Probability and Probability Distributions for Machine Learning

Probability is one of the essential skills one must possess to have a good hold on machine learning concepts, and it is helpful in prediction and decision-making processes. Other prerequisites to learning machine learning include: Algebra, Linear Algebra, Trigonometry, Statistics, Calculus(for advanced topics), Python Programming, Terminal or Cloud Console. 
 

Probability is not a challenging concept to learn, but it involves more than a few basic concepts to deal with while working with domains like machine learning. You will have to apply other concepts such as linear algebra, statistics, and calculus and also be able to work with python programming comfortably. You can start by learning Probability and Machine learning before diving into this Probability for Machine Learning course.

The probability for machine learning course is a 2.5 hours long course, but you can learn it at your pace since the course is self-paced. With all the prerequisites mastered, it will not take much time to understand the concepts in this course. If you are supposed to start with learning all the basics such as statistics, calculus, python programming, and other such topics, you will take anywhere from 3 to 6 months before you are good at probability for machine learning.

Probability for machine learning is a free course. You will be assigned with a few tasks after you complete the course to test your understanding. You can take this course in your leisure to learn and understand the subject since it is self-paced. You will be given with a certificate of completion after you successfully complete the course, and you can put it on your LinkedIn and other such platforms. 

Machine Learning Engineer, Data Scientist, NLP Scientist, Business Intelligence Developer, and Human-Centered Machine Learning Designers are a few of the many career opportunities if you have mastered probability for machine learning. However, it is not just sufficient to learn probability; but you must also basket other skills such as statistics, linear algebra, calculus, python programming, and other concepts to be a top professional in machine learning.