The unprecedented growth of mobile devices, applications and services have placed the utmost demand on mobile and wireless networking infrastructure. Rapid research and development of 5G systems have found ways to support mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Moreover inference from heterogeneous mobile data from distributed devices experiences challenges due to computational and battery power limitations. ML models employed at the edge-servers are constrained to light-weight to boost model performance by achieving a trade-off between model complexity and accuracy. Also, model compression, pruning, and quantization are largely in place.
Automated Machine Learning (AutoML) is a concept that provides the means to utilise existing data and create models for non-Machine Learning experts. In addition to that, AutoML provides Machine Learning (ML) professionals ways to develop and use effective models without spending time on tasks such as data cleaning and preprocessing, feature engineering, model selection, hyperparameter tuning, etc. Before we move any further, it is important to note that AutoML is not some system that has been developed by a single entity. Several organisations have developed their own AutoML packages. These packages cover a broad area, and targets people at different skill levels.
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Data Science and Machine Learning has been the latest talk right now and companies are looking for data scientists and machine learning engineers to handle their data and make significant contributions to them. Whenever data is given to data scientists, they must take the right steps to process them and ensure that the transformed data can be used to train various machine learning models optimally while ensuring maximum efficiency. It is often found that the data that is present in real-world is oftentimes incomplete and inaccurate along with containing a lot of outliers which some machine learning models cannot handle, leading to suboptimal training performance. It is also important to note that there might be duplicate rows or columns in the data which must be dealt with before giving it to machine learning models. Addressing these issues along with many others can be crucial, especially when one wants to improve model performance and generalizing ability of the model.
In this project we will be working with a data set, indicating whether or not a particular internet user clicked on an Advertisement. We will try to create a model that will predict whether or not they will click on an ad based off the features of that user. Welcome to this project on predict Ads Click in Apache Spark Machine Learning using Databricks platform community edition server which allows you to execute your spark code, free of cost on their server just by registering through email id. In this project, we explore Apache Spark and Machine Learning on the Databricks platform. I am a firm believer that the best way to learn is by doing.
Problem Statement A target marketing campaign for a bank was undertaken to identify a segment of customers who are likely to respond to an insurance product. Here, the target variable is whether or not the customers bought insurance product and it depends on factors like Product usage in three months, demographics, transaction patterns as like deposit amount, checking account, a branch of the bank, Residential information (like urban, rural) and so on.
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Either to identify the best model or to understand the nuances of the model with different changes to the data or the hyperparameters -- you would want to perform numerous machine learning experiments. The results could be interesting that enable the process of model selection. As part of my job, I usually have to perform several ML experiments which can be -- (say) to test the effectiveness of dimensionality reduction techniques, text preprocessing techniques (in case of an NLP model), or simple things like playing with the size of the test set. Either way, you might have to run a single code multiple times and record all the observations for comparison later. This is slightly different from the hyperparameter tuning process, and our aim is to identify the technique that best suits our problem.
The Data Science Blogathon by Analytics Vidhya began with a simple mission: To bring together a large community of data science enthusiasts to share their knowledge with the world. With 4000 articles under our belt on various topics such as Data Science, Machine Learning, Deep Learning, Data Lakes, and Data Engineering published by over 700 authors who are avid data science enthusiasts, students, professionals and researchers from across the globe. We bring to you the 20th edition of the Data Science Blogathon. This month's Data Science Blogathon brings you more rewards for you through our special referral programme. Yes, you read that right!