Machine Learning and R Programming for Data Science, Use PyCharm, Java & Android Studio to make apps with artificial intelligence! +Advanced R topics with hands-on projects!
- 0.0 (0 ratings)
- Created by Mammoth Interactive, John Bura
- English
- English [Auto-generated]
- 17.5 hours on-demand video
- 18 articles
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
Preview This Course - GET COUPON CODE
What you'll learn
- Write, compile and run Python code in one convenient location with PyCharm.
- Build advanced functions & classification models.
- Build user interfaces for Android by coding in Java.
- Clean, scale, and convert data to fit the needs of different models for training and testing.
- Build functions to save time typing code and become an efficient data scientist
- Build advanced functions in R for training and testing.
- Handle errors and problem solve the smart way.
- And much more!
Description
Do you want to learn the technology changing the world around us? This course is for you!
Funded by a #1 Mammoth Interactive Kickstarter Project
Machine learning is bringing us self-driving cars, facial recognition and artificial intelligence. Though machine learning originated for computers, the next big wave is machine learning on MOBILE.
Enroll Now in this Complete Masterclass!
17.5 hours on-demand video
Offline access via the Udemy app
18 Articles
11 Downloadable Resources
Full lifetime access
Have you ever thought: why can’t my mobile device do more? Learn how to do just that in this Machine Learning and R Programming for Data Science!
Perform handwritten digit recognition with basic and advanced MNIST projects
Build a weather prediction project
Explore PyCharm and the Python language
Explore Android Studio and the Java language
You will discover applications of machine learning and where we use machine learning daily! You'll explore different machine learning mechanisms and commonly used algorithms. You'll also explore TensorFlow, a machine learning framework.
Build a simple linear regression model in PyCharm with TensorFlow
Load data locally and multiple techniques.
Install packages.
Handle data outliers.
Use multiple linear regression and shrinkage methods.
And much more!
Included in this course is material for beginners to get comfortable with the interfaces. Please note that we reuse this content in similar courses because it is introductory material. You can find some material in this course in the following related courses:
Hands-On Machine Learning: Learn TensorFlow, Python, & Java!
Make predictions with Python machine learning for apps
R Programming: Practical Data Science and Modeling!
Build UI and Android Classes. Use PyCharm with TensorFlow! Code in Python, Java and R.
Python 3: Flexible and comprehensive, Python is an easy-to-use language that we will use to write powerful machine learning programs and other scripts.
Java 8: Object-oriented and reliable, Java is one of the most widely-used languages and has been the choice language of Android applications for years.
Android Studio 3: Build Android apps by providing both graphical and programming interfaces for front and back end functionality.
TensorFlow 1.4: Allows us to build computational graphs & neural networks and perform intense tasks like training and optimizing models with ease.
Learn multiple linear regression, box plotting & shrinkage. And more!
Enroll now to learn a massive amount of content, starting with how to load data locally, and other techniques. You spend so much time getting your data ready in R in data science that you need a good understanding of it.
You will learn data cleaning techniques, how to handle outliers, scaling data and why we scale it, and other important examples. You will learn to load and handle data in R, and much more.
Advanced Functions and Abstraction
You will work through numerous examples involving decision trees, random forest, testing, SVMs with tuning, and LDA. You will learn to build advanced functions in R around training and testing. You will be a functions expert and understand abstraction.
Regression, Shrinkage, and Correlation
You will learn regression models including linear regression and quickly move into multiple linear regression, shrinkage methods like ridge and lasso. You will learn when to use and when not to use correlation with model inputs.
Enroll Now While On Sale!
Who is the target audience?
- Anyone who wants to build intelligent mobile apps!
- Anyone who wants to learn how machine learning works in a mobile environment.
- R programmers who want to learn advanced R topics.
- Data scientists who want to take their knowledge to the next level.
- Anyone who wants to learn concepts through practical examples.
Post a Comment for "Machine Learning and R Programming for Data Science"