7 Powerful Machine Learning Tools to Supercharge Your App Design


A lot of people are associating machine learning or machine learning tool with smarter robots, self-driving cars, smarter homes, etc. However, machine learning is nothing but an artificial intelligence technique.

For developers out there looking to insert AI into their apps, you’ll need to include one or more machine learning tools that can help you achieve that aim. Let’s take a short look at each of them.

Machine Learning: The AI power behind ever-increasing insights and perks to marketings.

Nobody can predict the future. But today, app development is on the rise with more people installing new apps each day.

The app development field has also become highly competitive, more so than it ever has been before.

This leaves designers with a mess to find new ways of optimizing their workflows.

There are over 360 000 mobile applications being developed every year. Since the requirements are no longer just design inputs but are driven by machine learning algorithms experts have created 7 ultimate machine-learning tools for supercharging your app design.

What these Machine learning tools can do for your design work?

The tools and platforms can reveal and simplify insights, take design decisions out of their hands, and unveil predictions.

The hope is that using machine learning to create user interfaces will lead to the less logical effort, better designs, and improved human-computer interaction.

Machine Learning is nothing new by this time, but it’s constantly becoming easier and more accessible for marketers because we’re automating marketing operations that once needed manpower.

With such powerful tools as Google Trends Predictions, Facebook MLPaintbrush for retargeting, and Salesforce Einstein Ads already easy to use in your back pocket, the future of marketing operations feels limitless!

Introduction to Machine Learning (ML)

Whether it’s a computer, a smartphone, a car, or a robot, ML is everywhere. It pervades our daily lives and impacts many aspects of our interaction with machines.

We’ve already used ML-laced algorithms to introduce viewers to the principles guiding its development in the course of three vignettes. In this article, we explore seven ML tools, outline what they do, and examine their strengths and weaknesses.

Tools 1: Gradient Boosting Machine (GBM)

GBM is one of the popular and widely used machine learning tools. If we use GBM for prediction, it will fill up the target layer with raw X’s and Y’s; we call these values weights. With sufficient training, GBM attaches much importance to neighboring points that give accurate predictions.

Tools 2: Random Forest Machine learning tool

Random forest “comes up with different combinations of features and goes through the data with all the combinations. A final result is several trees that can be used to predict new observations.”

Tools 3: Naïve Bayesian Classifier Machine learning tool

Naïve Bayesian classifiers predict an item’s category from a training set of examples consisting of features and the corresponding target values.

The big idea behind Naïve Bayes is its complete independence of each feature, assuming these features correlate with each other in all possible ways–which they often do not.

Tools 4: Support Vector Machine (SVM) Machine learning tool

Support Vector Machines are one of the oldest, most successful, and easy-to-implement machine learning algorithms. This process trains the algorithm to categorize or detect patterns identified in data based on inputs from a training dataset. The algorithm computes the optimum weight vector for data points (e.g. words).

Tools 5: Logistic Regression

This Logistic Regression machine learning tool is generally used for classification problems. This machine learning tool is based on the concept of possibility and is a predictive analytic method.

This model is also known as Linear Regression but it is quite complex. Logistic function and sigmoid function are also known as linear functions.

Tools 6: K Nearest Neighbor Support Vector Machines (KNN SVMs)

The SVM-KNN algorithm, which combines the support vector machine (SVM) and the K-Nearest Neighbors (KNN), is used to build a solar flare forecasting model. The SVM-KNN strategy increases the SVM classifier model by utilizing the KNN algorithm regarding the distribution of different tests in a feature space, which is based on an established link between SVM and KNN.

Tools 7: Extremely Randomized Trees (XRT) Machine learning tool

This Extremely Randomized Tree Machine learning tool is similar to the Random Forests.

When constructing a tree, ERT does not resample observations. (Bagging is not something they do.)
The “best split” is not used by ERT.

Thi Machine learning tool can be used for regression and classification which is the same as Random Forest.


Machine Learning is a great resource for designers. It can automate repetitive tasks, produce designs that are complex to design by hand, and understand trends in design.

Read Next Article: Artificial Intelligence vs Machine Learning in 2021

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