Fundamentals of Machine Learning and AI

  • What is Machine Learning?
  • What problems does it try to solve?
  • What are the main categories and fundamental concepts of Machine Learning systems?
  • The main steps in a typical Machine Learning project.
  • Learning by fitting a model to data.
  • Optimizing a cost function.
  • Handling, cleaning, and preparing data.
  • Selecting and engineering features.
  • Selecting a model and tuning hyperparameters using cross-validation.
  • The main challenges of Machine Learning, in particular underfitting and overfitting (the bias/variance tradeoff).
  • Reducing the dimensionality of the training data to fight the curse of dimensionality.
  • The most common learning algorithms:
  • Linear and Polynomial Regression, Logistic Regression, k-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests, and Ensemble methods.

Neural Networks and Deep Learning

  • What are neural nets? What are they good for?
  • Building and training neural nets using TensorFlow
  • The most important neural net architectures:
  • feedforward neural nets
  • convolutional nets
  • recurrent nets
  • long short-term memory (LSTM) nets
  • autoencoders
  • Techniques for training deep neural nets
  • Scaling neural networks for huge datasets
  • Reinforcement learning
  • High School Math: Brush up on probability and linear algebra with Khan Academy
  • Use the Cloud Genius Workstation, roll your own Jupyter notebook using Docker, or use a cloud service as your playground
  • Review the prerequisite Jupyter notebooks in the getting started segment

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