"Data preparation and data cleaning... more than 60% of the work that you'll do is like data preparation and data cleaning."
"If 80% of our work is preparing high-quality data, then I think preparing that data is a core part of the work of a machine learning engineer."
"You're basically like a data janitor a lot, and that's a really important step because if you're generating a model that's very flawed, dirty, or has too many missing values, the model's not going to work very well."
"...few short learning here means you need to prepare the training data set where you have a sample question and a corresponding SQL query."
"That's how you prep your data set for those categorical values variables."
"Google Cloud Data Prep is a self-service data preparation tool."
"You can try it today, just go to your console and just launch data prep directly from there."
"Data prep allows you to do this comparison much much more easily."
"Choose your input tables, the data, whatever you want. Mostly, you narrow down on the events of interest, and then you parse and prepare the fields if needed."
"The workflows are very different; if you're doing a train test split, you traditionally could do that just by randomly shuffling. Now you have to care about preserving the time ordering when you create the feature and target variable."
"Please check out the video about common pitfalls and mistakes people make in preparing datasets that we have linked in the description section."
"We're going to combine all those features and we'll jump into splitting the data."
"Data wrangling is a process of cleaning, structuring, and enriching raw data into a desired format for better decision making."
"Data cleaning often is 80% of the work in a reservoir characterization study."
"Once we're done with this, our data will be ready for clustering, for machine learning, for classification, for regression, for anything else that we want to do."
"Once you build your new features, once you have your data cleaned, once it's in a form accessible for modeling, you can actually build your models."
"Pandas will enable you to do your data preparation, and it might even enable you to automate those processes."
"As they often say in data science, about 80-90% of your work is actually cleaning data."
"Data cleaning and preparation is probably 80% of the time that you spend on data analysis."
"Once we understand how to deal with data and shape our data so it's ready for the pivot table, we're gonna dive into the pivot table."
"Exploratory data analysis... is very important in terms of getting used with the features."
"Get your data, split that data, look at some of your training data, get some tools."
"The Python script will not only help you import a CSV file to a Postgres database but it will actually clean up the file names, clean up the column headers, and just prepare the entire dataset to be uploaded to the database itself."
"It's a good idea if you have your data balanced."
"Before we can get into the K-means algorithm itself, we need some data to work with."
"After this discussion, you should have the data ready to be trained upon."
"Our goal at the beginning of this was to get our data in a clean standard format for analysis."
"Prodigy is a modern annotation tool for creating training data for machine learning models."
"With Tableau Prep, we're helping you easily clean, manipulate, and get your data ready for analysis."
"Always remember you need to follow these three guidelines: prepare the data in the proper way, make considerations about the data model and the processes, and finally, there is the outcomes and the metrics that you want to report."
"Eighty percent of the effort associated with building an AI system is data wrangling or data architecture."
"Whether using a convolution neural network or an LSTM, you need to put the data into a specific form for time series."
"You need to get your data in the best shape that you possibly can before bringing it into Illustrator."
"With Glue DataBrew, you can prepare your datasets visually allowing you to reach your analysis in really effective way."
"You need to make non-stationary series stationary to be able to use that for forecasting."
"Make sure you have your data cleaned and ready to be analyzed."
"So, Hugging Face transformers has these, you know… As I say it right now, I find them somewhat obscure and not particularly well documented expectations about your data, that you kind of have to figure out."
"When you're preparing for data analytics, a lot of models can handle these categorical data types."
"Wrangling of data basically means to cleanse the data and to make it fit for your usage."
"If you want this to work for future data that you haven't seen yet, use a train-test split."
"Duplicate report is a very useful command, especially when it comes to merging."
"Power Query is an advanced feature of Microsoft Excel that allows you to prepare your data for analysis."
"Transform means to clean it up and get it ready to actually being used."
"We spend 80% of our time preparing data and the other 20% complaining about preparing data."
"We've gathered our data, we've prepared it, chosen a model, and trained it."
"If you have your own data set, how do you decide what goes into the learning algorithm?"