R: Artificial Neural Nets in R – Beginner to Expert!: 3-in-1 | Data Science Online Course by Udemy

Development Online Course by Udemy, On Sale Here

Implement solutions from scratch, covering real-world case studies to illustrate the power of neural network models.

An excellent training about Data Science

R: Artificial Neural Nets in R – Beginner to Expert!: 3-in-1

Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve a wide range of problems in different areas of AI and machine learning. The advantage of neural network is that it is adaptive in nature. It learns from the information provided, i.e. trains itself from the data, which has a known outcome and optimizes its weights for a better prediction in situations with unknown outcome. R provides this machine learning environment under a strong programming platform, which not only provides the supporting computation paradigm but also offers enormous flexibility on related data processing. The open source version of R and the supporting neural network packages are very easy to install and also simple to learn. Machine learning is widely used in many areas, ranging from the diagnosis of diseases to weather forecasting. You can also experiment with any novel example, which you feel can be interesting to solve using a neural network. This comprehensive 3-in-1 course is a step-by-step guide to understanding Neural Networks with R; throughout the course, practical, real-world examples help you get acquainted with the various concepts of Neural Networks. Develop a strong background in neural networks with R, to implement them in your applications. Learn how to build and train neural network models to solve complex problems. Implement solutions from scratch, covering real-world case studies to illustrate the power of neural network models. Contents and OverviewThis training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible. The first course, Getting Started with Neural Nets in R, covers building and training neural network models to solve complex problems. This course explains the niche aspects of neural networking and provides you with a foundation from which to get started with advanced topics by implementing them in R. This course covers an introduction to neural nets, the R language, and building neural nets from scratch- with R packages; specific worked models are applied to practical problems such as image recognition, pattern recognition, and recommender systems. At the end of the course, you will learn to implement neural network models in your applications with the help of practical examples from companies using neural nets. The second course, Create Your Own Sophisticated Model with Neural Networks, covers one-stop solution to learning complex models with Neural Networks and understanding the basics of Natural Language Processing. With this course you will learn the Decision Tree algorithms and Ensemble Models to build Random Forest, Regression Analysis. Focus on Decision Trees and Ensemble Algorithms. Use scikit-learn to classify text and Multiclass with scikit-learn. Explore various algorithms for classification. Look at Naive Bayes model and Label Propagation. Finally, you’ll use Neural Networks using different Classifiers and create your own Simple Estimator. The third course, Deep Learning Architecture for Building Artificial Neural Networks, covers an introduction to deep learning and its architectures with real-world use cases. The course starts off with an introduction to Deep Learning and the different tools, hardware, and software before we begin to understand the different training models. We then get to what everyone is talking about: Neural Networks. Understand how Neural Networks work and the benefits they offer for supervised and well as unsupervised learning before building our very own neural network. Explore the different Deep Learning Architectures, including how to set up your architecture and align the output. Finally we take a look at Artificial Neural Networks and understand how to build your own ANN. Taking this course will help you dive head first into the popular field of deep learning as a career choice or for further learning. By the end of the course, youll develop a strong background in neural networks with R, to build and train neural network models and solve complex problems. About the Authors ArunKrishnaswamyhas over 18 years of experience with large datasets, statistical methods, machine learning and software systems. He is one of the First Hadoop Engineers in the world, Advisor to AI Startups. He has 15+ years experience using R. He is also a Ph.D. in Statistics/Math with MS in CS. Expertise in Machine Learning, Neural Nets, and Deep Learning. Deep Experience in AWS, Spark, Cassandra, MongoDB, SQL, NoSQL, Tableau, R, Visualization. Data Science Mentor at UC Berkeley, Stanford, Caltech. Guest Lecturer at Community Colleges. Data Science in different domains o Fintech (Lending Club), o Cybersecurity (VISA) o Advertising Technology (Yahoo / Microsoft) o Bot Technology (voicy. ai) o Retail (WRS) o IOT (GE) o ERP (SAP) o Health Care (Blue Cross). Julian Avila is a programmer and data scientist in finance and computer vision. He graduated from the Massachusetts Institute of Technology (MIT) in m

Udemy is the leading global marketplace for learning and instruction

By connecting students all over the world to the best instructors, Udemy is helping individuals reach their goals and pursue their dreams.

Study anytime, anywhere.

START HERE