This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own linear regression module in Python. Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you'll be returning to it for years to come. In the first section, I will show you how to use 1-D linear regression to prove that Moore's Law is true.

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.

There is no doubt that Artificial Intelligence is one of the most sophisticated of all the emerging technologies. Artificial Intelligence deals with the understanding of machines and programming them to do tasks autonomously as well as helping them get smarter. It requires a great deal of understanding and problem-solving. It's also one of the most expensive subjects to learn if you are eager to secure a career in this field. Luckily for our lovely readers, we have compiled three courses for you below which will help you understand the basics of Artificial Intelligence for zero price.

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?

Shortening product life cycles and constant evolution of international products and technologies are posing major challenges to businesses. Not just start-ups, but established businesses too are having to constantly evolve for keeping up with new trends in products, services and technologies. With innovation becoming a buzz word, going back to school to keep up with, if not go ahead of the curve, is seeing top managers and CEOs undertaking courses to acquire new skills-- 'retooling', in corporate jargon. "Typically, people who have reached the corner office in large organisations would have done their MBAs in the 1970s and 1980s," says Dr Farhan Pettiwala, president of Enactus India, a subsidiary of Enactus Worldwide, an international non-profit organisation of students with a focus on community empowerment through promotion of entrepreneurship. "They are extremely good at people skills, but struggle with technology, especially when it comes to the Internet of Things and Artificial Intelligence. So this aspect is missing in Indian education."

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.