Collaborating Authors


What Are the Most Important Preprocessing Steps in Machine Learning and Data Science?


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.

Data Science Blogathon 20th Edition - Analytics Vidhya


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!

Community Detection with Node2Vec


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Rateless Codes for Near-Perfect Load Balancing in Distributed Matrix-Vector Multiplication

Communications of the ACM

Large-scale machine learning and data mining applications require computer systems to perform massive matrix-vector and matrix-matrix multiplication operations that need to be parallelized across multiple nodes. The presence of straggling nodes--computing nodes that unpredictably slow down or fail--is a major bottleneck in such distributed computations. Ideal load balancing strategies that dynamically allocate more tasks to faster nodes require knowledge or monitoring of node speeds as well as the ability to quickly move data. Recently proposed fixed-rate erasure coding strategies can handle unpredictable node slowdown, but they ignore partial work done by straggling nodes, thus resulting in a lot of redundant computation. We propose a rateless fountain coding strategy that achieves the best of both worlds--we prove that its latency is asymptotically equal to ideal load balancing, and it performs asymptotically zero redundant computations. Our idea is to create linear combinations of the m rows of the matrix and assign these encoded rows to different worker nodes. The original matrix-vector product can be decoded as soon as slightly more than m row-vector products are collectively finished by the nodes. Evaluation on parallel and distributed computing yields as much as three times speedup over uncoded schemes. Matrix-vector multiplications form the core of a plethora of scientific computing and machine learning applications that include solving partial differential equations, forward and back propagation in neural networks, computing the PageRank of graphs, etcetera. In the age of Big Data, most of these applications involve multiplying extremely large matrices and vectors and the computations cannot be performed efficiently on a single machine. This has motivated the development of several algorithms that seek to speed up matrix-vector multiplication by distributing the computation across multiple computing nodes.

Complete Machine Learning & Data Science Bootcamp 2022


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Parametric vs. Non-parametric tests, and when to use them


The fundamentals of Data Science include computer science, statistics and math. It's very easy to get caught up in the latest and greatest, most powerful algorithms -- convolutional neural nets, reinforcement learning etc. As an ML/health researcher and algorithm developer, I often employ these techniques. However, something I have seen rife in the data science community after having trained 10 years as an electrical engineer is that if all you have is a hammer, everything looks like a nail. Suffice it to say that while many of these exciting algorithms have immense applicability, too often the statistical underpinnings of the data science community are overlooked.

9 Completely Free Statistics Courses for Data Science


This is a complete Free course for statistics. In this course, you will learn how to estimate parameters of a population using sample statistics, hypothesis testing and confidence intervals, t-tests and ANOVA, correlation and regression, and chi-squared test. This course is taught by industry professionals and you will learn by doing various exercises.

15 Best Data Science Programs Online in 2022- [Free Programs Included]


This is a completely free course and a good first step towards understanding the data analysis process. In this course, you will learn the entire data analysis process including posing a question, data wrangling, exploring the data, drawing conclusions, and communicating your findings. This course will also teach Python libraries NumPy, Pandas, and Matplotlib.

5 Clustering Methods in Machine Learning


In the beginning, let's have some common terminologies overview, A cluster is a group of objects that lie under the same class, or in other words, objects with similar properties are grouped in one cluster, and dissimilar objects are collected in another cluster. And, clustering is the process of classifying objects into a number of groups wherein each group, objects are very similar to each other than those objects in other groups. Simply, segmenting groups with similar properties/behaviour and assign them into clusters. Being an important analysis method in machine learning, clustering is used for identifying patterns and structure in labelled and unlabelled datasets. Clustering is exploratory data analysis techniques that can identify subgroups in data such that data points in each same subgroup (cluster) are very similar to each other and data points in separate clusters have different characteristics.

SQL to SARIMAX: How I navigate the first time-series analysis personal project for my portfolio


The diagnostics plot for this particular model shows a decently good fit . When being used for prediction, it followed the real trend closely.