What you'll learn
Be Able To Harness The Power Of R For Practical Data Science
Master The Theory Of Artificial Neural Networks (ANN) and Deep Neural Networks (DNN)
Implement ANN For Classification & Regression Problems In R
Learn The Implementation Of Both ANN & DNN Using The H2o Package Of R Programming Language
Learn The Implementation Of Both ANN & DNN Using The MxNet Package Of R Programming Language
Introduction to Convolutional Neural Networks (CNN) For Imagery Classification
Implement CNNs Using Keras
Requirements
Be Able To Operate & Install Software On A Computer
Prior Exposure To Common Machine Learning Terms Such As Unsupervised & Supervised Learning
Prior Exposure To What Neural Networks Are & What They Can Be Used For
Description
YOUR COMPLETE GUIDE TO ARTIFICIAL NEURAL NETWORKS & DEEP LEARNING IN R: This course covers the main aspects of neural networks and deep learning. If you take this course, you can do away with taking other courses or buying books on R based data science. In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in neural networks and deep learning in R, you can give your company a competitive edge and boost your career to the next level!LEARN FROM AN EXPERT DATA SCIENTIST:My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University. I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals.Over the course of my research I realized almost all the R data science courses and books out there do not account for the multidimensional nature of the topic . This course will give you a robust grounding in the main aspects of practical neural networks and deep learning. Unlike other R instructors, I dig deep into the data science features of R and give you a one-of-a-kind grounding in data science... You will go all the way from carrying out data reading & cleaning to to finally implementing powerful neural networks and deep learning algorithms and evaluating their performance using R.Among other things:You will be introduced to powerful R-based deep learning packages such as h2o and MXNET. You will be introduced to deep neural networks (DNN), convolution neural networks (CNN) and unsupervised methods. You will learn how to implement convolutional neural networks (CNN)s on imagery data using the Keras frameworkYou will learn to apply these frameworks to real life data including credit card fraud data, tumor data, images among others for classification and regression applications. With this course, you'll have the keys to the entire R Neural Networks and Deep Learning Kingdom!NO PRIOR R OR STATISTICS/MACHINE LEARNING KNOWLEDGE IS REQUIRED:You'll start by absorbing the most valuable R Data Science basics and techniques. I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in R. My course will help you implement the methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R based data science in real-life.After taking this course, you'll easily use data science packages like caret, h2o, mxnet, keras to implement novel deep learning techniques in R. You will get your hands dirty with real life data, including real-life imagery data which you will learn to pre-process and model You'll even understand the underlying concepts to understand what algorithms and methods are best suited for your data. We will also work with real data and you will have access to all the code and data used in the course. JOIN MY COURSE NOW!
Overview
Section 1: INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
Lecture 1 Introduction to the Course
Lecture 2 Data and Code
Lecture 3 Install R and RStudio
Lecture 4 Install MXnet in R and RStudio
Lecture 5 Install Mxnet in R- Written Instructions
Lecture 6 Install H2o
Lecture 7 What is Keras?
Lecture 8 Install Keras in R
Lecture 9 What Are the Most Common Data Types We Will Encounter?
Section 2: Basic Data Access & Pre-Processing in R
Lecture 10 Read in Data From CSV and Excel Files
Lecture 11 Read in Data from Online HTML Tables-Part 1
Lecture 12 Read in Data from Online HTML Tables-Part 2
Lecture 13 Working with External Data in H2o
Lecture 14 Remove NAs
Lecture 15 More Data Cleaning
Lecture 16 Introduction to dplyr for Data Summarizing-Part 1
Lecture 17 Introduction to dplyr for Data Summarizing-Part 2
Lecture 18 Exploratory Data Analysis(EDA): Basic Visualizations with R
Section 3: Some Theoretical Foundations
Lecture 19 Difference Between Supervised & Unsupervised Learning
Lecture 20 Theory Behind ANN (Artificial Neural Network) and DNN (Deep Neural Networks)
Lecture 21 What Are Activation Functions?
Section 4: Build Artificial Neural Networks (ANN) in R
Lecture 22 Neural Network for Binary Classifications
Lecture 23 Evaluate Accuracy
Lecture 24 Implement a Multi-Layer Perceptron (MLP) For Supervised Classification
Lecture 25 Neural Network for Multiclass Classifications
Lecture 26 Neural Network for Image Type Data
Lecture 27 Multi-class Classification Using Neural Networks with caret
Lecture 28 Implement an ANN with H2o For Multi-Class Supervised Classification
Lecture 29 Implement an ANN Based Classification Using MXNet
Lecture 30 Implement MLP With Keras
Lecture 31 Keras MLP On Real Data
Lecture 32 Keras MLP For Regression
Lecture 33 Neural Network for Regression
Lecture 34 More on Artificial Neural Networks(ANN) - with neuralnet
Lecture 35 Implement an ANN Based Regression Using MXNet
Lecture 36 Identify Variable Importance in Neural Networks
Section 5: Build Deep Neural Networks (DNN) in R
Lecture 37 Implement a Simple DNN With "neuralnet" for Binary Classifications
Lecture 38 Implement a Simple DNN With "deepnet" for Regression
Lecture 39 Implement a DNN with H2o For Multi-Class Supervised Classification
Lecture 40 Implement a (Less Intensive) DNN with H2o For Supervised Classification
Lecture 41 Implement a DNN With Keras
Lecture 42 Identify Variable Importance
Lecture 43 Implement MXNET via "caret"
Lecture 44 Implement a DNN with H2o For Regression
Lecture 45 Implement a DNN with Keras For Regression
Lecture 46 Implement DNN Regression With Keras (Real Data)
Section 6: Unsupervised Classification with Deep Learning
Lecture 47 Theory Behind Unsupervised Classification
Lecture 48 Autoencoders for Unsupervised Learning
Lecture 49 Autoencoders for Credit Card Fraud Detection
Lecture 50 Use the Autoencoder Model for Anomaly Detection
Lecture 51 Autoencoders for Unsupervised Classification
Lecture 52 Autoencoders With Keras
Lecture 53 Keras Autoencoders on Real Data
Lecture 54 Stacked Autoencoder With Keras
Lecture 55 Keras For Outlier Detection
Lecture 56 Find the Outlier
Lecture 57 Outlier Detection For Cancer (With Keras)
Section 7: Convolutional Neural Networks (CNN)
Lecture 58 What is a CNN?
Lecture 59 Implement a CNN for Multi-Class Supervised Classification
Lecture 60 More About Our CNN Model Accuracy
Lecture 61 Set Up CNN With Keras
Lecture 62 More About CNN With Keras
Lecture 63 Implement Keras CNN On Real Images
Lecture 64 Some More Explanations
Lecture 65 Improve CNN Performance
Lecture 66 CNN For Multiclass Classification
Section 8: Working With Textual Data
Lecture 67 Basic Pre-Processing of Text Data
Lecture 68 Detect Frauds Using Keras Autoencoders on Text Data
Lecture 69 Word Embeddings For Classifying Fraud
Lecture 70 Word Embeddings For Classifying Fraud-GloVe
Section 9: Recurrent Neural Networks (RNN)
Lecture 71 Some theoretical foundations
Lecture 72 Use RNNs for Text Classification
Lecture 73 Use RNNs for Temporal Data
People Wanting To Master The R & R Studio Environment For Data Science,Anyone With Prior Exposure To Common Machine Learning Concepts Such As Supervised Learning,Students Wishing To Learn The Implementation Of Neural Networks On Real Data In R,Students Wishing To Learn The Implementation Of Basic Deep Learning Concepts In R

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