Goal: To Advance Your Career | Salary: ₹10-12 lakh | Professional Certificate | Python, NumPy Knowledge Needed | Taught by Imperial College London Instructors | Duration: 1 Month |
A knowledge-oriented 3-course series that teaches you the mathematics needed for applications in data science and machine learning.
Note: This course should be taken after you have taken the foundation course in machine learning and before you take the advance courses, so you have the mathematical basics covered.
What You’ll Learn
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from Imperial College London
What You’ll Earn
- ₹10-12 lakh per year is the average starting salary for a Machine Learning professional in India (Source: careers360.com, glassdoor.co.in)
- $112,000+ per year is the average salary for a Machine Learning professional in the US (Source: ziprecruiter.com)
Details
This 3-course beginner-friendly series covers three broad mathematical topics that are essential to building a successful career in machine learning.
The first course is on Linear Algebra and looks at what linear algebra is and how it relates to data. You understand what vectors and matrices are and how to work with them.
The second course, Multivariate Calculus, builds on the first chapter and looks at how to optimise 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 is on Dimensionality Reduction with Principal Component Analysis. It uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge, which you should have if you have done the basic courses in machine learning.
Applied Learning Project
The assignments will make you use the skills you have learned to produce mini-projects with Python on interactive notebooks, an easy-to-learn tool which will help you apply the knowledge to real world problems.
For example, you will:
- Use linear algebra to calculate the page rank of a small simulated internet,
- Apply multivariate calculus in order to train your own neural network,
- Perform a non-linear least squares regression to fit a model to a data set, and
- Use principal component analysis to determine the features of the MNIST digits data set
