> ## Documentation Index
> Fetch the complete documentation index at: https://aegean.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# ML Weekly Study Guide

> Week-by-week study guide for the Introduction to Machine Learning course.

<AccordionGroup>
  <Accordion title="Unit 1, Introduction to ML">
    <Steps>
      <Step title="Watch videos">
        <Icon icon="video" /> [Khan Academy Probability library](https://www.khanacademy.org/math/statistics-probability/probability-library), the full probability video series. Work through these core topics:

        <Icon icon="video" /> [Theoretical probability](https://www.khanacademy.org/math/statistics-probability/probability-library/basic-theoretical-probability/v/basic-probability), probability from equally likely outcomes.

        <Icon icon="video" /> [Experimental probability](https://www.khanacademy.org/math/statistics-probability/probability-library/experimental-probability-lib/v/experimental-probability), estimating probability from data.

        <Icon icon="video" /> [Probability with sample spaces](https://www.khanacademy.org/math/statistics-probability/probability-library/probability-sample-spaces/v/events-and-outcomes-3), counting outcomes in a sample space.

        <Icon icon="video" /> [Addition rule](https://www.khanacademy.org/math/statistics-probability/probability-library/addition-rule-lib/v/addition-rule-for-probability), the probability of A or B.

        <Icon icon="video" /> Multiplication rule for [independent events](https://www.khanacademy.org/math/statistics-probability/probability-library/multiplication-rule-independent/v/compound-sample-spaces) and [dependent events](https://www.khanacademy.org/math/statistics-probability/probability-library/multiplication-rule-dependent/v/introduction-to-dependent-probability), the probability of A and B.

        <Icon icon="video" /> [Conditional probability and independence](https://www.khanacademy.org/math/statistics-probability/probability-library/conditional-probability-independence/v/calculating-conditional-probability), conditioning and independence, the basis for Bayes' rule.

        <Icon icon="video" /> [An experiment on Sum and Product Rules](https://www.youtube.com/watch?v=jkNugBJG2yk), a hands-on experiment illustrating the sum and product rules of probability.

        <Icon icon="video" /> [Random variables](https://www.khanacademy.org/math/statistics-probability/random-variables-stats-library/random-variables-discrete/v/random-variables), discrete random variables.

        <Icon icon="video" /> [Probability density functions](https://www.khanacademy.org/math/statistics-probability/random-variables-stats-library/random-variables-continuous/v/probability-density-functions), continuous random variables.
      </Step>

      <Step title="Review probability theory">
        Refresh the probability foundations used throughout the course. See [Probability](/aiml-common/lectures/ml-math/probability/index) and Stanford's [Probability for Computer Scientists](https://probabilitycoders.stanford.edu/spr26).
      </Step>

      <Step title="Review lecture: Introduction to Machine Learning">
        Course roadmap and how the pieces of a machine learning system fit together. See GÉRON [Chapter 1: The Machine Learning Landscape](https://learning.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch01.html).
      </Step>

      <Step title="Read GERON Chapter 2">
        The end-to-end machine learning project workflow. See [Chapter 2: End-to-End Machine Learning Project](https://learning.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch02.html).
      </Step>

      <Step title="Set up your development environment">
        Follow the [Dev Environment](/aiml-common/resources/environment/index) guide to install Docker and configure your container.
      </Step>

      <Step title="Import the course repository">
        [Import](https://docs.github.com/en/migrations/importing-source-code/using-github-importer/importing-a-repository-with-github-importer) [eng-ai-agents](https://github.com/pantelis/eng-ai-agents) to your GitHub account and clone it locally.
      </Step>

      <Step title="Clone the Hands-On ML repository">
        Clone the companion repository for [Hands-On Machine Learning with Scikit-Learn and PyTorch](https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/) by Aurélien Géron: [github.com/ageron/handson-mlp](https://github.com/ageron/handson-mlp). This repository contains all the notebook exercises referenced throughout the course.
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Unit 2, Statistical Learning, Regression and Optimization">
    <Steps>
      <Step title="Watch videos">
        <Icon icon="video" /> [Statistical learning theory](/media/foundations/statistical-learning-theory) videos in the media library.
      </Step>

      <Step title="Review lecture: Supervised Learning">
        The supervised learning problem: learning a mapping from labeled examples. See [The Learning Problem](/aiml-common/lectures/learning-problem/index).
      </Step>

      <Step title="Review lecture: Linear Regression">
        Regression fundamentals and empirical risk minimization. See [Linear Regression](/aiml-common/lectures/regression/linear-regression/index).
      </Step>

      <Step title="Review lecture: SGD Optimization">
        Stochastic gradient descent for minimizing the empirical risk. See [SGD](/aiml-common/lectures/optimization/sgd/index).
      </Step>

      <Step title="Read GERON Chapter 4, SGD sections">
        Read the Gradient Descent, Batch Gradient Descent, Stochastic Gradient Descent, and Mini-Batch Gradient Descent sections from [Chapter 4: Training Linear Models](https://learning.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch04.html).
      </Step>

      <Step title="Run the GERON Chapter 4 notebook">
        Work through the [Training Linear Models](https://github.com/ageron/handson-mlp/blob/main/04_training_linear_models.ipynb) notebook.
      </Step>

      <Step title="Run the SGD notebook">
        Execute the [SGD Sinusoidal Dataset](/aiml-common/lectures/regression/linear-regression/sgd/sgd_sinusoidal_dataset) notebook in your container.
      </Step>

      <Step title="Assignment 1">
        Assignment 1 is released this week. Start [Assignment 1](/courses/ml/assignments/assignment-1).
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Unit 3, Maximum Likelihood and Classification">
    <Steps>
      <Step title="Watch videos">
        <Icon icon="video" /> [Statistical learning theory](/media/foundations/statistical-learning-theory) videos in the media library.
      </Step>

      <Step title="Review lecture: Entropy">
        Information theory principles and cross-entropy. See [Entropy](/aiml-common/lectures/entropy/index).
      </Step>

      <Step title="Review lecture: Marginal Maximum Likelihood">
        Marginal likelihood and parameter estimation. See [Marginal Maximum Likelihood](/aiml-common/lectures/optimization/maximum-likelihood/marginal_maximum_likelihood).
      </Step>

      <Step title="Review lecture: Conditional Maximum Likelihood">
        Conditional likelihood for supervised learning. See [Conditional Maximum Likelihood](/aiml-common/lectures/optimization/maximum-likelihood/conditional_maximum_likelihood).
      </Step>

      <Step title="Review lecture: Classification Introduction">
        Classification fundamentals and decision boundaries. See [Classification Introduction](/aiml-common/lectures/classification/classification-intro/index).
      </Step>

      <Step title="Review lecture: Logistic Regression">
        Binary classification with logistic regression. See [Logistic Regression](/aiml-common/lectures/classification/logistic-regression/index).
      </Step>

      <Step title="Assignment 2">
        Assignment 2 is released this week. Start [Assignment 2](/courses/ml/assignments/assignment-2).
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Unit 4, Deep Neural Networks">
    <Steps>
      <Step title="Watch videos">
        <Icon icon="video" /> [Dense neural networks](/media/neural-networks/dense-neural-networks) videos in the media library.
      </Step>

      <Step title="Read GERON Chapter 9 and DL Chapter 6">
        From Perceptron to MLPs, backpropagation fundamentals. See [Chapter 9: Artificial Neural Networks](https://learning.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch09.html).
      </Step>

      <Step title="Review lecture: DNN Introduction">
        Neural network architectures and forward pass. See [DNN Introduction](/aiml-common/lectures/dnn/dnn-intro/index).
      </Step>

      <Step title="Review lecture: Backpropagation">
        Gradient computation and the chain rule. See [Backpropagation](/aiml-common/lectures/dnn/backprop-intro/index).
      </Step>

      <Step title="Run the GERON Chapter 9 notebook">
        Work through the [Artificial Neural Networks](https://github.com/ageron/handson-mlp/blob/main/09_artificial_neural_networks.ipynb) notebook.
      </Step>

      <Step title="Read GERON Chapter 10, Classification MLPs">
        Read the Classification MLPs section from [Chapter 10: Neural Nets with PyTorch](https://learning.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch10.html#id406).
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Unit 5, Convolutional Neural Networks">
    <Steps>
      <Step title="Watch videos">
        <Icon icon="video" /> [Convolutional neural networks](/media/neural-networks/convolutional-neural-networks) videos in the media library.
      </Step>

      <Step title="Read DL Chapters 9 & 10, GERON Chapter 12">
        Convolutional Neural Network architecture and applications. See [Chapter 12: Deep Computer Vision with CNNs](https://learning.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch12.html).
      </Step>

      <Step title="Review lecture: CNN Introduction">
        Convolution operations, pooling, and spatial feature hierarchies. See [CNN Introduction](/aiml-common/lectures/cnn/cnn-intro/index).
      </Step>

      <Step title="Review lecture: CNN Layers">
        Layer types and architectural patterns. See [CNN Layers](/aiml-common/lectures/cnn/cnn-layers/index).
      </Step>

      <Step title="Review lecture: CNN Architectures and ResNets">
        ResNet, VGG, and other architectures. See [CNN Example Architectures](/aiml-common/lectures/cnn/cnn-example-architectures/index) and [Feature Extraction with ResNet](/aiml-common/lectures/scene-understanding/feature-extraction-resnet/index).
      </Step>

      <Step title="Read GERON Chapter 12, CNN sections">
        Read the Convolutional Layers, Pooling Layers, and CNN Architectures sections from [Chapter 12: Deep Computer Vision with CNNs](https://learning.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch12.html).
      </Step>

      <Step title="Run the GERON Chapter 12 notebook">
        Work through the [Deep Computer Vision with CNNs](https://github.com/ageron/handson-mlp/blob/main/12_deep_computer_vision_with_cnns.ipynb) notebook.
      </Step>

      <Step title="Assignment 3">
        Assignment 3 is released this week. Start [Assignment 3](/courses/ml/assignments/assignment-3).
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Unit 6, NLP Fundamentals">
    <Steps>
      <Step title="Watch videos">
        <Icon icon="video" /> [Natural language processing](/media/nlp/natural-language-processing) videos in the media library.
      </Step>

      <Step title="Read GERON Chapter 14">
        Natural language processing problem formulation and component mechanics. See [Chapter 14: Natural Language Processing with RNNs and Attention](https://learning.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch14.html).
      </Step>

      <Step title="Review lecture: NLP Pipelines">
        Tokenization, embeddings, and NLP pipeline components. See [NLP Pipelines](/aiml-common/lectures/nlp/nlp-introduction/nlp-pipelines/index).
      </Step>

      <Step title="Review lecture: Word2Vec">
        Word embeddings and distributional semantics. See [Word2Vec](/aiml-common/lectures/nlp/nlp-introduction/word2vec/index).
      </Step>

      <Step title="Assignment 4">
        Assignment 4 is released this week. Start [Assignment 4](/courses/ml/assignments/assignment-4).
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Unit 7, Language Modeling">
    <Steps>
      <Step title="Watch videos">
        <Icon icon="video" /> [Recurrent neural networks](/media/neural-networks/recurrent-neural-networks) and [Transformers](/media/nlp/transformers) videos in the media library.
      </Step>

      <Step title="Read GERON Chapters 14 & 15, DL Chapter 10">
        RNN/LSTM architectures, attention mechanisms, Transformer framework. See [Chapter 14: Natural Language Processing with RNNs and Attention](https://learning.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch14.html) and [Chapter 15: Transformers for NLP and Chatbots](https://learning.oreilly.com/library/view/hands-on-machine-learning/9798341607972/ch15.html).
      </Step>

      <Step title="Review lecture: RNNs and LSTMs">
        Recurrent architectures for sequence modeling. See [RNN Introduction](/aiml-common/lectures/rnn/introduction/index).
      </Step>

      <Step title="Review lecture: Transformers">
        Self-attention and the Transformer architecture. See [Transformers Introduction](/aiml-common/lectures/nlp/transformers/transformers-intro).
      </Step>

      <Step title="Run the GERON Chapter 13 notebook">
        Work through the [Processing Sequences Using RNNs and CNNs](https://github.com/ageron/handson-mlp/blob/main/13_processing_sequences_using_rnns_and_cnns.ipynb) notebook.
      </Step>
    </Steps>
  </Accordion>

  <Accordion title="Unit 8, Project">
    <Steps>
      <Step title="Data lakehouse project">
        Build the [data lakehouse project](/aiml-common/projects/lakehouse/index).
      </Step>
    </Steps>
  </Accordion>
</AccordionGroup>

***

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