joserfjuniorllms/azure-databricks

Azure Databricks Demos

azure-databricks

Azure Databricks Demos

Treinamento

Data Science no Azure Databricks

Overview

• 15hs não são o suficiente para mostrar tudo de ML

• Mudar para uma abordagem mais Business

• Data Science for Business

• Livro: Data Science for Business - O'Reilly Media

• Curso: Data science for Business - Udacity

• Ferramenta: Azure Databricks and Azure Cognitive Services

• Apresentação Pessoal

• Experiência Pessoal

• "[...]This book is unique in that it does not give a cookbook of algorithms, rather it helps the reader understand the underlying concepts behind data science, and most importantly how to approach and be sucessful at problem solving." - Chirs Volinsky, Director Statistics Research, AT&T Labs and Winner of the $1 Million Netflix Challenge.

• Data Science for Business is intended for several sorts of readers:

  • Business people who will be working with data scientists, managing data science oriented projects, or investing in data science ventures,
  • Developers who will be implementing data science solutions, and
  • Aspiring data scientists.

• This is not a book about algorithms.

• keep math and statistics to a minimum.

• Databricks ML Documentation

• Azure Cognitive Services

Content

Day 01:

• DS Fundamentals (5W2H)
  • Data Science (What?)
    • Scientific Method
    • Analytics
  • Knowledge Area (Where?)
    • Computer Science
    • IA
    • Machine Learning
  • Context (When?)
    • Cloud
    • Big Data
  • Cases (Why?)
    • Classification
    • Regression
    • Clustering
    • Generalization
    • Association
  • Data Scientist (Who?)
    • Data Scientist vs Data Engineering
    • Data Scientist vs All
  • Tools (How?)
    • R
    • Python
    • Scala
    • Spark
    • Databricks
    • Koalas
  • Career (How much?)
    • Trend & Hype
    • How to become one
    • Suggestions
• DS 101
  • Correlation
  • Linear Regression (Regression)
  • Logistic Regression (Classification)
• DS 102
  • Similarity
  • KNN (Supervised)
  • KMeans (Clustering)
• DS 103
  • PCA (Generalization)
  • Apriori (Association
• DS 104
  • Attribute Selection
  • Cross Validation
  • Metrics

Day 02:

• DS 201
  • Machine Learning Lifecycle
  • Dataset (Raw Data)
  • Text Mining (Data Prep)
  • Naive Bayes (Training)
  • Model Export (Deployment)
  • Review & Examples
• DS 202
  • Model Selection
  • Hyper parametrization
  • Model Evaluation
• DS 302
  • Databricks ML
  • Third Parties ML
  • Cognitive Services
• Hands-on 01
• Hands-on 02

Reference

1. http://data-science-for-biz.com/
2. https://docs.databricks.com/spark/latest/mllib/index.html
3. https://docs.microsoft.com/en-us/azure/cognitive-services/welcome