Machine Learning For Dummies

Index

• Machine Learning For Dummies
  • Index
  • Episode 1: A Pythonic Hope
    • E1: Information
    • E1: Contents
  • Episode 2: Python Strikes Back
    • E2: Information
    • E2: Contents
  • Episode 3: Return of the Tensor
    • E3: Information
    • E3: Contents
  • Dates 📆
  • Show Some ❤️

Episode 1: A Pythonic Hope

E1: Information

• Type: Talk
• Level: Beginner
• Duration: 2-3 Hours with a Break
• Prerequisites: Little bits of programming knowledge may help you understand some of the horrible jokes I make
• Technical Requirements: None
• Materials:
  • Jupyter Notebook
  • PDF - [COMING SOON]

E1: Contents

• Machine What?
  • Don't believe the hype
  • GG EZ
  • The Data Jedi Path
• Python: Learning to love Snakes
  • General overwiew of the language
  • Pythonic Syntax and Style
  • Pythonic Objects - Strings' Theory - Python as a calculator - Logical Python
    • Lists
    • Tuples
    • Dictionaries
  • The Code Must Flow
  • Functions
  • Import Antigravity
  • Python Mind Tricks
• Use Python Luke!
  • Royal Rumble
  • The Python Data/Scientific Ecosystem
  • Kong Fu Pandas [COMING SOON]
  • Data Plumbing
  • Data Porn
  • WE ARE LEGION
    • From Dask till dawn [COMING SOON]
    • Twinke twinkle little Spark [COMING SOON]
  • 010010 TOTALLY NOT A CHAPTER ON ROBOTS - END OF TRANSMISSION [COMING SOON]
  • De Naturalis Lingua [COMING SOON]

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Episode 2: Python Strikes Back

E2: Information

• Type: Workshop
• Level: Beginner - Intermediate
• Duration: 2-3 Hours with a Break
• Prerequisites: Basic Python Notions (see Episode 1: A Pythonic Hope)
• Technical Requirements:
  • Docker
  • Custom Docker Image (COMING SOON)
  • GitHub Account
  • Join the machine-learning-for-dummies Gitter Room for all communications and technical discussion regarding the Machine Learning for Dummies series!
  • Watch (and if you like it ⭐) the Machine Learning For Dummies Repo
• Materials:
  • Docker Lightning talk pdf
  • Custom Docker Image - [COMING SOON]
  • Jupyter Notebook - [COMING SOON]

E2: Contents

• Why we like whales: Introduction to docker by @federicoB
• Enter the Mad Scientist lab: IPython + Jupyter (lab)
• Numpy and the Masters of the vector-verse: From lists to vector
• Of Snakes and Pandas: Pandas Dataframe 101
• Nom Nom Nom: Ingesting Data
• Pandarellas - Becoming Cinderellas with Pandas 1: Cleaning
• Pandarellas - Becoming Cinderellas with Pandas 2: Transformation
• Bow Chicka Wow Wow: Introduction to Data Porn (also known as Data Visualization)

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Episode 3: Return of the Tensor

E3: Information

• Type: Talk
• Level: Intermediate
• Duration: 2-3 Hours with a Break
• Prerequisites: Numpy and Pandas basics
• Materials:
  • Introduction to Machine Learning
  • Deep Learning introduction with Tensorflow

E3: Contents

• Introduction to Machine Learning 0. What is Machine Learning?

  1. Why Use Machine Learning?
  2. Types of Machine Learning
  3. Supervised Learning
  4. Unsupervised Learning
  5. Semi-Supervised Learning
  6. Reinforcement Learning
  7. Machine Learning Challenges

• Glimpsing at: scikit-learn [COMING SOON]

  • An exceptional API [COMING SOON]
  • Machine Learning true Workhorse [COMING SOON]
  • Showcase: Diabetes [COMING SOON]

• Deep Learning introduction with Tensorflow by @galeone

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Dates 📆

• 25/11/2017 - Episode 1: A Pythonic Hope - @ImpactHub, Reggio Emilia, Italy
• 30/11/2017 - Episode 1: A Pythonic Hope - @LuogoComune, Bologna, Italy
• 30/01/2018 - Episode 2: Python Strikes Back - @LuogoComune, Bologna, Italy
• 20/02/2018 - Episode 2: Python Strikes Back Remastered [Replica] - @LuogoComune, Bologna, Italy
• 22/06/2018 - Episode 3: Return of the Tensor - @LuogoComune, Bologna, Italy

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