LEARNING PATH: R: Machine Learning and Deep Learning with R. With the help of this course you can Combine the power of machine learning and deep learning to create powerful data science applications.
This course was created by Packt Publishing. It was rated 4.1 out of 5 by approx 8676 ratings. There are approx 16830 users enrolled with this course, so don’t wait to download yours now. This course also includes 6.5 hours on-demand video, 1 Supplemental Resource, Full lifetime access, Access on mobile and TV & Certificate of Completion.
What Will You Learn?
Classify data with the help of statistical methods such as k-NN Classification, Logistic Regression, and Decision Trees
Learn the basics of deep learning and artificial neural network
Learn to deal with imbalanced datasets in artificial neural networks
Explore deep learning algorithms
Understand classification and probabilistic predictions with single-hidden-layer neural networks
Get to grips with convolutional deep belief networks
Learn practical applications of deep learning
Learn about feature engineering and multicore/cluster computing
Machine learning is a subfield of computer science that gives computers the ability to learn without being explicitly programmed. Deep Learning is the next big thing and a part of machine learning. Its favorable results in applications with huge and complex data is remarkable. R is one of the most popular programming languages among the data science professionals. So, if you’re a data science professional who wants to learn machine learning and deep learning with R, then go for this Learning Path.
Packt’s Video Learning Path is 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.
The highlights of this Learning Path are:
Classify data with the help of statistical methods such as k-NN Classification, logistic regression, and decision trees
Learn to develop machine learning applications and distributed jobs with SparkR
Dive deeper into deep learning and artificial neural networks
Let’s take a quick look at your learning journey. This Learning Path starts off by explaining different learning methods such as clustering, classification, model evaluation, and performance metrics. You will then dive into the general structure of the clustering algorithms and develop applications in the R environment by using clustering and classification algorithms for real-life problems.
Next, you will explore elements of deep learning neural networks, types of deep learning networks, and frameworks used for deep learning applications with building an application in TensorFlow package. You will learn to develop machine learning applications and distributed jobs with SparkR.
Moving ahead, this Learning Path teaches you how to leverage deep learning to make sense of your raw data by exploring various hidden layers of data. You will understand the basics of deep learning and artificial neural networks. You will then explore advanced ANN’s and RNN’s. Next, you will deep dive into convolutional neural networks and unsupervised learning. Finally, you will learn about the applications of deep learning in various fields and understand the practical implementations of scalability, HPC, and feature engineering.
By the end of this Learning Path, you will be able to build powerful machine learning and deep learning applications with the help of R.
Meet Your Experts:
We have the best works of the following esteemed authors to ensure that your learning journey is smooth:
Olgun Aydin is a PhD candidate at Department of Statistics, Mimar Sinan University. He has been working on Deep Learning for his PhD thesis. He is also working as a Data Scientist.He is familiar with Big Data technologies such as Hadoop, Spark and is able to use Hive, Impala. He is a big fan of R. He loves to work with Shiny and SparkR.He has many academic papers and proceedings about applications of statistics on different disciplines.
Vincenzo Lomonaco is a Deep Learning PhD student at the University of Bologna and founder of ContinuousAI .com an open source project aiming to connect people and reorganize resources in the context of Continuous Learning and AI. He is also the PhD students’ representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses “Machine Learning” and “Computer Architectures” in the same department. Previously, he was a Machine Learning software engineer at IDL in-line devices and a master student at the University of Bologna where he graduated cum laude in 2015 with the dissertation “Deep Learning for Computer Vision: A comparison between CNNs and HTMs on object recognition tasks”.