Look what I got! I hope return to fill in more details of how I prepared for the exam and some helpful resources. Overall, this was an excellent learning experience that helped me upskill for Artificial Intelligence, Machine Learning, and Deep Learning in a focused manner.
For starters, review the candidate handbook very carefully. The DeepLearning.AI on Coursera lays a strong foundation but you’ll need additional hands on practice as well. Be sure you’re comfortable with these topics:
1. Introduction to TensorFlow
- Learn best practices for using TensorFlow, a popular open-source machine learning framework
- Build a basic neural network in TensorFlow
- Train a neural network for a computer vision application
- Understand how to use convolutions to improve your neural network
2. Convolutional Neural Networks in TensorFlow
- Handle real-world image data
- Plot loss and accuracy
- Explore strategies to prevent overfitting, including augmentation and dropout
- Learn transfer learning and how learned features can be extracted from models
3. Natural Language Processing in TensorFlow
- Build natural language processing systems using TensorFlow
- Process text, including tokenization and representing sentences as vectors
- Apply RNNs, GRUs, and LSTMs in TensorFlow
- Train LSTMs on existing text to create original poetry and more
4. Sequences, Time Series and Prediction
- Solve time series and forecasting problems in TensorFlow
- Prepare data for time series learning using best practices
- Explore how RNNs and ConvNets can be used for predictions
- Build a sunspot prediction model using real-world data
Miscellaneous Tips
- Studied additional topics…
- Used both local GPU + Google colabs
- Kept handy code snippets for TensorBoard, early stopping, checkpoints, etc
Resources
- Review Candidate Handbook
- DeepLearning.AI on Coursera
- Hands-On ML with Scikit-Learn, Keras, and TensorFlow