiate your product and provide a decent ROI.  Additionally, you will acquire practical risk assessment and decision-making skills, empowering you to navigate ambiguity and deliver impactful results. Lastly, you will have a grasp of what is bleeding-edge in the AI space and how to roll out a feasible plan of attack to a POC and onto production. 

Key points:

• 6 hours a week for 1 week, including labs and a communications assignment

We cover the entire AI/ML product lifecycle and give you the opportunity to combine everything you have learned with hands-on labs. We will also provide a methodology for building and scaling a trustworthy AI system for production.

Outcome:

You can identify a problem where a solution includes AI and can scope the project and the value. You can use Figma to produce React or Streamlit code for the front end. You can select the correct model and train or use PEFT (in the case of LLMs) and can adjust and evaluate the model and setup near production architecture. You can also deploy the model and identify and fix model drift.  Through several case studies, you will also know what precautions are needed to ensure a trustworthy AI system is ready for production. Lastly, you comprehensively understand how to integrate AI into existing versus new products learned by doing it yourself hands-on. 

Key points:

• 6 hours a week for 1 week, including labs and a communications assignment

Effective C-suite communication demonstrates your ability to align product strategies with broader business goals, showcases leadership skills, and builds executive confidence in your decisions. Excelling in this area increases visibility, fosters strategic influence, and positions you as a key player in driving the organization's success, which is essential for advancing to higher leadership roles.

Outcome:

You can effectively communicate and collaborate with C-suite executives, aligning product strategies with overarching business objectives. You can present complex product information succinctly and persuasively to top management. You'll understand how to build and maintain executive confidence in your decisions, increase your professional visibility, and strategically influence organizational direction. Ultimately, you can position yourself as a vital contributor to your company's success and pave the way for career advancement into leadership roles.

Key points:

• 6 hours a week for 1 week, including labs and a communications assignment