Secrets Of The MultiMillionaire Trainer by T. Harv Eker
Original price was: $999.00.$49.00Current price is: $49.00.
This Course is available for download now. You can contact us for Screenshots or Demo. Access for this course will be sent on google drive. Join our telegram channel to see updates and occasional discounts. If you want to pay through Paypal or Card contact us – On Telegram Click Here or contact on Mail – [email protected]
Description
Secrets Of The MultiMillionaire Trainer by T. Harv Eker
T. Harv Eker – Secrets Of The MultiMillionaire Trainer | 2.52 GB
T. Harv Eker Finally Shares His 9 Trade Secrets
To Become A Highly Paid Speaker And Trainer Teaching What You Know And Love
Linear Algebra is one of the areas where everyone agrees to be a starting point in the learning curve of Machine Learning, Data Science, and Deep Learning.. Its basic elements – Vectors and Matrices are where we store our data for input as well as output.
Any operation or Processing involving storing and processing the huge number of data in Machine Learning, Data Science, and Artificial intelligence, would mostly use Linear Algebra in the backend.
Even Deep Learning and Neural Networks –
Employs the Matrices to store the inputs like image, text etc. to give the state of the art solution to complex problems.
Keeping in mind the significance of Linear Algebra in a Data Science career, we have tailor-made this curriculum such that you will be able to build a strong intuition on the concepts in Linear Algebra without being lost inside the complex mathematics.
At the end of this course, you will also learn, how the Famous Google PageRank Algorithm works, using the concepts of Linear Algebra which we will be learning in this course.
In this course. you will not only learn analytically, but you will also see its working by running in Python as well.
So, with this course, you will learn, build intuition, and apply to some of the interesting real-world applications.
Click on the Enroll Button to start Learning.
I look forward to seeing you in Lecture 1