Get your team access to 3,500 top Udemy courses anytime, anywhere. In this Data science Machine Learning project, we will predict the sales prices in the Housing data set using LinearRegression one of the predictive models. Databricks lets you start writing Spark ML code instantly so you can focus on your data problems.
Get your team access to 3,500 top Udemy courses anytime, anywhere. In this Data science Machine Learning project, we will create Employee Attrition Prediction Project using Decision Tree Classification algorithm one of the predictive models. Databricks lets you start writing Spark ML code instantly so you can focus on your data problems.
In this Data science Machine Learning project, we will create Telecom Customer Churn Prediction Project using Classification Model Logistic Regression, Naive Bayes and One-vs-Rest classifier few of the predictive models. Databricks lets you start writing Spark ML code instantly so you can focus on your data problems.
The practice of investment management has been transformed in recent years by computational methods. This course provides an introduction to the underlying science, with the aim of giving you a thorough understanding of that scientific basis. However, instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language. This course is the first in a four course specialization in Data Science and Machine Learning in Asset Management but can be taken independently. In this course, we cover the basics of Investment Science, and we'll build practical implementations of each of the concepts along the way.
It feels impossible to keep up with every new concept and technology in data science and machine learning. You have multiple languages, libraries and design principles. We have written pieces on different resources that can help data professionals keep up to date with all the various technologies. However, many of these courses cost money. But coursera offers an opportunity to take online courses for free from actual colleges and educational institutions.
Artificial Intelligence today is where personal computers were back in the 90s: a new skill that everyone will have to become familiar with within the next few years. What if you could be as familiar with AI as you are with MS Office? Why this course: The problem at hand is that while there are not enough data scientists and engineers to create AI solutions, there are even fewer managers and leaders who know how to apply AI to business or organizational problems in the right manner, or have the time to learn it in detail. The good news, however, is that just like with computers, most of us do not need to learn how to code to understand and use AI well. This course will help you get a thorough understanding of AI techniques & how to use/manage them, to support your career as well as your organization's growth.
The rapid pace of innovation in Artificial Intelligence (AI) is creating enormous opportunity for transforming entire industries and our very existence. After competing this comprehensive 6 course Professional Certificate, you will get a practical understanding of Machine Learning and Deep Learning. You will master fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will utilize popular Machine Learning and Deep Learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow applied to industry problems involving object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers. You will be able to scale Machine Learning on Big Data using Apache Spark.
As if manufacturers didn't already have enough on their hands trying to find suitable applicants for their shop floors and R&D departments, the world of artificial intelligence is about to explode onto the scene. And when it does, the scramble for talent will only grow maddeningly tougher. This may sound like trouble, but there's a tremendous upside. According to a newly released study by the MAPI Foundation and the Information Technology and Innovation Foundation (ITIF), not only will AI enable machines to do a lot more--but it will also empower humans to do a lot more as well. That means an upsurge of new kinds of jobs related to developing new AI solutions, leading new AI business strategies and supervising AI implementations.
The following is a guest post by Pete McCain, a technology startup enthusiast associated with App Velocity. If you would like to submit a guest post, please contact us. There was a time when we all were highly skeptical about online education because we couldn't fathom a computer screen replacing our classrooms and the education ideals that come with them. But now examining the impact of online education, we can clearly see how eagerly we've embraced the idea of e-learning. It has levelled up education in the developed parts of the world and democratized education where schools and teachers couldn't reach.
Producing content for Massive Open Online Course (MOOC) platforms like Coursera and EdX might be academically rewarding (and potentially lucrative), but it's time-consuming -- particularly where videos are involved. Professional-level lecture clips require not only a veritable studio's worth of equipment, but significant resources to transfer, edit, and upload footage of each lesson. That's why research scientists formerly at Udacity, an online learning platform with over 150 courses, are investigating a machine learning framework that automatically generates lecture videos from audio narration alone. They claim in a preprint paper ("LumièreNet: Lecture Video Synthesis from Audio") on Arxiv.org that their AI system -- LumièreNet -- can synthesize footage of any length by directly mapping between audio and corresponding visuals. "In current video production pipeline, an AI machinery which semi (or fully) automates lecture video production at scale would be highly valuable to enable agile video content development (rather than reshooting each new video)," wrote the paper's coauthors.