Having built up some fundamental notions regarding graphs, we turn to a very basic algorithmic question: node-to-node connectivity. Here is a complete guide to graph algorithms that you can rely on to practice for your next technical interview. No matter how complicated these concepts may seem, repeatedly getting familiar with different techniques and problems will make you more competent at solving them.
The field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix’s algorithms to make movie suggestions based on movies you have watched in the past or Amazon’s algorithms that recommend books based on books you have bought before.
The field of AI is broad and has been around for a long time. Deep learning is a subset of the field of machine learning, which is a subfield of AI. The facets that differentiate deep learning networks in general from “canonical” feed-forward multilayer networks. Deep learning has been a challenge to define for many because it has changed forms slowly over the past decade. Here are the 10 powerful deep learning methods AI engineers can apply to their machine learning problems.
Data Scientists at Work displays how some of the world’s top data scientists work across a dizzyingly wide variety of industries and applications — each leveraging her own blend of domain expertise, statistics, and computer science to create tremendous value and impact.
Often a classifier will have some confidence value in each category. These are most often generated by probabilistic classifiers. Sometimes, we threshold the probability values. In computer vision, this happens a lot in detection. The optimal threshold varies depends on the tasks. Some performance metrics are sensitive to the threshold. This post is going to cover some very basic concepts in machine learning, from linear algebra to evaluation metrics. It serves as a nice guide to newbies looking to enter the field.
Decision tree is a type of supervised learning algorithm that can be used in both regression and classification problems. It works for both categorical and continuous input and output variables. The major advantage of using decision trees is that they are intuitively very easy to explain. They closely mirror human decision-making compared to other regression and classification approaches. They can be displayed graphically, and they can easily handle qualitative predictors without the need to create dummy variables.
Sales is an integral function of any tech companies, especially enterprise software ones. If your company is building a consumer product, strong marketing strategies will help you gain customers. But if your company is SaaS-based, then strong salespeople are a must to get clients, and learning about sales is the perfect way to know more about the enterprise industry. The 2 most important skills that a salesman needs to learn are deliberate practice and teamwork + accountability.
Introduction to Big Data provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. A solid understanding of the basic concepts, policies, and mechanisms for big data exploration and data mining is crucial if you want to build end-to-end data science projects. Many datasets are too large to fit on a single machine. Unstructured data may not be easy to insert into a database. Distributed file systems store data across a large number of servers.
Clustering is a Machine Learning technique that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. In theory, data points that are in the same group should have similar properties and/or features, while data points in different groups should have highly dissimilar properties and/or features. Clustering is a method of unsupervised learning and is a common technique for statistical data analysis used in many fields.
There is traditionally no way to test marketing attribution models before using them in a real business context, seeing as it’s not possible to compare the algorithm’s output to some source-of-truth-data. Marketing attribution is still an evolving discipline, and data scientists are exploring possible ways of testing these models for a possible look into the performance before applying them to real-time data and doing a sort of real-life testing by moving marketing budget around.