Multi variable linear regression||Machine Learning Course||Part-7||Malayalam

Multi variable linear regression||Machine Learning Course||Part-7||Malayalam
In this Part-4 of "Machine Learning Course" in Malayalam, we discuss on how we can select hypothesis build cost function and how gradient descent algorithms work for multi variable linear regression

Overall scope of this course:
1. Introduction to Machine learning- Need for AI/ML, why to learn AI/ML, machine learning types, supervised, unsupervised and reinforced learning, application, difference between Human thinking Vs Machine thinking, difference between programming vs Machine learning.
2. Mathematics for Machine learning- Trigonometry, linear algebra, matrices, calculus & probability.
3.Python for Machine learning- variables, different libraries needed for data science such as numpy, pandas, matplotlib, etc
4. Deep-dive into machine learning- How ML algorithm works, the concept of cost function and gradient descent, practical examples for linear regression and Classifications, ML Agorithams and its usage.
5.Introduction to OpenCV- Image/video processing with OpenCV
6. Face recognition- Building a security alarm system using ML techniques

#machinelearningmalayalam#linearregressionmalayalam

[mohammedsidheeque]

Sir, at 4:14, while explaining h0(x), you say that matrix transpose matrix is ​​already explained in the Matrix class video. But it is missing when watching the full video.