“We are bringing technology to philosophers and poets.”

Machine Learning is usually considered to be the forte of professionals belonging to the programming and technology domain. People from arts and social science with no background in programming/technology often find it challenging to learn Machine Learning. However, Machine learning is not for technologists and programmers only. It is for everyone who wants to be a better researcher and decision-maker.

Machine Learning is for anyone looking to model how humans and machines make decisions, develop mathematical models of decisions, improve decision-making accuracy based on data, and do science with data.

Machine Learning brings you closer to the fascinating world of artificial intelligence. Machine Learning is a cross-disciplinary field encompassing computer science, mathematics, statistics, psychology, and management. It’s currently tough for normal learners to understand so many subjects, making Machine Learning inaccessible to many, especially those from social science backgrounds.

We built this course, “Machine Learning for Social Scientists,” to help learners master this topic without getting stuck in its technicalities or fear of coding. This course is built as a scratch to the advanced level course for Machine Learning. All the topics are explained with the basics. The instructor creates a connection with everyday instances and fundamental tools so that learners feel connected to their previous learning. For example, we demo some Excel calculations to ensure learners can see the connection between Excel spreadsheet analysis and Machine Learning using R language.

The course covers the following topics:

· Fundamentals of Machine Learning

· Applications of Machine Learning

· Statistical concepts underlying Machine Learning

· Supervised Machine Learning Algorithms

· Unsupervised Machine Learning Algorithms

· How to Use R to Implement Machine Learning Algorithms

· How to create Training and Testing datasets and train Machine Learning Models

· How to improve the accuracy of Machine Learning Models

· Linear Regression Algorithm

· Calculation of Parameters of Linear Regression Model manually, using Excel and R

· K Nearest Neighbor (KNN) Analysis

· Understanding Mathematics behind K Nearest Neighbor Analysis

· Estimating sensitivity and specificity of the model

· Implementing KNN Algorithm in R

· Many more

According to various estimates, Machine Learning is among the highest-paid job in the industry, and salaries of Machine Learning professionals could usually be above US$1,00,000 per annum. If you are looking forward to a course that can get you gently started with Machine Learning, this course is for you. To join the course, click on the Sign Up button and start your journey in Machine Learning from today.