Certified Entry-Level AI Specialist with Python (PCEI)

For IT leaders: Many teams are asked to evaluate or adopt AI tools before anyone on staff understands how models learn, where they fail, or why a 100% accuracy score should raise suspicion. This course gives Python-literate IT practitioners that foundation by having them build selected core algorithms from scratch, including k-nearest neighbors, decision trees, neural networks with backpropagation, and market basket analysis.

Role fit: Best for developers, data-adjacent staff, and analysts who already know basic Python. Learners who are new to Python should start with PCEP. The course relies on plain Python, vectors, matrices, and derivatives, so Team Leads should expect some math, explained step by step.

Time investment: 48 skills totaling about 39 hours, with most skills running around 50 minutes. At two to three skills per week, plan on roughly four to six months per learner.

Risk and governance: A dedicated skill covers ethical risks and safety concerns through real-world scenarios, such as AI-driven resume screening. This helps teams flag bias and responsibility issues before AI reaches production or vendor contracts.

Change management: Most skills include a hands-on Challenge & Solution exercise, so learners practice instead of only watching. Use Playlists to sequence the course alongside PCEP, and use Team Reporting to track progress toward PCEI certification readiness.

IT teams complete this training to move from talking about AI to building and evaluating it in plain Python. Instead of treating models as black boxes, learners write the algorithms themselves. They start with k-nearest neighbor, decision trees, and clustering. Then they build neural networks neuron by neuron and train them with gradient descent and backpropagation. Team Leads get practitioners who can explain why a model behaves the way it does.

The course is built around practical scenarios rather than theory alone:

• Predict risk from tabular data: Learners train neurons on a credit risk dataset and compare results across multiple training/test splits. This builds the habit of validating a model before anyone trusts it.
• Find patterns in transaction data: Using shopping cart data, teams practice identifying relationships in non-numerical data and interpreting whether those patterns are meaningful.
• Build repeatable data pipelines: Learners use vanilla Python, itertools, and stream processing to batch, filter, group, and write data out to CSV files. These skills carry over directly to everyday data prep work.
• Spot misleading results and ethical risk: Learners discover why 100% accuracy on real-world data should raise suspicion. A code-free challenge then walks through real scenarios such as AI-driven resume screening, so teams can flag responsibility and safety concerns before deployment.

The result is a team that can evaluate the capabilities and limitations of AI tools with grounded judgment rather than hype.

Knowledge

• How core machine learning algorithms work under the hood, including k-nearest neighbors, linear regression, decision trees, clustering, Naive Bayes, and market basket analysis (support, confidence, and lift)
• Why data normalization, training/test splits, and performance measurement matter, and why 100% accuracy on real-world data should raise suspicion
• How neurons and neural networks learn through training algorithms such as gradient descent, and how vectors and matrices support that training
• The fundamentals of reinforcement learning, from agents, rewards, and Q-table updates to exploration strategies
• Introductory concepts in natural language processing, generative AI, and large language models, including where they fit and where they fall short
• Common ethical risks and safety concerns in AI, such as those raised by automated resume screening

Ability

• Implement machine learning algorithms from scratch in Python and package them as reusable models
• Clean, transform, and group data using vanilla Python and the random module for sampling and splits
• Build simple and batched data pipelines that process datasets row by row or in batches and write results to files
• Build, train, and test neural networks, then apply them to practical datasets such as credit risk prediction
• Train a reinforcement learning agent to navigate a simple grid world
• Evaluate model results critically and flag ethical risks before AI systems reach production