Understanding Machine Learning
MP4 | Video: h264, 1280x720 | Audio: AAC, 44100 Hz
Language: English | Size: 669 MB | Duration: 1h 35m
This course provides introduction to how Machine Learning Models are created.
What you'll learn
What are models in Machine Learning?
How to build models for Machine Learning?
How does Machine Learning build a Linear Regression model?
Requirements
Some knowledge of programming in any language is essential.
Description
Machine Learning is becoming ubiquitous across all industries. Already many applications have been identified which use Machine Learning now. Few examples include Spam Detection, Face Recognition, Emotion Analysis, Object Detection, Credit Card Fraud Detection, Weather Prediction, and the list is almost endless. More new applications are being identified by different industries almost everyday.
It is not just about applying superior technology for traditional problems when we apply Machine Learning. It is also about business sense since applying Machine Learning, we can make experiments and applications much more economical.
This course is a result of a discussion among my Project Team from our cohort in IIT, Kanpur learning Cyber Security. We have embarked to create a product for Malware Detection using Machine Learning. While all of us are getting grips on Malware Analysis, the team needed some inputs of Machine Learning. To fill the gap, I conducted some sessions with our Project Team members on Machine Learning. This course is a collection of the recording of these sessions.
This course discusses what are Machine Learning Algorithms. We discuss Random Forest Algorithm and Linear Regression as examples to understand what are models in Machine Learning. We see how to implement such models using Python. During the discussion on the development of the Machine Learning models, we discuss the various steps like Data Preprocessing, Normalisation, Scaling, etc. We touch upon the basics of Neural Network and take a slight deep dive into Regression. The course includes discussion on concepts like what is overfitting, what is hyper-parameter tuning, etc.
This course tries to give an idea for what it takes to create a product which uses Machine Learning. I believe that the discussions can get one started to apply Machine Learning to many problems.
Who this course is for:
Students
Professionals
Engineers
Researchers
