Google AI Professional
Overviewā
The Google AI Professional Certificate validates knowledge of machine learning concepts, Vertex AI, responsible AI, and practical ML workflows on Google Cloud. Completed March 2026.
Core ML Conceptsā
Supervised vs Unsupervised Learningā
| Type | Definition | Examples |
|---|---|---|
| Supervised | Train on labelled data (input + correct output) | Classification, Regression |
| Unsupervised | Find patterns in unlabelled data | Clustering, Dimensionality Reduction |
| Reinforcement | Agent learns by reward/penalty from environment actions | Game AI, Robotics |
The ML Workflowā
- Define the problem ā classification, regression, generation?
- Collect and prepare data ā clean, label, split (train/val/test).
- Choose and train model ā select architecture, tune hyperparameters.
- Evaluate ā accuracy, precision, recall, F1, AUC-ROC.
- Deploy ā serve predictions via API.
- Monitor ā track data drift, model degradation.
Key Metricsā
| Metric | Formula | Use When |
|---|---|---|
| Accuracy | TP+TN / Total | Balanced classes |
| Precision | TP / (TP+FP) | Cost of false positives is high |
| Recall | TP / (TP+FN) | Cost of false negatives is high |
| F1 | 2 Ć PĆR / (P+R) | Balance precision and recall |
| AUC-ROC | Area under ROC curve | Binary classification ranking |
Neural Networks & Deep Learningā
Architecture Basicsā
- Input layer ā Hidden layers ā Output layer
- Each neuron: weighted sum of inputs + bias ā activation function.
- Activation functions: ReLU (hidden layers), Sigmoid/Softmax (output).
- Backpropagation: chain rule to compute gradients, update weights via gradient descent.
Common Architecturesā
| Architecture | Best For |
|---|---|
| Dense / MLP | Tabular data |
| CNN | Images ā convolutional layers detect local features |
| RNN / LSTM | Sequential data ā time series, text |
| Transformer | NLP, vision ā attention mechanism, parallelisable |
Overfitting & Regularisationā
- Overfitting ā model memorises training data, fails on unseen data.
- Solutions: Dropout, L1/L2 regularisation, data augmentation, early stopping, more training data.
Generative AI & LLMsā
Large Language Modelsā
LLMs (GPT, Gemini, LLaMA) are transformer-based models trained on massive text corpora. They predict the next token given a context window.
Key concepts:
- Tokens ā sub-word units (~4 chars average in English).
- Context window ā maximum token length the model processes at once.
- Temperature ā controls randomness (0 = deterministic, >1 = creative).
- Prompt engineering ā crafting inputs to steer model output.
Prompt Engineering Techniquesā
| Technique | Description |
|---|---|
| Zero-shot | Ask directly with no examples |
| Few-shot | Provide 2-5 examples in the prompt |
| Chain-of-thought | Ask the model to "think step by step" |
| RAG | Retrieval-Augmented Generation ā inject relevant docs into context |
| System prompts | Define model persona and constraints |
Embeddingsā
Embeddings are dense vector representations of text. Similar meaning = similar vectors (close in vector space). Used for:
- Semantic search
- Recommendation systems
- Clustering documents
- RAG retrieval step
Google Cloud AI Servicesā
| Service | Purpose |
|---|---|
| Vertex AI | Unified ML platform ā training, deployment, AutoML, MLOps |
| Gemini API | Access Google's Gemini LLMs |
| Vision AI | Pre-trained image classification, OCR, object detection |
| Natural Language API | Sentiment, entity extraction, syntax analysis |
| Speech-to-Text / Text-to-Speech | Audio transcription and synthesis |
| Translation API | 100+ language translation |
| AutoML | Train custom models without writing ML code |
Vertex AI Pipelineā
Data ā Feature Store ā Training Job ā Model Registry ā Endpoint ā Monitoring
Responsible AIā
Google's responsible AI principles:
- Be socially beneficial
- Avoid creating or reinforcing unfair bias
- Be built and tested for safety
- Be accountable to people
- Incorporate privacy design principles
- Uphold high standards of scientific excellence
- Be made available for uses that accord with these principles
Fairness & Biasā
- Data bias ā training data over-represents certain groups.
- Label bias ā human annotators introduce subjective bias.
- Evaluation: Measure model performance across demographic slices.
- Mitigation: Re-sample data, use fairness-aware algorithms, audit regularly.
Explainabilityā
- SHAP ā game-theoretic feature importance.
- LIME ā local linear approximation of model decisions.
- Vertex AI Explainable AI provides feature attributions for predictions.
Best Practicesā
- Split data before any preprocessing to prevent data leakage.
- Always establish a simple baseline before complex models.
- Version datasets and models (not just code).
- Monitor production models for data drift and concept drift.
- Document model cards ā describe purpose, limitations, fairness evaluation.
Common Mistakesā
- Evaluating only on accuracy with imbalanced classes.
- Training on the test set ā invalidates evaluation.
- Deploying without monitoring ā models degrade silently.
- Ignoring feature engineering ā often more impactful than model choice.
Interview Questionsā
Q: What is the bias-variance tradeoff?
Bias = error from wrong assumptions (underfitting). Variance = sensitivity to noise in training data (overfitting). Increasing model complexity reduces bias but increases variance. The goal is to find the sweet spot that minimises total error on unseen data.
Q: Explain how transformers work.
Transformers process all tokens in parallel using a self-attention mechanism that computes how relevant each token is to every other token. This enables capturing long-range dependencies better than RNNs, and allows efficient parallelisation during training.
Q: What is RAG and why is it useful?
Retrieval-Augmented Generation combines an LLM with a retrieval system. At inference time, relevant documents are fetched from a vector store and injected into the LLM's context. This grounds the model in factual, up-to-date information without retraining.
Cheat Sheetā
Supervised: Classification | Regression
Unsupervised: Clustering | Dimensionality Reduction
Metrics: Accuracy | Precision | Recall | F1 | AUC-ROC
Overfitting fixes: Dropout | L1/L2 | Augmentation | Early stopping
LLM concepts: Tokens | Context window | Temperature | Embeddings
Prompt types: Zero-shot | Few-shot | CoT | RAG
GCP AI: Vertex AI | Gemini | Vision AI | AutoML | Natural Language API
Responsible AI: Fairness | Explainability | Privacy | Safety
My Preparation Strategyā
Duration: 8 weeks
Completed: March 2026
I combined the official Google Cloud Skills Boost learning path with hands-on labs in Vertex AI. The most valuable practice was building a RAG pipeline with Vertex AI Vector Search and Gemini ā it made the theoretical concepts concrete.
Hardest topics: Understanding transformer attention mechanisms deeply, Vertex AI MLOps pipeline configuration, and the nuances of responsible AI evaluation frameworks.