Lilly Tech Systems
TensorFlow Developer Certificate
A complete exam prep course with hands-on coding practice for the TensorFlow Developer Certificate. This certification requires building and training models in a PyCharm-based Jupyter environment within 5 hours. We cover every exam category — regression, CNNs, NLP, and time series — with practice models that mirror the actual exam format.
Your Learning Path
Follow these lessons in order for complete TensorFlow Developer Certificate preparation, or jump to any exam category.
1. Exam Overview & Strategy
Exam format (5 hours, PyCharm, model submission), all 4 categories, recommended study plan, registration process, and cost ($100).
2. Regression & Classification
Dense networks for regression and classification, binary and multi-class problems, data normalization, and practice models with complete code.
3. CNNs
Image classification with convolutional networks, transfer learning with pre-trained models, data augmentation strategies, and practice models.
4. NLP & Sequences
Text classification, LSTM networks, word embeddings, tokenization, padding sequences, and practice models for sentiment analysis.
5. Time Series
Time series forecasting, windowed datasets, RNN and LSTM for sequences, moving averages, and practice models for prediction.
6. Full Practice Session
5 complete model-building exercises that mimic the actual exam format. Build, train, and evaluate models under exam-like conditions.
7. Exam Day Tips
Environment setup, time management strategies, common mistakes to avoid, and a comprehensive FAQ accordion for last-minute review.
What You'll Learn
By the end of this course, you will be able to:
Build TensorFlow Models
Create dense, convolutional, and recurrent neural networks using tf.keras Sequential and Functional APIs for any exam category.
Train & Evaluate Models
Compile models with appropriate loss functions and optimizers, use callbacks for early stopping, and evaluate with correct metrics.
Handle Real Datasets
Load, preprocess, and augment data for images, text, and time series using tf.data pipelines and built-in TensorFlow datasets.
Pass the Exam
Manage your time across 5 hours, avoid common pitfalls, submit models in the correct format, and score above the passing threshold.