Hands on Machine learning & Data science with R- Over 10 projects

Introduction of Machine learning & Data science course.

How to get help during the course.

In this video and next few videos, we will look at career options in data science and machine learning.

This video will teach you how to make correct decision in your career to switch to data science and choose correct profile for you.

In this video, we will explore various roles and job profiles in the field of machine learning & data science. It will help you in charting your course.

In this video, we will look at the differences between machine learning & artificial intelligence. We will also cover classification of machine learning in this video.

You will learn about the first strategy which you can use to directly target your favorite employers along with few tips on how to build your CV.

In this video, we will target job sites and you will learn profile optimization to rank better on these job portals so that you can get more high-quality job options.

In this video, you will install rstudio, setup your environment and download all code files from the resource section.

You need to learn fundamentals of vectors, matrix and data frames before learning anything in data science or machine learning.

Learn various types of data types in R.

You will learn about various types of variables and objects in R. Learn how to create and use them.

Learn comments in R and create vectors with R.

As a data scientist, data wrangling will take most of your time. We will start learning that in this video.

Learn more techniques about data wrangling in R.

Learn operators in R programming.

More operators in R.

Loops are very confusing but important, in this video you will learn about Loops in R programming. 

Conditional blocks are integral to any programming language and R language is not an exception. Learn about ifelse blocks in R.

Learn about functions in R programming.

Test your knowledge of R programming language.

Before you start with machine learning and data science, you need to learn how to load data in rstudio and read various file types. You will do that in this video.

Learn how to do data selection and manipulation in R programming. This is one of the most important activity for a data scientist.

You will learn various techniques to select rows and columns in any data set with r. Data sub-setting is one of the most common and time consuming activity in machine learning.

Learn to apply various techniques like select and filter in your machine learning & data science project using Dplyr package in R. Dyplyr is one of the most used package in the machine learning industry.

We will continue with data selection & manipulation for machine learning, in this video we will cover arranging and mutating your data with Dplyr.

Creating subset of the data sets and merging them is quite common, in this video you will learn them. This is used widely in data science.

Handling missing values is critical for today's data scientists, you will learn to do that in this video.

Test your knowledge

Data visualization is vital to data analysis and data science. In this video, we will start with that.

There is lots of confusion around histograms and bar plots. You will learn the differences between them and where to use them.

Create bar plots and histograms with R programming language.

You will learn to create horizontal bar plots in video and start with Plot function.

Explore plot function with learning heat maps.

Learn boxplots, pair and par commands in R to create better data visualization for your machine learning projects.

Line graphs are common and maps are on demand. Learn them to create with R programming langauge.

GGPlot2 is one of most popular data visualization library among machine learning engineers and data scientists. 

Learn more visualization with ggplot2.

You will learn about lattice and scatter3d plots library.

Fundamentals of applied statistics which are must for machine learning and data science.

Learn what is descriptive analysis and inferential analysis with their differences.

Learn mean, median, mode and range.

Learn variance and standard deviation.

Learn standard error, skewness and kurtosis.

Learn t-test and f ratio.

Learn hypothesis testing and how to use it in data science and machine learning.

Learn the fundamentals of machine learning and types of machine learning models.

Learn fundamentals of regression machine learning models.

Learn fundamentals of classification problems and how to use them.

Learn Fundamentals of dimension reduction like PCA and data reduction models like clustering models.

Learn fundamentals of Analysis of Variance (ANOVA) and how to use it in the world of machine learning.

Implement One way and Two way anova in R.

Learn fundamentals of Linear regression. 

You will implement simple and multiple linear regression models in this video with R.

Practice linear regression with this project.

Learn fundamentals of Logistic regression.  

In this part 1 of logistic regression implementation with R, we will start by loading the data in session and cover initial data wrangling.

In this part 2 of logistic regression implementation with R, we will continue with data wrangling and start data visualization.

In this part 3 of logistic regression implementation with R, we will complete training logistic regression model and use it for prediction.

Practice with logistic regression project.

Learn basics of dimension reduction, PCA and how to apply them.

Implementation of principal component analysis with princomp in R.

Practice principal component analysis with this project.

Understand data reduction techniques with fundamentals of K-Means

Implementation of k-means in R.

Practice with K-Means Project

Fundamentals of tree based models like CART technique.

Implementation of classification and regression tree model in R.

Learn fundamentals of ensemble techniques like bagging & boosting. You will also learn about random forest machine learning model.

Implementation of random forest model with R.

Learn fundamentals of K- nearest neighbors.

Implementation of KNN in R

Naive Bayes fundamentals and implementation in r

Detailed lecture on automated neural networks, their applications and their implementation in R.

Implementation of Neural networks with R