Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. Prerequisites: Working knowledge of Python is necessary if you want to run the source code that is provided. Basic knowledge of machine learning, especially ML classification techniques, would be helpful but it's not mandatory. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

Andrew Ng has led teams at Google and Baidu that have gone on to create self-learning computer programs used by hundreds of millions of people, including email spam filters and touch-screen keyboards that make typing easier by predicting what you might want to say next. As a way to get machines to learn without supervision, he has trained them to recognize cats in YouTube videos without being told what cats were. And he revolutionized this field, known as artificial intelligence, by adopting graphics chips meant for video games. To push the boundaries of artificial intelligence further, one of the world's most renowned researchers in the field says many more humans need to get involved. So his focus now is on teaching the next generation of AI specialists to teach the machines.

In this video course, we start by focusing on R's similarities with programming languages such as basic/C/C# with loops and conditional tests like if, so that you can feel at home and be productive straight away. We begin by introducing R and setting things up so that you are ready to go using Rstudio, the associated IDE. Then we look at R as a programming language and see how the standard things are done in it, so you can see that it's not that different from other programming languages. Next, we introduce some R commands, which are very useful and not as common in traditional languages since manipulating data is more important in R. Moving on, we look at an example in the Titanic dataset, which is the kind of thing you'll come across in R, a multidimensional collection of variables of different types. Using the tools that we cover we can form a picture, a story behind the data.

I am from Budapest, Hungary. I am qualified as a physicist and later on I decided to get a master degree in applied mathematics. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation.

Are you currently enrolled in either of my Core or Intermediate Spatial Data Analysis Courses? Or perhaps you have prior experience in GIS or tools like R and QGIS? You don't want to spend 100s and 1000s of dollars on buying commercial software for imagery analysis? The next step for you is to gain profIciency in satellite remote sensing data analysis. MY COURSE IS A HANDS ON TRAINING WITH REAL REMOTE SENSING DATA WITH OPEN SOURCE TOOLS!

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.

Studying how to learn and how to love learning

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.

A neural network is often mentioned but covers only a small part of machine learning. Especially beginners might get discouraged because of statistics and math which is an integral part of machine learning. By joining this course you get the chance to create and optimize your own machine learning algorythms. But if you want to actually practise python machine learning and create your own models in python, then this beginner's course is the right way to start!