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Advanced Data Mining projects with R Udemy

@machinelearnbot

Advanced Data Mining Projects with R takes you one step ahead in understanding the most complex data mining algorithms and implementing them in the popular R language. Follow up to our course Data Mining Projects in R, this course will teach you how to build your own recommendation engine. You will also implement dimensionality reduction and use it to build a real-world project. Going ahead, you will be introduced to the concept of neural networks and learn how to apply them for predictions, classifications, and forecasting. Finally, you will implement ggplot2, plotly and aspects of geomapping to create your own data visualization projects.By the end of this course, you will be well-versed with all the advanced data mining techniques and how to implement them using R, in any real-world scenario.


Tableau 10 and Tableau 9.3 Desktop, Server & Data Science

@machinelearnbot

This course is about learning Business Intelligence & Analytical tool called Tableau, which has been in leaders position since 4 years Business Intelligence, Analytics, Data Visualisation, Tableau desktop, Tableau server, Tableau & Hadoop, Tableau & R, are the common terminologies used to find this course We have included course content in form of powerpoint presentation, datasets used for visualisation, 2 live case study projects for download, interview questions, sample resumes/profiles for job seekers This course is extremely exhaustive & hence will last for more than 25 hours Course is structured to start with introduction to the tool & the principles behind data visualisation. From there Tableau desktop is explained thoroughly including analytical concepts behind applicable visualisation. Finally course ends with explanation on Tableau server & the final 2 use cases as projects along with interview questions for job seekers Jobs are abundant for Tableau & salaries are very promising & highest in this domain. Also this course is very exhaustive which includes Statistics, Forecasting, Regression models, K-means Clustering, Text Mining, Hadoop & R required for Tableau. Also included are Tableau Desktop & Server concepts in one course.


Deep Learning Prerequisites: Linear Regression in Python

@machinelearnbot

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.


Regression Models Coursera

@machinelearnbot

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.


Python for Machine Learning and Data Mining - Udemy

@machinelearnbot

Data Mining and Machine Learching are a hot topics on business intelligence strategy on many companies in the world. These fields give to data scientists the opportunity to explore on a deep way the data, finding new valuable information and constructing intelligence algorithms who can "learn" since the data and make optimal decisions for classification or forecasting tasks. This course is focused on practical approach, so i'll supply you useful snippet codes and i'll teach you how to build professional desktop applications for machine learning and datamining with python language. We'll also manage real data from an example of a real trading company and presenting our results in a professional view with very illustrated graphical charts. We will make a review of the main packages for scientific use and data analysis in python such us Numpy, Pandas, Matplotlib, Seaborn, Scikit-Learn and more.


Classification-Based Machine Learning for Finance

@machinelearnbot

Finally, a comprehensive hands-on machine learning course with specific focus on classification based models for the investment community and passionate investors. In the past few years, there has been a massive adoption and growth in the use of data science, artificial intelligence and machine learning to find alpha. However, information on and application of machine learning to investment are scarce. This course has been designed to address that. It is meant to spark your creative juices and get you started in this space.


Deep Learning Prerequisites: Logistic Regression in Python

@machinelearnbot

This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic 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 logistic regression module in Python. This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for free.


21 Best Online Courses On Data Science

#artificialintelligence

Description: Learn how to Visualize Data with MATLAB in 2D, 3D, 4D, 5D and create animated plots with tens of projects and examples!


Linear regression in R for Data Scientists - Udemy

#artificialintelligence

When buying any of my courses, I also give you free coupons to the rest of my courses. Just send me a message after enrolling. Pay one course, get 5!! Linear regression is the primary workhorse in statistics and data science. Its high degree of flexibility allows it to model very different problems. We will review the theory, and we will concentrate on the R applications using real world data (R is a free statistical software used heavily in the industry and academia).