Gain an insight into the advanced aspects of machine learning with R by taking this practical course in Advanced machine learning with R. This course aims to guide individuals with a basic knowledge on R programming language, statistics and data frames, on how to build machine systems in R by taking them through real-world examples. This accredited qualification might pose a great advantage for your career in data science and will provide you with skills that are sure to make you stand out from the others within the industry.
This course will start off by teaching you on how to tune hyper parameters based on the type of data to effectively solve a machine learning problem. You will be provided with an example that lets you explore sonar data, perform exploratory analysis on the dataset and improve the model through iterating. The other example will dive into the basics of neural networks by providing you an example on DNA classification. You will be guided on how to explore the DNA dataset, implement a neural network that classifies data and work with neural network algorithm directly.
The next example will pry into the keras package of R programming language to classify the DNA dataset from the previous example. You will also get to explore the CIFAR10 dataset and the convolutional Neural Network that is used in visual imagery. The final example of this course will discuss about the shiny package in R programming language that can be used to present data within a production system. By the end of this course, you will walk away with an advanced knowledge on machine learning with R to make significant progress within the industry.
Why study at Global Edulink?
Global Edulink offers the most convenient path to gain recognised skills and training that will give you the opportunity to put into practice your knowledge and expertise in an IT or corporate environment. You can study at your own pace at Global Edulink and you will be provided with all the necessary material, tutorials, qualified course instructor, narrated e-learning modules and free resources which include Free CV writing pack, free career support and course demo to make your learning experience more enriching and rewarding.
|Access Duration||The course will be directly delivered to you, and you have 12 months access to the online learning platform from the date you joined the course. The course is self-paced and you can complete it in stages, revisiting the lectures at any time.|
|Who is this course aimed at?||This course might interest individuals hoping to learn how R programming language can be used to build machine learning systems|
|Method Of Assessment||The course is assessed online with a final, multiple-choice test, which is marked automatically. You will know instantly whether you have passed the course.|
|Certification||Those who pass this test will get a certificate in Advanced Machine Learning with R.|
This certificate will supplement your qualifications and help you in working for a number of jobs in the data science industry. You can also use this certificate to expand your education in the area or to win the job promotion or salary increment put forth by your organisation. Mentioned below are some of the jobs this certificate will benefit you in, along with the average UK salary per annum according to https://www.payscale.com,
|section 1: sonar data – hyper-parameter tuning|
|1.1. The Course Overview||00:00:00|
|1.2. Explore Sonar Data Set||00:00:00|
|1.3. Tuning Grids||00:00:00|
|1.4. Iterating – Improving our Tuning||00:00:00|
|1.5. Final Results||00:00:00|
|section 2: neural network|
|2.1. Neural Networks Basics||00:00:00|
|2.2. Explore the DNA Set||00:00:00|
|2.3. Implement a Neural Network||00:00:00|
|2.4. Multi-layer Perceptron||00:00:00|
|2.5. One Hot Encoding and MLP||00:00:00|
|section 3: keras – deep learning on the gpu|
|3.1. Overview of the Keras||00:00:00|
|3.2. Installing Keras||00:00:00|
|3.3. Neural Network in Keras||00:00:00|
|3.4. CIFAR10 Data Set||00:00:00|
|3.5. Convolutional Neural Network||00:00:00|
|section 4: deploying your model|
|4.1. Saving Your Model in R||00:00:00|
|4.2. Saving Your Model for Another Language||00:00:00|
|4.3. Shiny Web Interfaces||00:00:00|
|4.4. Wrapping Your Model in Shiny||00:00:00|
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