This amazing liveVideo course will put your machine learning on the fast track! AWS Machine Learning in Motion gives you a complete tour of the essential tools, techniques, and concepts you need to do complex predictions and other data analysis using the AWS machine learning services! In this interactive liveVideo course, you'll get started with cloud-based machine learning under the guidance of experienced software engineer and TED Speaker Kesha Williams. You'll cut through the theory and jargon as you build a working crime-fighting machine learning algorithm! Starting with a tour of AWS' tools and the basics of machine learning, you'll dive into the learning algorithms supported by AWS, such as linear regression, multinomial logistic regression, and logistic regression.

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.

Welcome to the course on "Simple Linear Regression Analysis ( A Complete Course)" This course covers running and evaluating linear regression models (simple linear regression) including assessing the overall quality of models and interpreting individual predictors for significance with PDF files and complete exercises that consists of examples and concepts . We also explore R-Square in depth, including how to interpret R-Square for significance. Together with in-depth coverage of simple regression, we'll also explore correlation, which is closely related to regression analysis. By the end of this course you will be skilled in running and interpreting your own linear regression analyses, as well as critically evaluating the work of others. Lectures provided in HD video .While you can be confident that you are getting accurate information with Quantitative Specialists, Be confused by regression no longer -- Enroll Today!

Like many other machine learning techniques, it is borrowed from the field of statistics and despite its name, it is not an algorithm for regression problems, where you want to predict a continuous outcome. Instead, Logistic Regression is the go-to method for binary classification. It gives you a discrete binary outcome between 0 and 1. To say it in simpler words, it's outcome is either one thing or another. A simple example of a Logistic Regression problem would be an algorithm used for cancer detection that takes screening picture as an input and should tell if a patient has cancer (1) or not (0).

Multiple regression analysis is tool that allows you to expand on your research question, and conduct a more rigorous test of the association between your explanatory and response variable by adding additional quantitative and/or categorical explanatory variables to your linear regression model. In this session, you will apply and interpret a multiple regression analysis for a quantitative response variable, and will learn how to use confidence intervals to take into account error in estimating a population parameter. You will also learn how to account for nonlinear associations in a linear regression model. Finally, you will develop experience using regression diagnostic techniques to evaluate how well your multiple regression model predicts your observed response variable. Note that if you have not yet identified additional explanatory variables, you should choose at least one additional explanatory variable from your data set.

The Business Statistics and Analysis Specialization is designed to equip you with a basic understanding of business data analysis tools and techniques. You'll master essential spreadsheet functions, build descriptive business data measures, and develop your aptitude for data modeling. You'll also explore basic probability concepts, including measuring and modeling uncertainty, and you'll use various data distributions, along with the Linear Regression Model, to analyze and inform business decisions. The Specialization culminates with a Capstone Project in which you'll apply the skills and knowledge you've gained to an actual business problem. To successfully complete all course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later.

Data Science is quite a large and diverse field. As a result, it is really difficult to be a jack of all trades. Traditionally, Data Science would focus on mathematics, computer science and domain expertise. While I will briefly cover some computer science fundamentals, the bulk of this blog will mostly cover the mathematical basics one might either need to brush up on (or even take an entire course). In most data science workplaces, software skills are a must. While I understand most of you reading this are more math heavy by nature, realize the bulk of data science (dare I say 80%) is collecting, cleaning and processing data into a useful form.

This module, along with module 2B introduces two key concepts in statistics/epidemiology, confounding and effect modification. A relation between an outcome and exposure of interested can be confounded if a another variable (or variables) is associated with both the outcome and the exposure. In such cases the crude outcome/exposure associate may over or under-estimate the association of interest. Confounding is an ever-present threat in non-randomized studies, but results of interest can be adjusted for potential confounders.

It is a practical, hands-on course, i.e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects.

As we know from the basic maths that if we plot an'X','Y' graph, a linear relationship will always come up with a straight line. The equation of a straight line is written using the y mx b, where m is the slope (Gradient) and b is y-intercept (where the line crosses the Y axis). Once we get the equation of a straight line from 2 points in space in y mx b format, we can use the same equation to predict the points at different values of x which result in a straight line. In this formula, m is the slope and b is y-intercept. Let's take a real world example to demonstrate the usage of linear regression and usage of Least Square Method to reduce the errors Let's take a real world example of the price of agricultural products and how it varies based on the location its sold.