Simply Understandable Language
MOBJAI™ uses plain and understandable language, making complex concepts accessible to all. Its user-friendly approach ensures that users can easily grasp and utilize AI-driven analytics without requiring technical jargon or data science expertise.
all companies use unnecessary words, we keep it simple
We just wanted you to have the right language to hand
Artificial intelligence (AI) is a branch of computer science that involves the development of algorithms and systems that can perform tasks that would typically require human intelligence, such as understanding natural language, recognizing images, and making decisions. Business people can think of it as a way to automate and improve decision-making and processes by mimicking human cognitive functions such as learning, problem-solving, and perception. AI can be applied in various areas of business, such as customer service, marketing, manufacturing, and finance. For example, a business might use AI-powered chatbots to interact with customers, or use machine learning to predict which products will be in high demand. As AI technology advances, it has the potential to revolutionize industries and create new opportunities for growth and efficiency.
Machine learning is a method of teaching computers to learn from data, without being explicitly programmed. It is a branch of artificial intelligence that involves training algorithms on a dataset, so that the algorithm can make predictions or take actions on new data it encounters. Business people can think of it as a way for a computer to improve its performance on a certain task by learning from examples, without being explicitly programmed how to do it. For example, a business might use machine learning to predict which customers are most likely to make a purchase, or to identify patterns in sales data to improve inventory management. Machine learning can be applied to many areas of business, such as customer segmentation, fraud detection, predictive maintenance, and more.
AutoML (Automatic Machine Learning) is a technology that allows businesses to automate the process of building and optimizing machine learning models. This can save time and resources for businesses by reducing the need for specialized data scientists to build models from scratch. AutoML can also improve the accuracy and performance of models by automating the selection of algorithms and hyperparameters. This can help businesses to quickly and easily make use of the power of machine learning to improve their operations and gain a competitive advantage.
Data science is an interdisciplinary field that involves using scientific methods, processes, algorithms and systems to extract insights and knowledge from structured and unstructured data. It encompasses a wide range of techniques including data visualization, data mining, machine learning and statistics. Business people can think of it as a way to extract valuable insights from the data they collect and use it to make strategic decisions, improve operations and gain a competitive advantage. Data science can be applied to different areas of business, such as customer behavior analysis, market research, financial forecasting, and performance optimization.
A data scientist is a professional who uses statistical and computational methods to extract insights and knowledge from data. They work with large and complex data sets, and use a variety of tools and techniques to analyze and interpret the data. They also have experience in machine learning and are able to build predictive models to help businesses make decisions. Essentially, they help organizations to make data-driven decisions by turning raw data into actionable insights. Business people can think of them as the experts who help to unlock the value of the data that companies collect and make sense of it to improve their business performance.
In machine learning, an outcome column (also known as a target or label column) is the column in a dataset that contains the values that the model is trying to predict. For example, in a dataset of customer information, the outcome column might be whether or not the customer made a purchase. Business people can think of it as the column that represents the desired result or outcome that they want to predict or influence.
Feature selection is the process of choosing a subset of relevant features from a larger set of features to use in building a machine learning model. The goal of feature selection is to improve the performance of the model by removing irrelevant or redundant features, and make the model more efficient and easier to understand. Imagine you are a store trying to predict which products will be the most popular next month. You have a lot of information about your products, like their price, brand, and customer reviews. But not all of this information is going to be helpful in predicting the popularity of the products. For example, the color of the product might not be as important as its price or brand. So, feature selection helps us pick out the most important information, or features, to use in our prediction. By choosing the right features, we can make our prediction more accurate and be able to make better business decisions, for example, which products to stock more and which to put on sale. This can ultimately lead to an increase in sales and profits.
Overfitting in machine learning happens when a model is so good at fitting the training data that it starts to memorize the noise or random variation present in the data, instead of learning the underlying pattern. This can lead to poor performance when the model is applied to new, unseen data. Imagine you are a store trying to predict the number of customers you will have in a day based on past data, like temperature and day of the week. You train a model on this data, and it seems to work great, it can predict the number of customers very accurately. But when you use the model to predict the number of customers for future days, it doesn't work as well because it's not accounting for other factors like holidays, special events, or unforeseen weather changes. This is similar to overfitting in machine learning, where a model is too good at fitting the training data, but doesn't generalize well to new, unseen data. In business problems, overfitting can lead to poor decision making and wasted resources. To prevent overfitting, we use techniques like cross-validation, regularization and early stopping.
Hyperparameters are settings that are used to control the behavior of a machine learning model. They are set before the model is trained and can have a big impact on the model's performance. Examples of hyperparameters include the number of layers in a neural network, the learning rate used during training, and the number of trees in a random forest. Understanding and optimizing these settings can help improve the accuracy of the model and make it more effective for business use cases.
Virtual Automated Analytics Assistant
A virtual automated analytics assistant is an AI-powered tool that helps users analyze and interpret data, draw insights, and make data-driven decisions. It automates the process of data analysis, making it easier for users without technical expertise to understand complex data sets. This type of assistant can also generate reports, provide recommendations, and answer queries based on the data, allowing users to focus on making informed decisions and strategic actions.