• 📚 Aurélien Géron. Hands on Machine Learning with Scikit-Learn, Keras and TensorFlow. [Link]
• 📚 Chris Fregly, Antje Barth. Data Science on AWS. [Link]
• 📚 Hannes Hapke, Catherine Nelson. Building Machine Learning Pipelines. [Link]
• 📚 François Chollet. Deep Learning with Python [Link]
• 📚 Mariano Anaya. Clean Code in Python [Link]
• 🤜 Dataquest Academic Program [Link]
• 😃 CS329S - ML Systems Design [Link]

Week 01 - Warming up

• Git and Version Control
  • Introduction to Git [Link]
  • Git Remotes [Link]
  • Git Branches [Link]
  • Merge Conflicts [Link]
  • Git Installation and Github integration [Link]
• Elements of the Command Line
  • Introduction to the Command Line [Link]
  • The Filesystem [Link]
  • Modifying the Filesystem [Link]
  • Glob Patterns and Wildcards [Link]
  • Users and Permissions [Link]
• Text Processing in the Command Line
  • Getting Help and Reading Documentation [Link]
  • File Inspection [Link]
  • Text Processing [Link]
  • Redirection and Pipelines [Link]
  • Standard Streams and File Descriptors [Link]

Week 02 - Introduction to Data Science [Slide]

• Pandas and Numpy Fundamentals
  • Introduction to Numpy [Link]
  • Boolean Indexing with Numpy [Link]
  • Introduction to Pandas [Link]
  • Exploring Data with Pandas: Fundamentals [Link]
  • Exploring Data with Pandas: Intermediate [Link]
  • Data Cleaning Basics [Link]
  • 🤠 Guided Project - Exploring eBay Car Sales Data [Link]

Week 03 - Clean Code Principles for Data Science and Machine Learning [Slides]

• Outline [Video]
• Coding Best Practices [Video]
• Writing Clean Code [Video]
• Refactoring Code [Video]
• Efficient Code [Video]
• Documentation [Video]
• Python Code Quality Authority (PCQA) - pycodestyle [Video]
• PCQA - pylint [Video]
• PCQA - autopep8 [Video]
• PCQA - nbQA [Video]
• ▶️ Hands on
  • 💾 Datasets [Link]
  • Writting Clean Code
  • Exercise 01
  • Exercise 02
  • Exercise 03
  • Using pycodestyle
  • 🐍 using pylint - [script] [refactored script]
  • 📝 Best practices for writing functions [Link]

Week 04 Production Ready Code [Slide]

• Outline [Video]
• Catching Errors [Video]
• Testing and Data Science [Video]
• A brief introduction about pytest [Video]
• Logging [Video]
• Case study: testing and logging [Video]
• Model Drift [Video]
• Hands on
  • Production ready code
  • Data Visualization Fundamentals [Link]
  • Storytelling Data Visualization and Information Design [Link]

Week 05 Building a Reproducible Model Workflow [Slide]

• Outline [Video]
• Business Reflections [Video]
• Introduction to MLOps [Video]
• A brief history of MLOps and Tools [Video]
• Tools and environment installation [Video] README
• Tools and environment installation cont. [Video] environment.yml
• Machine Learning Pipelines [Video]
• Machine Learning Pipelines - Command Line Interface [Video] my_script.py
• Versioning Data and Artifacts [Video] Upload and version artifacts
• Guided Exercise - CLI + Weights and Biases [Video] Guided Exercise 01
• MLflow Projects [Video]
• Introduction to YAML [Video] YAML Intro.
• Guided Exercise - Build a MLflow component [Video] Guided Exercise 02
• Linking together the components MLflow + Hydra [Video]
• Guided Exercise - Multiples MLflow components + Hydra [Video] Guided Exercise 03
• Additional Material
  • Context Managers [Link]
  • Introduction to Decorators [Link]
  • Decorators: advanced [Link]

Week 06 Building a Reproducible Model Workflow Cont. - Introduction to Machine Learning [Slide]

• Outline [Video]
• What is Machine Learning? [Video]
• Machine Learning Types [Video]
• Variables, Pipeline, Controlling Chaos [Video]
• Data Segregation - train, dev and test sets [Video]
• Bias vs Variance [Video]
• Optional hands on Dataquest.io
  • Machine Learning Fundamentals [Link]

Week 07 Building a Reproducible Model Workflow Cont. - ETL, Data Checks, Data Segregation [Slide]

• Outline [Video]
• Extract, Transform, Load (ETL)
  • Exploratory Data Analysis (EDA) Video [Part I] [Part II] [Part III] [Part IV] [Part V] [Source-Code]
  • Preprocessing [Video] [Source-Code]
• Data Segregation [Video] [Source-Code]
• Data Checks
  • Data Validation [Video [Source-Code]
  • Deterministic Test [Video] [Source-Code]
  • Non-Deterministic Test [Video] [Source-Code]
  • Multiple Hypothesis Testing [Video] [Source-Code]
  • Multiple Hypothesis Testing Using MLFlow [Video] [Source-Code]
  • Multiple Hypothesis Testing Using Parameters in PyTest [Video] [Source-Code]

Week 08 Building a Reproducible Model Workflow Cont. - Train, Validation and Experiment Tracking [Slides]

• Outline [Video]
• A brief review [Video]
• Decision Trees
  • Introduction [Video]
  • Mathematical Foundations [Video]
• Evaluation Metrics
  • How to choose an evaluation metric? [Video]
  • Threshold metrics [Video]
  • Ranking metrics [Video]
• Implementing Pipelines
  • MLOps Level 0 with Pipeline incorporating train [Part I] [Part II] [Part III] [Source-Code]
  • MLOps Level 0 with Pipeline incorporating train and preprocessing [Part I] [Part II] [Part III] [Source-Code]
  • MLOps Level 1 with Pipeline incorporating train and preprocessing [Part I] [Part II] [Source-Code]
  • MLOps Level 1 with Pipeline and Hyper-parameter Tuning [Part I] [Part II] [Part III] [Part IV] [Source-Code]
  • Test evaluation [Part I] [Source-Code]

Week 09 Building a Reproducible Model Workflow Cont. - Final Pipeline, Release and Deploy [Slides]

• Outline [Video]
• Final Pipeline
  • Big picture of the final pipeline [Video]
  • All together [Part I] [Part II] [Source-Code]
• Release for reproducibility
  • Create a GitHub repository for the final pipeline [Video] [Source-Code]
  • Semantic versioning and remote execution [Video] [Source-Code]
• Deployment
  • Deploy with MLflow [Video] [Source-Code]