1. Find the right study materials for Machine Learning
You don’t need to spend a lot to learn AI and ML right now. Two of my favorites:
Python for Data Science and Machine Learning Bootcamp by Jose Portilla on Udemy.com
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition by Aurélien Géron, published by O’Reilly Media
2. Don’t just shift/enter with Jupyter Notebooks
Don’t just copy/paste and shift/enter when going through tutorials. Retype examples from Jupyter Notebook etc. into your own notebooks / IDE to aid retention and experiment.
My IDE of choice right now for Python is PyCharm. Get to know PyCharm if you are planning to take the TensorFlow Developer Certificate exam, since it is required to complete all of the assessments. The more familiar with it you are, the more comfortable you will be during the test.
3. You don’t need a GPU starting out
Google Colab and Kaggle both provide access to GPU acceleration. Check out Colab Pro too; it provides access to faster GPUs and more memory. Pro is currently only available in the U.S. and Canada.
Photo by Timothy Dykes on Unsplash