This course not only covers machine learning techniques, it also covers in depth the rationale of investing strategy development. This course is the first of the Machine Learning for Finance and Algorithmic Trading & Investing Series. If you are looking for a course on applying machine learning to investing, the Machine Learning for Finance and Algorithmic Trading & Investing Series is for you. With over 30 machine learning techniques test cases, which included popular techniques such as Lasso regression, Ridge regression, SVM, XGBoost, random forest, Hidden Markov Model, common clustering techniques and many more, to get you started with applying Machine Learning to investing quickly.

This video, with the help of practical projects, highlights how TensorFlow can be used in different scenarios--this includes projects for training models, machine learning, deep learning, and working with various neural networks. Each project provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with tensors. Simply pick a project in line with your environment and get stacks of information on how to implement TensorFlow in production. Rodolfo Bonnin is a Systems Engineer and PhD student at Universidad Tecnológica Nacional, Argentina. He also pursued parallel programming and image understanding postgraduate courses at Uni Stuttgart, Germany.

If you are looking to build data science models that are good for production, Java has come to the rescue. With the aid of strong libraries such as MLlib, Weka, DL4j, and more, you can efficiently perform all the data science tasks you need to. This course will help you to learn how you can retrieve data from data sources with different level of complexities. You will learn how you could handle big data to extract meaningful insights from data. Later we will dive to visualizing data to uncover trends and hidden relationships.

Anyone who has spent time around children understands that they learn at different rates. Some students are also auditory or visual learners. Personalized AI tutors within the classroom could offer an alternative method for teaching students the fundamentals.

It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or make business forecasting related decisions. Learning regression machine learning is indispensable for data mining applications in areas such as consumer analytics, finance, banking, health care, science, e-commerce and social media. It is also essential for academic careers in data mining, applied statistical learning or artificial intelligence. And it is necessary for any business forecasting related decision. But as learning curve can become steep as complexity grows, this course helps by leading you through step by step real world practical examples for greater effectiveness.

Machine learning algorithms seem to be used carelessly on a trial and error basis without proper understanding about how they work. This guide will introduce you to the fundamentals of the classification machine learning problem using R and how you can apply this into data sets that will generate value for your business. What is a classification machine learning problem in a nutshell? Goal: To predict if an object belongs to one category or the other. Example: To predict if a user stays or leaves a firm.

Studying how to learn and how to love learning

What is machine learning / ai? How to lean machine learning in practice? "I give you 2 options. Take the red pill and you will experience wonderland, take the blue pill and you will wake up tomorrow morning in your bed as if nothing has happend" If you decide to take the red pill then... Machine learning is the new steam engine and will shift the world of tomorrow. If you want to be part of this and get your hands dirty than come and join me to explore practical examples of machine learning and deep neural networks in python.

Are you looking at improving and extending the capabilities of your machine learning systems? If yes, then this course is for you. ML is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields, such as search engines, robotics, self-driving cars, and more. It is transforming the way businesses operate.

There are many problems associated with analyzing data sets that contain missing data. However, there are various techniques to'fill in,' or impute, missing data values with reasonable estimates based on the characteristics of the data itself and on the patterns of'missingness.' Generally, techniques appropriate for imputing missing values in multivariate normal data and not as useful when applied to non-multivariate-normal data. This Visualization and Imputation of Missing Data course focuses on understanding patterns of'missingness' in a data sample, especially non-multivariate-normal data sets, and teaches one to use various appropriate imputation techniques to "fill in" the missing data. Using the VIM and VIMGUI packages in R, the course also teaches how to create dozens of different and unique visualizations to better understand existing patterns of both the missing and imputed data in your samples.