Roadmap

Part I, “The Fundamentals of

Machine Learning”, covers the following topics:

• What machine learning is, what problems it tries to solve, and the main categories

and fundamental concepts of its systems

• The 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 challenges of machine learning, in particular underfitting and overfitting

(the bias/variance trade-off)

• The most common learning algorithms: linear and polynomial regression, logistic

regression, k-nearest neighbors, support vector machines, decision trees, random

forests, and ensemble methods

• Reducing the dimensionality of the training data to fight the “curse of

dimensionality”

• Other unsupervised learning techniques, including clustering, density estimation,

and anomaly detection

Part II, “Neural Networks and Deep Learning”, covers the following topics:

• What neural nets are and what they’re good for

• Building and training neural nets using TensorFlow and Keras

• The most important neural net architectures: feedforward neural nets for tabular

data, convolutional nets for computer vision, recurrent nets and long short-term

memory (LSTM) nets for sequence processing, encoder–decoders and transformers

for natural language processing (and more!), autoencoders, generative

adversarial networks (GANs), and diffusion models for generative learning

• Techniques for training deep neural nets

• How to build an agent (e.g., a bot in a game) that can learn good strategies

through trial and error, using reinforcement learning

• Loading and preprocessing large amounts of data efficiently

• Training and deploying TensorFlow models at scale

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