AI News, Datasets for Data Science and Machine Learning

Datasets for Data Science and Machine Learning

These days, we have the opposite problem we had 5-10 years ago…

Below, you’ll find a curated list of free datasets for data science and machine learning, organized by their use case.

Our picks: Aggregators: While not appropriate for general-purpose machine learning, deep learning has been dominating certain niches, especially those that use image, text, or audio data.

From our experience, the best way to get started with deep learning is to practice on image data because of the wealth of tutorials available.

And for messy data like text, it's especially important for the datasets to have real-world applications so that you can perform easy sanity checks.

Here are a couple options: Aggregators: Streaming datasets are used for building real-time applications, such as data visualization, trend tracking, or updatable (i.e.

Our picks: Aggregators: Web scraping is a common part of data science research, but you must be careful of violating websites' terms of services.

How to Prepare Data For Machine Learning

The process for getting data ready for a machine learning algorithm can be summarized in three steps: You can follow this process in a linear manner, but it is very likely to be iterative with many loops.

Three common data preprocessing steps are formatting, cleaning and sampling: It is very likely that the machine learning tools you use on the data will influence the preprocessing you will be required to perform.

The specific algorithm you are working with and the knowledge of the problem domain will influence this step and you will very likely have to revisit different transformations of your preprocessed data as you work on your problem.

You discovered a three step framework for data preparation and tactics in each step: Data preparation is a large subject that can involve a lot of iterations, exploration and analysis.

For now, just consider the questions raised in this post when preparing data and always be looking for clearer ways of representing the problem you are trying to solve.

Preparing Your Dataset for Machine Learning: 8 Basic Techniques That Make Your Data Better

There’s a good story about bad data told by Martin Goodson, a data science consultant.

It employed machine learning (ML) to automatically sort through patient records to decide who has the lowest death risk and should take antibiotics at home and who’s at a high risk of death from pneumonia and should be in the hospital.

One of the most dangerous conditions that may accompany pneumonia is asthma, and doctors always send asthmatics to intensive care resulting in minimal death rates for these patients.

So, the absence of asthmatic death cases in the data made the algorithm assume that asthma isn’t that dangerous during pneumonia, and in all cases the machine recommended sending asthmatics home, while they had the highest risk of pneumonia complications.

But regardless of your actual terabytes of information and data science expertise, if you can’t make sense of data records, a machine will be nearly useless or perhaps even harmful.

But as we discussed in our story on data science team structures, life is hard for companies that can’t afford data science talent and try to transition existing IT engineers into the field.

Problems with machine learning datasets can stem from the way an organization is built, workflows that are established, and whether instructions are adhered to or not among those charged with recordkeeping.

Yes, you can rely completely on a data scientist in dataset preparation, but by knowing some techniques in advance there’s a way to meaningfully lighten the load of the person who’s going to face this Herculean task.

While those opportunities exist, usually the real value comes from internally collected golden data nuggets mined from the business decisions and activities of your own company.

The companies that started data collection with paper ledgers and ended with .xlsx and .csv files will likely have a harder time with data preparation than those who have a small but proud ML-friendly dataset.

When formulating the problem, conduct data exploration and try to think in the categories of classification, clustering, regression, and ranking that we talked about in our whitepaper on business application of machine learning.

You want an algorithm to answer binary yes-or-no questions (cats or dogs, good or bad, sheep or goats, you get the idea) or you want to make a multiclass classification (grass, trees, or bushes;

Ranking is actively used to recommend movies in video streaming services or show the products that a customer might purchase with a high probability based on his or her previous search and purchase activities.

Hotels know guests’ credit card numbers, types of amenities they choose, sometimes home addresses, room service use, and even drinks and meals ordered during a stay.

For instance, Salesforce provides a decent toolset to track and analyze salespeople activities but manual data entry and activity logging alienates salespeople.

If you’re aggregating data from different sources or your dataset has been manually updated by different people, it’s worth making sure that all variables within a given attribute are consistently written.

If you haven’t employed a unicorn who has one foot in healthcare basics and the other in data science, it’s likely that a data scientist might have a hard time understanding which values are of real significance to a dataset.

The technique can also be used in the later stages when you need a model prototype to understand whether a chosen machine learning method yields expected results.

Data rescaling belongs to a group of data normalization procedures that aim at improving the quality of a dataset by reducing dimensions and avoiding the situation when some of the values overweight others.

Imagine that you run a chain of car dealerships and most of the attributes in your dataset are either categorical to depict models and body styles (sedan, hatchback, van, etc.) or have 1-2 digit numbers, for instance, for years of use.

But the prices are 4-5 digit numbers ($10000 or $8000) and you want to predict the average time for the car to be sold based on its characteristics (model, years of previous use, body style, price, condition, etc.)  While the price is an important criterion, you don’t want it to overweight the other ones with a larger number.

The sets usually contain information about general processes in a wide range of life areas like healthcare records, historical weather records, transportation measurements, text and translation collections, records of hardware use, etc.

If you recommend city attractions and restaurants based on user-generated content, you don’t have to label thousands of pictures to train an image recognition algorithm that will sort through photos sent by users.

So, you still must find data scientists and data engineers if you need to automate data collection mechanisms, set the infrastructure, and scale for complex machine learning tasks.

Becoming a Machine Learning Engineer | Step 2: Pick a Process

Picking your process is super important After a few applied machine learning problems, you usually develop a pattern or process for quickly getting started and achieving good results.

Let me give you a head start and teach you a 5-step systematic process that I developed while becoming a machine learning engineer.

This work forces you to think about the data in the context of the problem before it gets lost in the craziness of algorithms Data Selection: Consider what data is available to you.

Format is, clean it, and take a sample from it Data Transformation: Processed your ready data for machine learning by engineering its features using scaling, attribute decomposition, and attribute aggregation.

You can do this in a few ways, but it’s important to make sure that your results are significant at this point because hyper-parameter tuning isn’t going to turn a crap result in to a good result.

Ensemble Methods: Where predictions are made by combining multiple models Extreme Feature Engineering: Attribute decomposition and aggregation seen in data preparation is pushed to the limits The results of a complex machine learning problem are often meaningless in a vacuum.

Here is a quick template for you to present your results: Why: Define the environment that the problem exists in and set up a motivation for the solution Question: Describe the problem as a question that you went out and answered.

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