Are you interested to explore advanced algorithm concepts such as random forest vector machine, K- nearest, and more through real-world examples? Then this Learning Path is for you
Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
Machine learning and data science are some of the top buzzwords in the technical world today. Machine learning - the application and science of algorithms that makes sense of data, is the most exciting field of all the computer sciences! It explores the study and construction of algorithms that can learn from and make predictions on data. Also, R language is widely used among statisticians and data miners to develop statistical software and data analysis.
We are living in an age where data comes in abundance; using the self-learning algorithms from the field of machine learning, you can turn this data into knowledge as well as gain unimaginably powerful insights into data.
The highlights of this Learning Path are:
Knowing statistics helps you build strong machine learning models that are optimized for a given problem statement.
Let’s take a quick look at your learning journey...
You’ll understand the real-world examples that demonstrates the statistical side of machine learning and familiarize you with it.
In this Learning Path, you’ll work through various examples on advanced algorithms, and focus a bit more on some visualization options. You’ll start by learning how to use random forest to predict what type of insurance a patient has based on their treatment and you will get an overview of how to use random forest/decision tree and examine the model.
After that, you’ll explore the next example on soil classification from satellite data using K-nearest neighbor where you’ll predict what neighborhood a house is in - based on other data about it. You’ll also dive into the example of predicting a movie genre based on its title, where you’ll use the tm package and learn some techniques for working with text data. You’ll use libraries such as scikit-learn, e1071, randomForest, c50, xgboost, and so on. Finally, you’ll explore the application of frequently used algorithms on various domain problems, using both Python and R programming.
By the end of the Learning Path, you’ll have mastered the required statistics for machine learning algorithm and will be able to apply your new skills to any sort of industry problem.
Meet Your Expert:
We have the best work of the following esteemed authors to ensure that your learning journey is smooth: