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Deep Learning Foundation: Linear Regression and Statistics Udemy NED Is statistics the foundation on top of which machine learning is built? Is machine ... "Traditional" linear regression may be considered by some Machine Learning ... Highest Rated What you'll learn Linear regression statistics basics Assumptions of linear regression hypothesis testing sampling Program your own version of a linear regression model in Python Derive and solve a linear regression model, and apply it appropriately to data science problemsRequirements Jupyter notebook and simple python programmingDescription Hi Everyone welcome to new course which is created to sharpen your linear regression and statistical basics. In this course I have explained hypothesis testing, Unbiased estimators, Statistical test, Gradient descent. End of the course you will be able to code your own regression algorithm from scratch.Who this course is for: Python developers curious about data science data science and machine leaning engineers Hi Everyone welcome to new course which is created to sharpen your linear regression and statistical basics. In this course I have explained hypothesis testing, Unbiased estimators, Statistical test, Gradient descent.
Learn the basics of machine learning without using code Learn to teach a machine with a camera Use an AI platform to build AI Models and Train the datasets Know about IBM Watson & Wipro Holmes AI technologies Convert a web application/software to an app in less than a minute Digishock 1.0 course from Udemy is a must in order to understand the tools better. No other experience or technical knowledge is necessary. This mind-blowing course takes the huge leap from Digishock 1.0 and is for anyone who want to get introduced with Machine Learning and Deep Learning without learning code. This practical hands-on course involves hands-on exercises with numerous tricks and techniques of analytics, advanced predictive concepts to work on to ensure that all are familiarized with the discipline of machine-learning, deep-learning, big data, analytics etc. The USP of the course is that there is no kind of technical knowledge required whatsoever for students who will participate in this course.