What you will learn in this course?
About this courseFor a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics  stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science. In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them. The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting. The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress highdimensional data. This course is of intermediate difficulty and will require basic Python and numpy knowledge. At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning. Click the link or image below to access the course contents:

You are the best judge of what needs to be done, but here are some considerations:

2) Modeling

3) Evaluation
Thumbrules:
References  Google Scholar  https://scholar.google.com/ 
Load the Car dataset
Simple Scatter Plot
Add Some color and change the shape of points
Add a fitline to the scatter plot
3D Scatter Plots
3D Scatter plots with colors and vertical drop lines
Using ggplot2
Add regression lines
Using googleVis


Get the temperature data from airquality dataset
Simple Histogram with colors
Using ggplot2
Using googleVis
