Learn Practical Machine Learning with this Coursera Program

Tapas Mohanty

30 December 2022

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Course Overview

In the Machine Learning course by Coursera, you will learn about the most effective practical machine learning techniques and gain practice in implementing them. More importantly, you will learn the theory and the practical know-how needed to apply these techniques to new problems.

Finally, you will learn about some of Silicon Valley's best practices and innovations in the field of machine learning and AI. This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition.

The practical courses will also draw from numerous case studies and applications so that you will also learn how to apply learning algorithms to build smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Andrew NG, the main instructor of this course, is the Founder of DeepLearning.AI, a General Partner at AI Fund, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University. As a pioneer both in machine learning and online education, Dr. NG has changed countless lives through his work in AI. He has authored or co-authored over 100 research papers in machine learning, robotics, and related fields.

"This course helped me connect with different clients and explain how our products can resolve their complex problems."

- Tapas Mohanty

Course Structure

It is a self-paced, well-curated intermediate-level practical course spread over 61 hours. It is taught online by experienced faculty members. This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. The modules of this course are detailed, comprehensive, and practical.

It is designed in a way that in each class the theory is integrated with practical examples. The assessment method involves completing assignments and undertaking projects under various modules throughout the degree. These can be artificial intelligence practical research-based or concept specific. Some of the crucial skills you develop upon undergoing this course are ML algorithms, Regression analysis, ML Techniques, Artificial Neural Networks, etc.

This course has 4 prime modules:

Module 1: Linear Algebra Review
Module 2: Octave or MATLAB Tutorial
Module 3: Regularization
Module 4: Neural Networks: Representation

Insider Tips

In order to get the best out of this course, I have included some important tips below that I think you might find useful.

Technical Exhibitionism from the Course

This course provides a broad introduction to practical machine learning with python, data mining, and statistical pattern recognition. The curriculum draws from numerous case studies and applications so that you will also learn how to apply learning algorithms to build smart robots (perception, control), understand text (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Assessment and Grading Criteria

The assessment is based on assignments and projects undertaken by the learners in various modules throughout the degree. These can be research-based or concept specific.

The evaluations are designed to ensure continuous student engagement with the program and to encourage practical learning. There are instructor-moderated discussions kept at stipulated intervals to validate the learning experience of the learners.

Final Take

I am currently working as a Product Manager in Infosys. This course helped me in connecting with different clients and explaining how our products can resolve their complex problems. Moreover, it helped me connect with different stakeholders, especially developers and architects to explain the client requirements in great detail.