Today is a tremulous time for a lot of people in almost every sector around the globe. Things are changing fast thanks primarily to awesome technological advances like nothing we have seen since the invention of gunpowder. No one can deny how the world has been and continues to be changed by gunpowder. Now it is the internet that is disrupting entire societies, both for good and for evil. Everything from Artificial Intelligence (AI) and Machine Learning (ML) to small IoT gadgets that monitor an employee's daily activities are blasting off into the stratosphere as they continue to develop.

About this course: Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist's toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated.

In this multi-part series, we will explore how to get started with tensorflow. This tensorflow tutorial will lay a solid foundation to this popular tool that everyone seems to be talking about. The second part is a tensorflow tutorial on getting started, installing and building a small use case. If you have tensorflow already installed, you can just skip to the next section. Different operating systems have different means to install tensorflow.

No matter where you are in your coding journey this course will get you up and running with Apache Spark, from installation and configuration to power user with 5.5 hours of top quality video tutorials. The first chapters are a step by step guide through the fundamentals of Spark programming, covering data frames, aggregations and data sets. Next you'll dive into what you can do with all the data you collect using Spark, filter results with R and expose your data to Python for deeper processing and presentation using charts and graphs. After that, you go further into the capabilities of Spark's stream processing, machine learning, and graph processing libraries. The last chapter combines all the skills you learned from the preceding chapters to develop a real-world Spark application.By the end of this video, you will be able to consolidate data processing, stream processing, machine learning, and graph processing into one unified and highly interoperable framework with a uniform API using Scala or Python.

You probably can't remember what it feels like to play Super Mario Bros. for the very first time, but try to picture it. An 8-bit game world blinks into being: baby blue sky, tessellated stone ground, and in between, a squat, red-suited man standing still--waiting. He's facing rightward; you nudge him farther in that direction. A few more steps reveal a row of bricks hovering overhead and what looks like an angry, ambulatory mushroom. Another twitch of the game controls makes the man spring up, his four-pixel fist pointed skyward. Maybe try combining nudge-rightward and spring-skyward?

About this course: This course aims to help you to draw better statistical inferences from empirical research. First, we will discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, you will learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for your study, for example in order to achieve high statistical power. Subsequently, you will learn how to interpret evidence in the scientific literature given widespread publication bias, for example by learning about p-curve analysis. Finally, we will talk about how to do philosophy of science, theory construction, and cumulative science, including how to perform replication studies, why and how to pre-register your experiment, and how to share your results following Open Science principles.

I am a data scientist, big data engineer, and full stack software engineer. I have a masters degree in computer engineering with a specialization in machine learning and pattern recognition. I have worked in online advertising and digital media as both a data scientist and big data engineer, and built various high-throughput web services around said data. I've created new big data pipelines using Hadoop/Pig/MapReduce. I've created machine learning models to predict click-through rate, news feed recommender systems using linear regression, Bayesian Bandits, and collaborative filtering and validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Humber College, and The New School.

This course aims to put the entire world of machine learning with AWS in front of you. Machine learning has become the new black. Predictive analytics is a complex domain requiring coding skills, an understanding of the mathematical concepts underpinning machine learning algorithms, and the ability to create compelling data visualizations. The challenge in today's world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge.

With today's software, statistics is easy, right? Even before the start of Data Mania, circa 2010, vendors have been suggesting that if we buy their easy-to-use statistical software, we don't really need to know what we're doing. Since then, hogwash about automated machine learning and "AI" has populated the blogosphere in great quantity. What should populate the blogosphere instead are the true horror stories about costly errors people with little background in statistics are making with this easy-to-use software. Over time, they may help, but typically these programs and courses cover a wide range of subjects superficially.

Want to watch this again later? Sign in to add this video to a playlist. Report Need to report the video? Sign in to report inappropriate content. Report Need to report the video?