Data Analytics

Here is a repository containing template projects and exercises involving data analytics 👉 Data Analytics

An ML pipeline can look something like this:

• Data acquisition and cleaning
• Exploratory data analysis (EDA)
  • Summary statistics
  • Visualization
  • Preliminary statistics
• Feature engineering
  • Feature reduction
  • Scaling
• Spot checking algorithms
• Hyperparameter tuning
• Model training and evaluation
• Model selection
• Explaning the model

Cloud technologies also makes it possible to develop and deploy data products, which involve the following tasks on top of those mentioned above:

• Model deployment
• Batch prediction

Still fairly new to cloud technologies (I used Google Map API, and DialogFlow before) and so currently looking into Google Cloud Products ☁️.