Become a Machine Learning expert. Put your machine learning knowledge to work, and expand your production engineering capabilities and begin to turn your ideas into realities.
- Prerequisites: Python 3.9, gsutil
- `pip install -r requirements.txt
- Ansible playbook for setting one common env + prereqs.
- Ansible for setting up paperspace nvidia
- Optional prereq: graphviz?
- Work with outdated apis for now. Some stuff like
layers.DenseFeatures
are deprecated. - remove apt-get, use zipfile inbuilt pymodule for unzip.
- Either use py3.12 or
conda install -c conda-forge typing-extensions=4.12 -y --force-reinstall
- Remove all requirements.txt within each week.
Each course is spread out in weeks, and are made up of video slides, lab sessions, quizzes, assignments, related course materials, code and data
- Ungraded Lab: Simple Feature Engineering
- Ungraded Lab: Feature Engineering Pipeline
- Ungraded Lab: Feature Selection
- Ungraded Lab: Feature Engineering with Weather Data
- Ungraded Lab: Feature Engineering with Accelerometer Data
- Ungraded Lab: Feature Engineering with Images
- Ungraded lab: Manual Feature Engineering
- Ungraded lab: Algorithmic Dimensionality Reduction
- Ungraded Lab: Quantization and Pruning
- Ungraded lab: Distributed Strategies with TF and Keras
- Ungraded Lab: Knowledge Distillation
- Ungraded lab: Distributed Strategies with TF and Keras
- Ungraded Lab: TensorFlow Model Analysis
- Ungraded Lab: Model Analysis with TFX Evaluator
- Ungraded Lab: Fairness Indicators
The solutions presented are intended to serve as reference for other learners who enroll in this course. Solutions are adapted from John Moses' repo.