Deep Learning: Neural Networks And Heuristics With R

Confidently build predictive Machine Learning and Deep Learning models using R to solve business problems

What you'll learn

Learn how to solve real life problem using the deep learning techniques

Learn Deep Learning models such as Regression, Heauristic etc

Understanding of basics of statistics and concepts of Deep Learning

How to do basic statistical operations and run ML models in R

Requirements

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

Description

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.You're looking for a complete Machine Learning and Deep Learning course that can help you launch a flourishing career in the field of Deep Learning, R, right? You've found the right Deep Learning course! After completing this course you will be able to:Confidently build predictive Machine Learning and Deep Learning models using R to solve business problems and create business strategyAnswer Machine Learning, Deep Learning, R, related interview questionsParticipate and perform in online Data Analytics and Data Science competitions such as Kaggle competitionsLearn how to solve real life problem using the deep learning techniquesdeep Learning models such as Regression, Heauristic etcUnderstanding of basics of statistics and concepts of Deep LearningHow to do basic statistical operations and run ML models in RIn-depth knowledge of data collection and data preprocessing for Machine Learning problemHow to convert business problem into a Machine learning problem

Overview

Section 1: Deep Learning: Neural Networks With R

Lecture 1 Reviewing Dataset

Lecture 2 Creating Dataframes

Lecture 3 Generating Output

Lecture 4 Running Neural Network Code

Lecture 5 Importing Dataset

Lecture 6 Neural Network Plots for Hidden Layer 1

Lecture 7 Syntax and Commands for MLP

Lecture 8 Running the Code

Lecture 9 Testing for Dataframes

Lecture 10 Predict Results

Lecture 11 Creating R Folder

Lecture 12 Generating Output Plot

Lecture 13 Testing and Predicting the Outputs

Section 2: Deep Learning: Heuristics Using R

Lecture 14 Course Contents

Lecture 15 Creating Dataframes

Lecture 16 Generating Descriptive

Lecture 17 Generating Descriptive Continued

Lecture 18 Setting Directory and Environment

Lecture 19 Assigning Variables

Lecture 20 Syntax and Command Part 1

Lecture 21 Syntax and Command Part 2

Lecture 22 Syntax and Command Part 3

Lecture 23 Setting Directory and Environment-Cryptocurrencies

Lecture 24 Spearman Techniques

Lecture 25 Generating Line Graphs

Lecture 26 Generating Scatter Plots

Lecture 27 Generating Multiple Scatter Plots

Lecture 28 Understanding Regression Modeling Theory

Lecture 29 Implementing Linear Regression Modeling

Lecture 30 Syntax and Commands

Lecture 31 Generating Scatter Plots-Energy Sector

Lecture 32 Multiple Scatter Plots

Lecture 33 Creating Dataframes-Financial Markets

Lecture 34 Understanding Multiple

Lecture 35 Implementing Multiple Regression Model in R

Lecture 36 Plot and Draw Line of Fit

Lecture 37 Multiple Scatter Plots in a Graphical Frame

People pursuing a career in data science,Working Professionals beginning their Data journey,Statisticians needing more practical experience